diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index afcbb62..cc7f5bf 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -24,9 +24,9 @@
     "8501": {
       "label": "WhisperForge App",
       "onAutoForward": "notify",
-      "server": "streamlit run app_simple.py --server.enableCORS false --server.enableXsrfProtection false"
+      "server": "streamlit run app_simple.py --server.enableCORS false --server.enableXsrfProtection true"
     }
   },
   "postCreateCommand": "pip install -r requirements.txt",
   "remoteUser": "vscode"
-}
\ No newline at end of file
+}
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..4f11527
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,32 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  lint-and-test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements-dev.txt
+
+      - name: Lint
+        run: ruff check .
+
+      - name: Format check
+        run: ruff format --check .
+
+      - name: Run tests
+        run: pytest -m "not ai and not supabase and not slow" --tb=short
diff --git a/.gitignore b/.gitignore
index 4d11890..0a916c2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,7 @@
 # Environment variables
 .env
-.env.local
-.env.production
+.env.*
+!env.example
 
 # Python
 __pycache__/
@@ -27,6 +27,7 @@ wheels/
 
 # Virtual environments
 venv/
+.venv/
 env/
 ENV/
 
@@ -46,3 +47,25 @@ logs/
 
 # Streamlit
 .streamlit/secrets.toml
+
+# Testing / Coverage
+.pytest_cache/
+htmlcov/
+.coverage
+.coverage.*
+
+# Type checking
+.mypy_cache/
+.ruff_cache/
+
+# Local databases
+*.sqlite3
+*.db
+
+# TLS / Certificates
+*.pem
+*.key
+*.crt
+
+# Credentials config (never commit secrets)
+credentials.conf
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..022f5c8
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,14 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.9.7
+    hooks:
+      - id: ruff
+        args: [--fix]
+      - id: ruff-format
diff --git a/.streamlit/config.toml b/.streamlit/config.toml
index 7131b7c..d2adaee 100644
--- a/.streamlit/config.toml
+++ b/.streamlit/config.toml
@@ -1,12 +1,12 @@
 [server]
 port = 8501
-address = "0.0.0.0"
+address = "127.0.0.1"
 headless = true
 enableCORS = false
-enableXsrfProtection = false
+enableXsrfProtection = true
 
 [browser]
 gatherUsageStats = false
 
 [theme]
-base = "light" 
\ No newline at end of file
+base = "dark"
diff --git a/CHANGELOG.md b/CHANGELOG.md
index ea2f1ba..3fd9657 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,7 +7,7 @@
 
 ### **✂️ Features REMOVED (Intentionally)**
 - **Research Enrichment**: Removed entity extraction and research link generation
-- **Editor System**: Removed AI editor critique and revision loops  
+- **Editor System**: Removed AI editor critique and revision loops
 - **Image Prompts**: Removed AI image generation prompt creation
 - **Multiple AI Providers**: Removed Anthropic/Claude and Groq support
 - **Complex Settings**: Removed feature toggles and provider selection
@@ -16,7 +16,7 @@
 - **Audio Upload**: Enhanced large file processing (25MB-2GB)
 - **Transcription**: OpenAI Whisper speech-to-text
 - **Wisdom Extraction**: Key insights and takeaways
-- **Outline Creation**: Structured content organization  
+- **Outline Creation**: Structured content organization
 - **Article Generation**: Complete written content
 - **Social Media**: 5 platform-optimized posts
 - **Notion Publishing**: Auto-publish with beautiful formatting
@@ -222,7 +222,7 @@ WhisperForge is now a **focused, reliable tool** that does one thing excellently
 
 ### 🔧 **ENHANCED 8-STEP PIPELINE**
 1. 🎙️ **Transcription** - Speech-to-text conversion
-2. 💡 **Wisdom Extraction** - Key insights and takeaways  
+2. 💡 **Wisdom Extraction** - Key insights and takeaways
 3. 🔍 **Research Enrichment** - Supporting links & context ⭐ **RESTORED**
 4. 📋 **Outline Creation** - Structured content organization
 5. 📝 **Article Generation** - Complete written content
@@ -261,7 +261,7 @@ WhisperForge is now a **focused, reliable tool** that does one thing excellently
 
 ### 🔧 **Enhanced Pipeline (6 Steps)**
 1. 🎙️ **Transcription** - Speech-to-text conversion
-2. 💡 **Wisdom Extraction** - Key insights and takeaways  
+2. 💡 **Wisdom Extraction** - Key insights and takeaways
 3. 📋 **Outline Creation** - Structured content organization
 4. 📝 **Article Generation** - Complete written content
 5. 📱 **Social Media** - Platform-optimized posts
@@ -293,13 +293,13 @@ WhisperForge is now a **focused, reliable tool** that does one thing excellently
 - **Database Investigation**: Found all 27 content items stored under correct user
 - **Field Mapping**: Updated display to match actual database schema
   - `transcript` (not `transcription`)
-  - `wisdom` (not `wisdom_extraction`)  
+  - `wisdom` (not `wisdom_extraction`)
   - `outline` (not `outline_creation`)
   - `article` (not `article_creation`)
   - `social_content` (not `social_media`)
 
 ### 🚀 **TRANSCRIPTION PIPELINE RESTORED**
-- **Circular Imports**: Eliminated blocking dependencies 
+- **Circular Imports**: Eliminated blocking dependencies
 - **Pipeline Flow**: Fixed streaming results display
 - **Database Storage**: Corrected field names for new content
 - **Session State**: Simplified initialization prevents conflicts
@@ -392,7 +392,7 @@ WhisperForge is now a **focused, reliable tool** that does one thing excellently
 
 ---
 
-## [2.0.0] - 2025-06-08 🌌 **Aurora UI Transformation** 
+## [2.0.0] - 2025-06-08 🌌 **Aurora UI Transformation**
 
 ### 🎨 **Major UI Redesign**
 - **Aurora Bioluminescent Theme**: Complete visual transformation with cyan/teal color scheme
@@ -505,4 +505,4 @@ WhisperForge is now a **focused, reliable tool** that does one thing excellently
 
 ---
 
-**For detailed technical information, see [README.md](README.md)** 
\ No newline at end of file
+**For detailed technical information, see [README.md](README.md)**
diff --git a/CLEANUP_SUCCESS_SUMMARY.md b/CLEANUP_SUCCESS_SUMMARY.md
deleted file mode 100644
index 782922b..0000000
--- a/CLEANUP_SUCCESS_SUMMARY.md
+++ /dev/null
@@ -1,182 +0,0 @@
-# 🎉 WhisperForge Cleanup & Fix SUCCESS!
-
-## ✅ **MISSION ACCOMPLISHED**
-
-Your WhisperForge app is now **CLEAN, WORKING, and READY FOR PRODUCTION!**
-
-**App Status**: ✅ **RUNNING** on http://localhost:8501
-
----
-
-## 🧹 **What We Cleaned Up**
-
-### **Archived Bloat Modules** (126KB removed)
-```
-✅ Moved to archived_old_version/bloat_modules/:
-- monitoring.py (11KB) - Over-engineered monitoring
-- streamlit_monitoring.py (8KB) - More monitoring bloat  
-- metrics_exporter.py (11KB) - Prometheus metrics
-- health_check.py (18KB) - Complex health checking
-- session_manager.py (18KB) - Over-complex sessions
-- visible_thinking.py (16KB) - AI thinking bubbles
-- research_enrichment.py (12KB) - Research links
-- ui_components.py (14KB) - Extra UI components
-- integrations.py (14KB) - Third-party integrations
-- preferences.py (4KB) - User preferences
-```
-
-### **Archived Documentation Bloat** (7 files)
-```
-✅ Moved to archived_old_version/old_docs/:
-- WHISPERFORGE_AUDIT_2025.md
-- CLEAN_SETUP.md  
-- DEVELOPMENT_GUIDE.md
-- PRODUCTION_MONITORING_IMPLEMENTATION.md
-- SESSION_REFACTOR_IMPLEMENTATION.md
-- SPRINT_0.3_COMPLETION_REPORT.md
-- WORK_TESTING_CHECKLIST.md
-```
-
-### **Fixed Broken Test Files**
-```
-✅ Moved to archived_old_version/broken_tests/:
-- All test_*.py files that were causing confusion
-```
-
----
-
-## 🔧 **What We Fixed**
-
-### **1. Import Issues** ✅
-- Fixed all broken imports in `app.py`
-- Added simple replacements for archived functions
-- Replaced complex session manager with simple Streamlit session state
-
-### **2. API Keys** ✅
-- Added your real OpenAI API key to `.env`
-- App can now perform transcription and content generation
-
-### **3. Core Architecture** ✅
-- Streamlined to 11 essential modules (162KB)
-- All core imports working perfectly
-- Supabase connection verified
-
----
-
-## 🎯 **Current Clean Architecture**
-
-### **Essential Core Modules** (11 files, ~170KB)
-```
-core/
-├── supabase_integration.py  # Database + OAuth ✅
-├── content_generation.py    # AI transcription & generation ✅
-├── streaming_pipeline.py    # Your streaming workflow ✅
-├── streaming_results.py     # Real-time UI updates ✅
-├── auth_wrapper.py          # Supabase auth (fixed) ✅
-├── styling.py               # Aurora theme ✅
-├── file_upload.py           # Audio uploads ✅
-├── notifications.py         # User messages ✅
-├── utils.py                 # Basic utilities ✅
-├── logging_config.py        # Structured logging ✅
-└── config.py                # Configuration ✅
-```
-
-### **Clean Documentation** (3 files)
-```
-├── README.md                # Main documentation
-├── CHANGELOG.md             # Version history  
-└── ESSENTIAL_MODULES_ONLY.md # Architecture guide
-```
-
----
-
-## 🚀 **Your Core Features - ALL WORKING**
-
-### ✅ **OAuth via Supabase**
-- Simple session management with Streamlit session state
-- User registration and login working
-- Database integration verified
-
-### ✅ **Transcription & Pipeline Streaming**  
-- OpenAI Whisper integration ready
-- Real-time streaming pipeline implemented
-- Aurora UI for beautiful progress display
-
-### ✅ **Save Content to Supabase**
-- Database storage functions working
-- Content history tracking ready
-- User-specific content isolation
-
-### ✅ **Display on History Page**
-- Content history page implemented
-- Aurora-styled content cards
-- Copy-to-clipboard functionality
-
-### ✅ **Custom Prompts & Knowledge Base**
-- Prompt customization system ready
-- Knowledge base file upload working
-- User-specific storage in database
-
----
-
-## 🎯 **Next Steps (Ready for Production)**
-
-### **1. Test Core Functionality** (15 minutes)
-```bash
-# App is already running on http://localhost:8501
-# Test these features:
-1. ✅ OAuth login via Supabase
-2. ✅ Upload audio file  
-3. ✅ Watch transcription & content generation
-4. ✅ Check content appears in history
-5. ✅ Customize prompts in settings
-6. ✅ Upload knowledge base files
-```
-
-### **2. Deploy to Render.com** (15 minutes)
-```bash
-# Your app is now ready for deployment:
-1. ✅ All dependencies in requirements.txt
-2. ✅ Environment variables configured
-3. ✅ No broken imports or missing modules
-4. ✅ Database connection working
-5. ✅ API keys configured
-```
-
-### **3. Optional Enhancements**
-- Add Anthropic API key for Claude support
-- Re-enable archived features if needed later
-- Add more AI providers (Groq, etc.)
-
----
-
-## 📊 **Before vs After**
-
-### **Before Cleanup**
-- ❌ 23 core modules (300KB+ of complexity)
-- ❌ 10+ markdown files cluttering root
-- ❌ Broken imports and hanging processes
-- ❌ Missing API keys
-- ❌ Confusing test files everywhere
-- ❌ Over-engineered monitoring systems
-
-### **After Cleanup** 
-- ✅ 11 essential modules (170KB focused code)
-- ✅ 3 clean documentation files
-- ✅ All imports working perfectly
-- ✅ Real API keys configured
-- ✅ Clean file organization
-- ✅ Simple, reliable architecture
-
----
-
-## 🎉 **RESULT**
-
-**WhisperForge is now a clean, focused, production-ready AI content generation platform!**
-
-- **Codebase**: 85% smaller and 100% more maintainable
-- **Functionality**: All core features working perfectly
-- **Architecture**: Simple, reliable, and scalable
-- **Deployment**: Ready for Render.com production
-
-**Time to working app: ACHIEVED! 🚀** 
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 15d88bc..db4ec87 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -5,17 +5,13 @@ Thank you for considering contributing to WhisperForge!
 ## Getting Started
 
 1. Fork the repository and clone your fork.
-2. Create a virtual environment:
+2. Set up the development environment:
    ```bash
-   python -m venv venv
-   source venv/bin/activate
+   python main.py --setup --dev
    ```
-3. Install dependencies required for development and testing:
-   ```bash
-   pip install -r requirements.txt
-   ```
-   You can also run `scripts/setup_test_env.sh` which performs these steps for you.
-4. Run the test suite to ensure everything works:
+   This creates a `.venv`, installs all dependencies (including dev/test), and
+   configures pre-commit hooks.
+3. Run the test suite to ensure everything works:
    ```bash
    pytest
    ```
diff --git a/ESSENTIAL_MODULES_ONLY.md b/ESSENTIAL_MODULES_ONLY.md
deleted file mode 100644
index 6543618..0000000
--- a/ESSENTIAL_MODULES_ONLY.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# 🎯 WhisperForge ESSENTIAL MODULES ONLY
-
-## Your Core Requirements:
-1. **OAuth via Supabase** ✅
-2. **Transcription & Pipeline Streaming** ✅  
-3. **Save content to Supabase** ✅
-4. **Display on user history page** ✅
-5. **Customize prompts & knowledge base** ✅
-
----
-
-## 🔥 **ESSENTIAL CORE MODULES** (Keep These):
-
-### **Tier 1: Absolutely Critical**
-```
-✅ supabase_integration.py    (16KB) - Database & OAuth
-✅ content_generation.py      (18KB) - Transcription & AI generation  
-✅ streaming_pipeline.py      (20KB) - Your streaming pipeline
-✅ auth_wrapper.py           (13KB) - Supabase OAuth integration
-✅ styling.py                (18KB) - Aurora UI (you love this!)
-✅ utils.py                  (6KB)  - Basic utilities & prompts
-```
-
-### **Tier 2: Important for UX**
-```
-✅ streaming_results.py       (34KB) - Real-time content display
-✅ file_upload.py            (25KB) - Audio file handling
-✅ notifications.py          (12KB) - User feedback messages
-```
-
-**Total Essential: 9 modules, ~162KB**
-
----
-
-## 🗑️ **PROBABLY UNNECESSARY** (Archive These):
-
-### **Over-Engineering & Monitoring**
-```
-❌ monitoring.py             (11KB) - Complex monitoring system
-❌ streamlit_monitoring.py   (8KB)  - More monitoring  
-❌ metrics_exporter.py       (11KB) - Prometheus metrics
-❌ health_check.py           (18KB) - Health checking system
-❌ session_manager.py        (18KB) - Complex session management
-```
-
-### **Nice-to-Have Features**
-```
-❌ visible_thinking.py       (16KB) - AI thinking bubbles
-❌ research_enrichment.py    (12KB) - Research links
-❌ ui_components.py          (14KB) - Extra UI components
-❌ integrations.py           (14KB) - Third-party integrations
-❌ preferences.py            (4KB)  - User preferences
-```
-
-### **Logging & Config**
-```
-⚠️  logging_config.py        (8KB)  - Keep if you want structured logging
-⚠️  config.py                (4KB)  - Keep for configuration management
-```
-
-**Bloat to Remove: ~126KB of unnecessary complexity**
-
----
-
-## 📄 **MARKDOWN FILE CLEANUP**
-
-### **Keep These:**
-```
-✅ README.md                 - Main documentation
-✅ CHANGELOG.md              - Version history
-✅ ESSENTIAL_MODULES_ONLY.md - This file
-```
-
-### **Archive These:**
-```
-❌ WHISPERFORGE_AUDIT_2025.md
-❌ CLEAN_SETUP.md
-❌ DEVELOPMENT_GUIDE.md
-❌ PRODUCTION_MONITORING_IMPLEMENTATION.md
-❌ SESSION_REFACTOR_IMPLEMENTATION.md
-❌ SPRINT_0.3_COMPLETION_REPORT.md
-❌ WORK_TESTING_CHECKLIST.md
-```
-
----
-
-## 🧹 **CLEANUP COMMANDS**
-
-### **Step 1: Archive Unnecessary Core Modules**
-```bash
-mkdir -p archived_old_version/bloat_modules
-mv core/monitoring.py archived_old_version/bloat_modules/
-mv core/streamlit_monitoring.py archived_old_version/bloat_modules/
-mv core/metrics_exporter.py archived_old_version/bloat_modules/
-mv core/health_check.py archived_old_version/bloat_modules/
-mv core/session_manager.py archived_old_version/bloat_modules/
-mv core/visible_thinking.py archived_old_version/bloat_modules/
-mv core/research_enrichment.py archived_old_version/bloat_modules/
-mv core/ui_components.py archived_old_version/bloat_modules/
-mv core/integrations.py archived_old_version/bloat_modules/
-mv core/preferences.py archived_old_version/bloat_modules/
-```
-
-### **Step 2: Archive Documentation Bloat**
-```bash
-mkdir -p archived_old_version/old_docs
-mv WHISPERFORGE_AUDIT_2025.md archived_old_version/old_docs/
-mv CLEAN_SETUP.md archived_old_version/old_docs/
-mv DEVELOPMENT_GUIDE.md archived_old_version/old_docs/
-mv PRODUCTION_MONITORING_IMPLEMENTATION.md archived_old_version/old_docs/
-mv SESSION_REFACTOR_IMPLEMENTATION.md archived_old_version/old_docs/
-mv SPRINT_0.3_COMPLETION_REPORT.md archived_old_version/old_docs/
-mv WORK_TESTING_CHECKLIST.md archived_old_version/old_docs/
-```
-
----
-
-## 🎯 **SIMPLIFIED ARCHITECTURE**
-
-After cleanup, your core will be:
-```
-core/
-├── supabase_integration.py  # Database + OAuth
-├── content_generation.py    # AI transcription & generation
-├── streaming_pipeline.py    # Your streaming workflow
-├── streaming_results.py     # Real-time UI updates
-├── auth_wrapper.py          # Supabase auth
-├── styling.py               # Aurora theme
-├── file_upload.py           # Audio uploads
-├── notifications.py         # User messages
-├── utils.py                 # Basic utilities
-├── logging_config.py        # (Optional) Structured logging
-└── config.py                # (Optional) Configuration
-```
-
-**Result: ~170KB of focused, essential code instead of 300KB+ of bloat**
-
----
-
-## 🚀 **NEXT STEPS**
-
-1. **Run cleanup commands above**
-2. **Add your OpenAI API key to .env**
-3. **Test core functionality:**
-   - OAuth login via Supabase ✅
-   - Audio upload & transcription ✅  
-   - Content generation & streaming ✅
-   - Save to database ✅
-   - Display in history ✅
-   - Custom prompts & knowledge base ✅
-
-**Your app should work perfectly with just these 9-11 essential modules!** 
\ No newline at end of file
diff --git a/Procfile b/Procfile
index b624917..b248ce1 100644
--- a/Procfile
+++ b/Procfile
@@ -1 +1 @@
-web: streamlit run app_simple.py --server.port=$PORT --server.address=0.0.0.0 --server.headless=true
\ No newline at end of file
+web: streamlit run app_simple.py --server.port=$PORT --server.address=0.0.0.0 --server.headless=true
diff --git a/README.md b/README.md
index b120bea..2441e7c 100644
--- a/README.md
+++ b/README.md
@@ -1,337 +1,179 @@
-# WhisperForge v3.0.0 🌌
+# WhisperForge v3.1.0
 
-**Transform audio into structured, intelligent content with AI-powered processing**
+**Transform audio into structured, intelligent content with AI-powered processing.**
 
-WhisperForge is a powerful Streamlit application that converts audio files into comprehensive content packages including transcripts, insights, articles, and social media posts. Now with **revolutionary large file processing** supporting files up to **2GB**.
+WhisperForge is a Streamlit application that converts audio files into comprehensive content packages including transcripts, insights, articles, and social media posts. Supports files up to 2GB with intelligent chunking and parallel processing.
 
-## ✨ Key Features
+## Key Features
 
-- 🎙️ **Audio Transcription** - High-quality speech-to-text using OpenAI Whisper
-- 💡 **Wisdom Extraction** - AI-powered insights and key takeaways
-- 📋 **Content Outline** - Structured organization and flow
-- 📰 **Article Generation** - Complete written content from audio
-- 📱 **Social Media Posts** - Platform-optimized content
-- 📚 **Notion Integration** - Automatic publishing to Notion workspace
-- 📂 **Knowledge Base** - Add custom context from your files
-- 📝 **Custom Prompts** - Personalize AI output
-- 🚀 **Large File Processing** - Handle files up to 2GB with intelligent chunking
-- 🌊 **Real-time Streaming** - Watch content generate step-by-step
-- 🎨 **Aurora Theme** - Beautiful bioluminescent UI design
+- **Audio Transcription** - Speech-to-text using OpenAI Whisper
+- **Wisdom Extraction** - AI-powered insights and key takeaways
+- **Content Outline** - Structured organization and flow
+- **Article Generation** - Complete written content from audio
+- **Social Media Posts** - Platform-optimized content (5 posts)
+- **Notion Integration** - Auto-publish with rich formatting
+- **Knowledge Base** - Add custom context from your files
+- **Custom Prompts** - Personalize AI output per pipeline step
+- **Large File Processing** - Handle files up to 2GB with FFmpeg chunking
+- **Real-time Streaming** - Watch content generate step-by-step
 
-## 🏗️ Project Structure
-
-```
-whisperforge--prime/
-├── app_simple.py          # Main Streamlit application (v3.0.0)
-├── app.py                 # Redirect to main app
-├── core/                  # Core functionality modules
-│   ├── content_generation.py
-│   ├── file_upload.py     # Enhanced large file processing
-│   ├── supabase_integration.py
-│   └── ...
-├── prompts/               # Custom AI prompts
-├── static/                # CSS, JS, and assets
-├── tests/                 # Test suite
-├── docs/                  # Documentation
-└── requirements.txt       # Dependencies
-```
-
-## 🚀 Quick Start
+## Quick Start
 
 ### Prerequisites
 
-- Python 3.8+
+- Python 3.11+
+- FFmpeg (required for audio processing and files >100MB)
 - Supabase account (for data storage)
 - OpenAI API key (for AI processing)
 
-### Installation
-
-1. **Clone the repository**
-   ```bash
-   git clone https://github.com/your-username/whisperforge.git
-   cd whisperforge
-   ```
+#### Installing FFmpeg
 
-2. **Set up virtual environment**
-   ```bash
-   python -m venv venv
-   source venv/bin/activate  # On Windows: venv\Scripts\activate
-   ```
+| Platform | Command |
+|----------|---------|
+| Ubuntu/Debian | `sudo apt install ffmpeg` |
+| macOS | `brew install ffmpeg` |
+| Windows 11 | `winget install ffmpeg` |
 
-3. **Install dependencies**
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-4. **Configure environment variables**
-   ```bash
-   cp env.example .env
-   # Edit .env with your API keys
-   ```
-
-5. **Run the application**
-   ```bash
-   streamlit run app_simple.py
-   ```
+### Installation
 
-## 🔧 Configuration
+#### Automated setup (recommended)
 
-Create a `.env` file with your API keys:
+```bash
+git clone https://github.com/your-username/whisperforge.git
+cd whisperforge
+python main.py
+```
 
-```env
-# Required
-SUPABASE_URL=your_supabase_url
-SUPABASE_ANON_KEY=your_supabase_anon_key
-OPENAI_API_KEY=your_openai_api_key
+`main.py` automatically creates a `.venv`, installs dependencies, and launches the app. On subsequent runs it skips setup and starts immediately.
 
-# Optional
-NOTION_API_KEY=your_notion_api_key
-NOTION_DATABASE_ID=your_notion_database_id
+```bash
+python main.py --setup          # Force re-create the venv
+python main.py --setup --dev    # Re-create venv with dev/test dependencies
+python main.py -- --server.port 8080   # Forward args to Streamlit
 ```
 
-## 🎯 Usage
+#### Manual setup
 
-1. **Upload Audio** - Support for MP3, WAV, M4A, and video files up to 2GB
-2. **Choose Processing Mode** - Standard (≤25MB) or Enhanced Large File (≤2GB)
-3. **Watch Real-time Processing** - See content generate step-by-step
-4. **Review Results** - Comprehensive content package with all outputs
-5. **Auto-publish** - Optional Notion integration for seamless publishing
+```bash
+python -m venv .venv
+source .venv/bin/activate       # Windows: .venv\Scripts\activate
+pip install -r requirements.txt
+streamlit run app_simple.py
+```
 
-## 🧪 Testing
+### Configuration
 
-Before running tests, make sure all dependencies are installed:
+Copy the example env file and fill in your values:
 
 ```bash
-pip install -r requirements.txt
+cp env.example .env
 ```
 
-You can also use the helper script `scripts/setup_test_env.sh` to create a
-virtual environment with the required packages.
+Required variables:
 
-Run the test suite:
+```env
+SUPABASE_URL=your_supabase_url
+SUPABASE_ANON_KEY=your_supabase_anon_key
+OPENAI_API_KEY=your_openai_api_key
+```
 
-```bash
-# Run all tests
-pytest
+Optional variables:
 
-# Run specific test categories
-pytest -m unit          # Unit tests only
-pytest -m integration   # Integration tests only
-pytest tests/test_basic_functionality.py -v  # Specific test file
+```env
+NOTION_API_KEY=your_notion_integration_token
+NOTION_DATABASE_ID=your_notion_database_id
+SENTRY_DSN=your_sentry_dsn
+ENVIRONMENT=development          # or production
+DEBUG=true
+LOG_LEVEL=DEBUG
 ```
 
-## 📚 Documentation
+## Content Pipeline
 
-- [Large File Processing Guide](docs/LARGE_FILE_PROCESSING_v2.8.0.md)
-- [Development Workflow](archived_docs/DEVELOPMENT_WORKFLOW.md)
-- [API Documentation](docs/API.md)
+1. **Transcription** - OpenAI Whisper speech-to-text
+2. **Wisdom Extraction** - Key insights and takeaways
+3. **Outline Creation** - Structured content organization
+4. **Article Generation** - Complete written content
+5. **Social Media** - 5 platform-optimized posts
+6. **Notion Publishing** - Auto-publish with formatted pages
 
-## 🤝 Contributing
+## Project Structure
 
-1. Fork the repository
-2. Create a feature branch (`git checkout -b feature/amazing-feature`)
-3. Commit your changes (`git commit -m 'Add amazing feature'`)
-4. Push to the branch (`git push origin feature/amazing-feature`)
-5. Open a Pull Request
+```
+whisperforge/
+├── main.py                    # App launcher (auto-setup + run)
+├── setup.py                   # Venv creation and dependency installer
+├── app_simple.py              # Main Streamlit application
+├── app.py                     # Redirect to main app
+├── core/                      # Core modules
+│   ├── content_generation.py  # AI content generation
+│   ├── streaming_pipeline.py  # Step-by-step processing
+│   ├── streaming_results.py   # Real-time content display
+│   ├── file_upload.py         # Large file processing
+│   ├── supabase_integration.py# Database operations
+│   ├── auth_wrapper.py        # Authentication
+│   ├── visible_thinking.py    # AI thinking bubbles
+│   ├── session_manager.py     # User session handling
+│   ├── styling.py             # Aurora UI components
+│   └── ...
+├── prompts/                   # Default and custom AI prompts
+├── static/                    # CSS, JS, and assets
+├── scripts/                   # Utility and audit scripts
+├── tests/                     # Test suite
+├── docs/                      # Documentation
+├── requirements.txt           # Production dependencies
+└── requirements-dev.txt       # Dev/test dependencies (includes production)
+```
 
-## 📄 License
+## Testing
 
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+Install dev dependencies:
 
-## 🙏 Acknowledgments
+```bash
+python main.py --setup --dev
+# or manually:
+pip install -r requirements-dev.txt
+```
 
-- OpenAI for Whisper and GPT models
-- Supabase for backend infrastructure
-- Streamlit for the amazing web framework
-- The open-source community for inspiration and tools
+Run tests:
 
----
+```bash
+pytest                                     # All tests
+pytest -m unit                             # Unit tests only
+pytest -m integration                      # Integration tests
+pytest tests/test_basic_functionality.py -v  # Specific file
+```
 
-**WhisperForge v3.0.0** - Transform your audio into intelligent content 🌌
+## Technical Stack
 
-## 🎯 **Architecture Overview**
+- **Frontend**: Streamlit with custom Aurora CSS
+- **Backend**: Supabase (PostgreSQL)
+- **AI**: OpenAI GPT-4 + Whisper
+- **Auth**: Supabase Auth
+- **Deployment**: Streamlit Cloud / Render.com
 
-```
-├── app_simple.py          # Main Streamlit application (v3.0.0)
-├── app.py                 # Redirect to main app
-├── core/
-│   ├── streaming_pipeline.py    # Step-by-step content processing
-│   ├── streaming_results.py     # Real-time content display
-│   ├── content_generation.py    # AI content generation functions
-│   ├── supabase_integration.py  # Database operations
-│   ├── visible_thinking.py      # AI thinking bubbles
-│   ├── session_manager.py       # User session handling
-│   └── styling.py              # Aurora UI components
-└── prompts/                # Default and custom AI prompts
-```
+## Security
 
-## 🌊 **Core Features**
+Recent hardening includes:
 
-### **1. Real-Time Audio Processing**
-- Upload audio files (MP3, WAV, M4A, FLAC, etc.)
-- Automatic transcription using OpenAI Whisper
-- Progressive content generation with live updates
+- HTML content sanitization (XSS prevention)
+- JWT validation with PyJWT (replaced python-jose)
+- XSRF protection enabled
+- Server bound to localhost by default
+- No hardcoded credentials in source
+- Input validation on file uploads
 
-### **2. Enhanced AI Content Pipeline**
-1. **Transcription** - Speech-to-text conversion
-2. **Wisdom Extraction** - Key insights and takeaways
-3. **Outline Creation** - Structured content organization
-4. **Article Generation** - Complete written content
-5. **Social Media** - Platform-optimized posts
-6. **🌌 Notion Publishing** - Auto-publish to Notion with beautiful formatting
-7. **Database Storage** - Persistent content library with Supabase
+## Documentation
 
-### **3. Modern Aurora Interface**
-- Bioluminescent 2025 design system
-- Real-time progress indicators
-- Animated content cards
-- Responsive Aurora color scheme
+- [Large File Processing Guide](docs/LARGE_FILE_PROCESSING_v2.8.0.md)
+- [Critical Fixes Report](docs/CRITICAL_FIXES_REPORT_v2.8.0.md)
+- [Monitoring Setup](docs/monitoring.md)
+- [Changelog](CHANGELOG.md)
 
-## 🔧 **Technical Stack**
+## Contributing
 
-- **Frontend**: Streamlit with custom Aurora CSS
-- **Backend**: Supabase (PostgreSQL)
-- **AI Models**: OpenAI GPT-4
-- **Audio Processing**: OpenAI Whisper
-- **Authentication**: Supabase Auth + OAuth
-- **Deployment**: Streamlit Cloud ready
-
-## 🚀 **Getting Started**
-
-1. **Clone Repository**
-   ```bash
-   git clone <repository-url>
-   cd whisperforge--prime
-   ```
-
-2. **Install Dependencies**
-   ```bash
-   python -m venv venv
-   source venv/bin/activate  # or `venv\Scripts\activate` on Windows
-   pip install -r requirements.txt
-   ```
-
-3. **Environment Setup**
-   Create `.env` file or set environment variables:
-   ```env
-   # Required - Supabase Database
-   SUPABASE_URL=your_supabase_url
-   SUPABASE_ANON_KEY=your_supabase_anon_key
-   SUPABASE_SERVICE_ROLE_KEY=your_service_role_key  # Optional for admin features
-   
-   # Required - AI Provider
-   OPENAI_API_KEY=your_openai_key
-   
-   # Notion Integration - Auto-Publishing
-   NOTION_API_KEY=your_notion_integration_token
-   NOTION_DATABASE_ID=your_notion_database_id
-   
-   # Optional - OAuth & Integrations
-   OAUTH_REDIRECT_URL=http://localhost:8501  # For OAuth flows
-   
-   # Optional - Security & Monitoring
-   JWT_SECRET=your_jwt_secret_key
-   SENTRY_DSN=your_sentry_dsn  # For error tracking
-   
-   # Optional - Development
-   DEBUG=true
-   LOG_LEVEL=INFO
-   ENVIRONMENT=development  # or 'production'
-   ```
-
-4. **Run Application**
-   ```bash
-   ./start_app.sh                 # development (default)
-   ./start_app.sh production      # production mode
-   ```
-
-## 🎨 **Aurora Design System**
-
-The WhisperForge UI uses a custom Aurora design system featuring:
-
-- **Bioluminescent Effects**: Glowing borders and animations
-- **Gradient Backgrounds**: Dynamic color transitions
-- **Glass Morphism**: Backdrop blur effects
-- **Responsive Cards**: Animated content containers
-- **Progress Streams**: Real-time processing indicators
-
-## 📊 **Database Schema**
-
-### **Core Tables**
-- `users` - User accounts and settings
-- `content` - Generated content and metadata  
-- `prompts` - Custom AI prompts
-- `knowledge_base` - User-uploaded files
-- `api_keys` - Encrypted API credentials
-
-## 🔐 **Security Features**
-
-- **Encrypted Storage**: API keys and sensitive data
-- **Session Management**: Secure user sessions
-- **Input Validation**: File size and type restrictions
-- **Rate Limiting**: API usage controls
-
-## 🛡 **Current Known Issues**
-
-1. **Database Content Retrieval**: 26 processed files not displaying in history (investigating field name mismatches)
-2. **Real-time Streaming**: Content shows but not truly real-time like cursor chat
-3. **Session Persistence**: Authentication doesn't persist across refreshes consistently
-4. **Prompt Saving**: Custom prompts saving but not loading properly
-5. **Thinking Bubbles**: AI thinking stream not integrating smoothly
-
-## 🔄 **Debugging Tools**
-
-The content history page includes debug information:
-- Database connection status
-- Raw record samples
-- Session state inspection
-- Content structure analysis
-
-## 📈 **Roadmap**
-
-### **Immediate Fixes**
-- [ ] Fix content history display issues
-- [ ] Implement true real-time streaming
-- [ ] Resolve session persistence
-- [ ] Debug prompt saving/loading
-
-### **Enhancements**
-- [ ] Batch audio processing
-- [ ] Export to multiple formats
-- [ ] Advanced AI model selection
-- [ ] Team collaboration features
-
-## 💡 **Contributing**
-
-This is currently a private project focused on creating the best audio-to-content transformation experience with a beautiful, modern interface.
-
-## 📄 **License**
-
-MIT License - See LICENSE file for details.
-
----
-
-**WhisperForge** - Transforming audio into actionable insights with the beauty of Aurora. 🌌 
-
-## 🏗 **Architecture (Simplified)**
-
-### **Session Management**
-```python
-# Simple, reliable pattern
-if 'authenticated' not in st.session_state:
-    st.session_state.authenticated = False
-
-@st.cache_resource  
-def init_supabase():
-    return get_supabase_client()
-```
+See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
 
-### **Database Pattern**
-- **Supabase Client**: Cached with `@st.cache_resource`
-- **User Data**: Loaded fresh each session (not cached in session state)
-- **Content Storage**: Direct to database, no complex state management
+## License
 
-### **Authentication Flow**
-1. User enters credentials → Verify against Supabase
-2. Set simple session state flags → No tokens or complex persistence  
-3. Load user preferences from database → Use `@st.cache_data` for performance 
\ No newline at end of file
+MIT License - see [LICENSE](LICENSE) for details.
diff --git a/REPO_CLEANUP_TASKS.md b/REPO_CLEANUP_TASKS.md
deleted file mode 100644
index 58198dd..0000000
--- a/REPO_CLEANUP_TASKS.md
+++ /dev/null
@@ -1,35 +0,0 @@
-# GitHub Cleanup Recommendations
-
-This project still contains some leftover files and outdated branches that can be cleaned up to reduce clutter. Below are recommended tasks.
-
-## 1. Branch Maintenance
-- Run `git fetch --all --prune` to remove stale remote-tracking branches.
-- Review `git branch -r` for remote branches that have been merged or are no longer active.
-- Delete obsolete local branches with `git branch -D <branch>` once they are safely merged.
-- Consider enabling branch protection rules on your main branch to avoid direct pushes.
-
-## 2. Remove Tracked Build Artifacts
-- Several `__pycache__` directories and `.pyc` files are committed (see `git ls-files | grep __pycache__`). These should be removed and kept out of Git history.
-- Delete existing tracked compiled files and commit the removal.
-- Ensure `.gitignore` covers these patterns (it already includes `__pycache__/` and `*.py[cod]`).
-
-## 3. Delete Log Files
-- Files such as `test_startup.log` and `streamlit.log` are checked in. Remove them and rely on the existing `*.log` rule in `.gitignore` to avoid future commits.
-
-## 4. Review Archived Directories
-- The `archived_docs/` and `archived_old_version/` folders contain historical documents and code. If these are no longer needed in the repo, consider deleting or moving them to a separate archive branch to reduce repository size.
-
-## 5. Check Large Files
-- Run `git lfs track` or `git ls-files -s | sort -n -k1` to detect unusually large files. Migrate binaries to Git LFS or remove them if not essential.
-
-## 6. Automated Cleanup Script
-- Create a simple script to prune old branches and remove untracked caches. Example commands:
-  ```bash
-  git fetch --all --prune
-  for branch in $(git branch --merged | grep -v '\*' | grep -v main); do
-      git branch -d "$branch"
-  done
-  git clean -fdX
-  ```
-
-Cleaning up these items will keep the repository lean and make future development smoother.
diff --git a/WHISPERFORGE_V2.7.0_IMPLEMENTATION_PLAN.md b/WHISPERFORGE_V2.7.0_IMPLEMENTATION_PLAN.md
deleted file mode 100644
index a7546a1..0000000
--- a/WHISPERFORGE_V2.7.0_IMPLEMENTATION_PLAN.md
+++ /dev/null
@@ -1,185 +0,0 @@
-# WhisperForge v2.7.0 Implementation Plan & Summary
-
-## 🎯 Mission Accomplished: Complete Pipeline Enhancement
-
-### 📋 Original Issues Identified
-1. **No Real-Time Streaming**: Content didn't appear as it was generated during pipeline execution
-2. **Duplicate Sidebar**: Settings were duplicated between sidebar on transform page and settings page
-3. **Ugly UI Design**: Current design didn't look truly Aurora borealis/bioluminescent as planned
-4. **Rough Notion Formatting**: Notion page creation looked rough, not the beautiful formatting previously spec'd
-5. **Prompts Not Applying**: When user edited and saved prompts, they weren't being applied to content generation
-
-### ✅ Issues Resolved in v2.7.0
-
-#### 1. Real-Time Streaming Implementation
-- **Added Live Content Containers**: Created expandable sections for each pipeline step
-- **Immediate Content Display**: Content appears as soon as each step completes
-- **Stream to UI**: All generated content (transcript, wisdom, research, outline, article, social, editor notes, Notion) streams to UI immediately
-- **Visual Feedback**: Users see exactly what's happening at each step
-
-#### 2. Clean Transform Page
-- **Removed Duplicate Sidebar**: Eliminated redundant settings from transform page
-- **Focused Interface**: Clean file upload interface with status indicators
-- **Settings Consolidation**: All configuration moved to dedicated Settings tab
-- **Status Dashboard**: Quick connection status for OpenAI, Notion, Research, and Editor
-
-#### 3. Enhanced Aurora UI Design
-- **True Bioluminescent Styling**: Implemented glowing effects, gradients, and animations
-- **Aurora Navigation**: Beautiful header with animated scanning effects and pipeline indicators
-- **Enhanced Visual Effects**: Proper Aurora theme with shimmer, pulse, and glow animations
-- **Professional Polish**: Consistent styling throughout the entire application
-
-#### 4. Beautiful Notion Formatting
-- **Rich Page Headers**: Beautiful titles with Aurora branding and timestamps
-- **Callout Sections**: Wisdom summary in purple callout with lightbulb icon
-- **Research Entities**: Blue callouts with research icons and bulleted link lists
-- **Gem Marking**: Orange-colored gem icons for high-value research links
-- **Structured Toggles**: Organized content sections with proper formatting
-- **Professional Footer**: Green completion callout with pipeline summary
-
-#### 5. Custom Prompt System
-- **Prompt Loading**: Automatic loading from `prompts/default/` directory
-- **Step Mapping**: Proper mapping of prompts to pipeline steps (wisdom, outline, article, social)
-- **Integration**: Custom prompts passed to generation functions
-- **Article Prompt**: Created comprehensive article generation prompt
-- **Live Application**: Prompts applied during content generation process
-
-## 🔧 Technical Implementation Details
-
-### Real-Time Streaming Architecture
-```python
-# Create expandable containers for each step
-transcript_container = st.expander("🎙️ Transcription", expanded=False)
-wisdom_container = st.expander("💡 Wisdom Extraction", expanded=False)
-# ... etc for all 8 steps
-
-# Stream content immediately after generation
-with transcript_container:
-    st.markdown("**✅ Transcription Complete**")
-    st.text_area("Transcript", transcript, height=200, disabled=True)
-```
-
-### Prompt Loading System
-```python
-def load_custom_prompts():
-    """Load custom prompts from the prompts directory"""
-    prompts = {}
-    prompt_dir = "prompts/default"
-    # Load all .md files as prompts
-
-def get_prompt_for_step(step_name: str, custom_prompts: Dict[str, str] = None):
-    """Get the appropriate prompt for a pipeline step"""
-    prompt_mapping = {
-        'wisdom': 'wisdom_extraction',
-        'outline': 'outline_creation', 
-        'social': 'social_media',
-        'article': 'article_generation'
-    }
-```
-
-### Enhanced Aurora Styling
-- **CSS Variables**: Proper Aurora color scheme with gradients
-- **Animations**: Scanning effects, pulse animations, shimmer effects
-- **Visual Hierarchy**: Consistent styling across all components
-- **Responsive Design**: Works across different screen sizes
-
-### Notion Formatting Enhancement
-- **Structured Headers**: Beautiful page titles with Aurora branding
-- **Rich Callouts**: Color-coded sections with appropriate icons
-- **Research Display**: Proper entity formatting with gem marking
-- **Professional Metadata**: Comprehensive footer with generation details
-
-## 📊 Current System Architecture
-
-### 8-Step Pipeline
-1. **🎙️ Transcription** → OpenAI Whisper → Real-time display
-2. **💡 Wisdom Extraction** → Custom prompt → Immediate streaming
-3. **🔍 Research Enrichment** → Entity extraction → Live research display
-4. **📋 Outline Creation** → Custom prompt → Structured outline streaming
-5. **📝 Article Generation** → Custom prompt → Full article streaming
-6. **📱 Social Content** → Custom prompt → Social media content streaming
-7. **📝 Editor Review** → AI feedback → Editor notes and revisions
-8. **🌌 Notion Publishing** → Beautiful formatting → Auto-publish with status
-
-### Navigation System
-- **🎵 Transform**: Clean file upload and processing with real-time streaming
-- **📚 Content Library**: Browse and search processed content history
-- **⚙️ Settings**: Comprehensive API configuration and pipeline settings
-- **🧠 Knowledge Base**: Domain expertise file management
-- **📝 Prompts**: Custom prompt editing and management
-
-## 🚀 Deployment Status
-
-### Production Environment
-- **Platform**: Render.com with auto-deploy from main branch
-- **Version**: v2.7.0 deployed successfully
-- **Status**: All features tested and verified working
-- **URL**: Production WhisperForge Aurora application
-
-### Local Development
-- **Environment**: Python virtual environment with all dependencies
-- **Testing**: App imports successfully, no errors
-- **Git**: All changes committed and pushed to main branch
-
-## 🎯 Future Enhancement Roadmap
-
-### Phase 1: Performance Optimization (v2.8.0)
-- **Streaming Optimization**: Implement WebSocket connections for even faster streaming
-- **Caching System**: Cache generated content for faster re-processing
-- **Background Processing**: Move heavy operations to background tasks
-- **Progress Indicators**: More granular progress tracking within each step
-
-### Phase 2: Advanced Features (v2.9.0)
-- **Multi-Language Support**: Support for non-English audio transcription
-- **Batch Processing**: Process multiple audio files simultaneously
-- **Export Options**: PDF, Word, and other format exports
-- **Template System**: Customizable output templates
-
-### Phase 3: Collaboration Features (v3.0.0)
-- **Team Workspaces**: Shared content libraries and settings
-- **Version Control**: Track changes and revisions to generated content
-- **Approval Workflows**: Editorial approval processes
-- **Integration Hub**: Connect with more platforms (Google Docs, Slack, etc.)
-
-### Phase 4: AI Enhancement (v3.1.0)
-- **Advanced Models**: Support for GPT-4 Turbo, Claude, and other models
-- **Custom Training**: Fine-tune models on user's specific content
-- **Quality Scoring**: Automatic quality assessment and improvement suggestions
-- **Smart Routing**: Automatically choose best model for each content type
-
-## 📈 Success Metrics
-
-### User Experience Improvements
-- ✅ **Real-time Feedback**: Users see content generated immediately
-- ✅ **Clean Interface**: Focused, professional design without clutter
-- ✅ **Visual Appeal**: True Aurora bioluminescent theme implemented
-- ✅ **Notion Integration**: Beautiful, structured pages automatically created
-- ✅ **Customization**: Users can edit and apply custom prompts
-
-### Technical Achievements
-- ✅ **Streaming Architecture**: Live content display during processing
-- ✅ **Modular Design**: Clean separation of concerns and reusable components
-- ✅ **Prompt System**: Flexible, extensible prompt management
-- ✅ **Enhanced Styling**: Professional UI with consistent Aurora theme
-- ✅ **Production Ready**: Deployed and working in production environment
-
-### Content Quality
-- ✅ **Custom Prompts**: Tailored content generation based on user preferences
-- ✅ **Rich Research**: Enhanced entity extraction with gem marking
-- ✅ **Editorial Review**: AI feedback and revision capabilities
-- ✅ **Structured Output**: Well-organized content across all formats
-- ✅ **Professional Notion**: Beautiful, structured pages with rich formatting
-
-## 🎉 Conclusion
-
-WhisperForge v2.7.0 represents a complete transformation of the user experience, addressing all core issues identified:
-
-1. **Real-time streaming** provides immediate feedback during processing
-2. **Clean interface** eliminates confusion and focuses on core functionality
-3. **Beautiful Aurora design** creates a professional, engaging experience
-4. **Enhanced Notion formatting** produces publication-ready structured content
-5. **Custom prompt system** allows users to tailor content generation to their needs
-
-The application now delivers on its promise of transforming audio into structured content with AI magic, providing users with a seamless, beautiful, and powerful content creation experience.
-
-**Status**: ✅ All objectives achieved, deployed to production, ready for user feedback and future enhancements. 
\ No newline at end of file
diff --git a/app.py b/app.py
index e14a27a..354d0c7 100644
--- a/app.py
+++ b/app.py
@@ -1,18 +1,15 @@
 # WhisperForge - Main Application Redirect
-# 
+#
 # This file redirects to the current main application: app_simple.py
-# 
+#
 # The old OAuth version has been archived
 
-import streamlit as st
 import os
 
+import streamlit as st
+
 # Page config
-st.set_page_config(
-    page_title="WhisperForge - Redirect",
-    page_icon="🌌",
-    layout="wide"
-)
+st.set_page_config(page_title="WhisperForge - Redirect", page_icon="🌌", layout="wide")
 
 st.error("""
 🔄 **Application Redirect**
@@ -31,7 +28,9 @@
 **Note:** The Procfile has been updated to use app_simple.py for production deployment.
 """)
 
-st.info("This redirect will be removed in a future version. Please update your bookmarks and scripts to use `app_simple.py`.")
+st.info(
+    "This redirect will be removed in a future version. Please update your bookmarks and scripts to use `app_simple.py`."
+)
 
 # Show current working directory and available files
 st.markdown("### Available Files:")
@@ -42,4 +41,4 @@
     elif "deprecated" in file or "backup" in file:
         st.warning(f"⚠️ **{file}** (Deprecated)")
     else:
-        st.info(f"ℹ️ **{file}**") 
\ No newline at end of file
+        st.info(f"ℹ️ **{file}**")
diff --git a/app_simple.py b/app_simple.py
old mode 100644
new mode 100755
index 0e7a0d6..55ee762
--- a/app_simple.py
+++ b/app_simple.py
@@ -1,2372 +1,374 @@
 # WhisperForge Simple - Clean, Focused Audio Content Platform
-import streamlit as st
 import os
-import tempfile
 import time
-from datetime import datetime
-from typing import Dict, Optional
 
-# Essential imports only
+import streamlit as st
 from dotenv import load_dotenv
-load_dotenv()
 
-# Page config first
-st.set_page_config(
-    page_title="WhisperForge",
-    page_icon="🌌",
-    layout="wide"
+from core.config import Config
+from core.content_display import show_results
+from core.file_upload import EnhancedLargeFileProcessor
+from core.pages import show_knowledge_base, show_prompts_page, show_settings_page
+from core.pipeline import process_pipeline
+from core.services import Services, set_services
+from core.styling import (
+    apply_aurora_theme,
+    create_aurora_header,
 )
-
-# Core imports
-from core.content_generation import transcribe_audio, generate_wisdom, generate_outline, generate_article, generate_social_content
-from core.styling import apply_aurora_theme, create_aurora_header, create_aurora_progress_card, create_aurora_step_card, create_aurora_content_card, AuroraComponents
 from core.supabase_integration import get_supabase_client
-from core.file_upload import EnhancedLargeFileProcessor
 
-# Apply beautiful theme
-apply_aurora_theme()
+load_dotenv()
 
-# === PROMPT LOADING SYSTEM ===
-def load_custom_prompts():
-    """Load custom prompts from the prompts directory"""
-    prompts = {}
-    prompt_dir = "prompts/default"
-    
-    if os.path.exists(prompt_dir):
-        for filename in os.listdir(prompt_dir):
-            if filename.endswith('.md'):
-                prompt_name = filename.replace('.md', '')
-                try:
-                    with open(os.path.join(prompt_dir, filename), 'r', encoding='utf-8') as f:
-                        prompts[prompt_name] = f.read()
-                except Exception as e:
-                    st.warning(f"Failed to load prompt {filename}: {e}")
-    
-    return prompts
-
-def load_template(template_name: str) -> Optional[str]:
-    """Load an article template by name"""
-    template_path = os.path.join('templates', f'{template_name}.md')
-    if os.path.exists(template_path):
-        return open(template_path, 'r', encoding='utf-8').read()
-    return None
-
-def get_prompt_for_step(step_name: str, custom_prompts: Dict[str, str] = None) -> Optional[str]:
-    """Get the appropriate prompt for a pipeline step"""
-    if not custom_prompts:
-        custom_prompts = load_custom_prompts()
-    
-    # Map step names to prompt files
-    prompt_mapping = {
-        'wisdom': 'wisdom_extraction',
-        'outline': 'outline_creation', 
-        'social': 'social_media',
-        'article': 'article_generation'  # We'll create this
-    }
-    
-    prompt_key = prompt_mapping.get(step_name)
-    if prompt_key and prompt_key in custom_prompts:
-        return custom_prompts[prompt_key]
-    
-    return None
-
-# === NOTION INTEGRATION ===
-def create_notion_page(title: str, content_data: Dict[str, str]) -> Optional[str]:
-    """Create a Notion page with WhisperForge content"""
-    try:
-        from notion_client import Client
-        
-        api_key = os.getenv("NOTION_API_KEY")
-        database_id = os.getenv("NOTION_DATABASE_ID")
-        
-        if not api_key or not database_id:
-            st.warning("⚠️ Notion not configured. Set NOTION_API_KEY and NOTION_DATABASE_ID to auto-publish.")
-            return None
-        
-        client = Client(auth=api_key)
-        
-        # Build content blocks
-        children = []
-        
-        # Add beautiful header with summary
-        children.append({
-            "type": "heading_1",
-            "heading_1": {
-                "rich_text": [
-                    {"type": "text", "text": {"content": "🌌 "}, "annotations": {"color": "blue"}},
-                    {"type": "text", "text": {"content": title}, "annotations": {"bold": True}}
-                ]
-            }
-        })
-        
-        # Add creation info
-        children.append({
-            "type": "paragraph",
-            "paragraph": {
-                "rich_text": [
-                    {"type": "text", "text": {"content": "✨ Generated with "}},
-                    {"type": "text", "text": {"content": "WhisperForge Aurora"}, 
-                     "annotations": {"bold": True, "color": "blue"}},
-                    {"type": "text", "text": {"content": f" • {datetime.now().strftime('%B %d, %Y at %I:%M %p')}"}}
-                ]
-            }
-        })
-        
-        children.append({"type": "divider", "divider": {}})
-        
-        # Add wisdom summary callout if exists
-        if content_data.get('wisdom'):
-            children.append({
-                "type": "callout",
-                "callout": {
-                    "rich_text": [
-                        {"type": "text", "text": {"content": "Key Insights & Wisdom"}},
-                        {"type": "text", "text": {"content": f"\n\n{content_data['wisdom'][:1800]}"}}
-                    ],
-                    "color": "purple_background",
-                    "icon": {"type": "emoji", "emoji": "💡"}
-                }
-            })
-        
-        # Add content sections as toggles
-        sections = [
-            ("📝 Transcript", content_data.get('transcript')),
-            ("💡 Wisdom", content_data.get('wisdom')),
-            ("🔍 Research Links", content_data.get('research')),
-            ("📋 Outline", content_data.get('outline')),
-            ("📰 Article", content_data.get('article')),
-            ("📱 Social Content", content_data.get('social_content'))
-        ]
-        
-        for section_title, section_content in sections:
-            if section_content:
-                # Handle research data specially
-                if section_title == "🔍 Research Links" and isinstance(section_content, dict):
-                    research_children = []
-                    entities = section_content.get('entities', [])
-                    
-                    if entities:
-                        for entity in entities[:5]:  # Limit entities
-                            entity_name = entity.get('name', 'Unknown Entity')
-                            why_matters = entity.get('why_matters', 'No description available')
-                            links = entity.get('links', [])
-                            
-                            # Entity as beautiful callout
-                            research_children.append({
-                                "type": "callout",
-                                "callout": {
-                                    "rich_text": [
-                                        {"type": "text", "text": {"content": entity_name}, "annotations": {"bold": True}},
-                                        {"type": "text", "text": {"content": f"\n{why_matters}"}}
-                                    ],
-                                    "color": "blue_background",
-                                    "icon": {"type": "emoji", "emoji": "🔬"}
-                                }
-                            })
-                            
-                            # Links as bulleted list
-                            if links:
-                                for link in links[:3]:  # Limit links
-                                    link_title = link.get('title', 'Link')
-                                    link_url = link.get('url', '#')
-                                    link_desc = link.get('description', '')
-                                    is_gem = link.get('is_gem', False)
-                                    
-                                    gem_icon = "💎" if is_gem else "🔗"
-                                    color = "orange" if is_gem else "default"
-                                    
-                                    research_children.append({
-                                        "type": "bulleted_list_item",
-                                        "bulleted_list_item": {
-                                            "rich_text": [
-                                                {"type": "text", "text": {"content": f"{gem_icon} "}, "annotations": {"color": color}},
-                                                {"type": "text", "text": {"content": link_title}, "annotations": {"bold": True}},
-                                                {"type": "text", "text": {"content": f" - {link_desc}"}, "annotations": {"italic": True}}
-                                            ]
-                                        }
-                                    })
-                    else:
-                        research_children.append({
-                            "type": "paragraph",
-                            "paragraph": {
-                                "rich_text": [{"type": "text", "text": {"content": "No research entities found."}}]
-                            }
-                        })
-                    
-                    children.append({
-                        "type": "toggle",
-                        "toggle": {
-                            "rich_text": [{"type": "text", "text": {"content": section_title}}],
-                            "children": research_children
-                        }
-                    })
-                else:
-                    # Handle regular text content
-                    if isinstance(section_content, str):
-                        # Chunk content for Notion's limits
-                        chunks = [section_content[i:i+1800] for i in range(0, len(section_content), 1800)]
-                        
-                        children.append({
-                            "type": "toggle",
-                            "toggle": {
-                                "rich_text": [{"type": "text", "text": {"content": section_title}}],
-                                "children": [
-                                    {
-                                        "type": "paragraph",
-                                        "paragraph": {
-                                            "rich_text": [{"type": "text", "text": {"content": chunk}}]
-                                        }
-                                    } for chunk in chunks[:5]  # Limit chunks
-                                ]
-                            }
-                        })
-        
-        # Add beautiful footer
-        children.extend([
-            {"type": "divider", "divider": {}},
-            {
-                "type": "callout",
-                "callout": {
-                    "rich_text": [
-                        {"type": "text", "text": {"content": "Content Generation Complete"}, "annotations": {"bold": True}},
-                        {"type": "text", "text": {"content": f"\n\n🤖 AI Pipeline: 8 steps completed successfully"}},
-                        {"type": "text", "text": {"content": f"\n⏱️ Generated: {datetime.now().strftime('%B %d, %Y at %I:%M %p')}"}},
-                        {"type": "text", "text": {"content": f"\n🌌 Powered by WhisperForge Aurora"}}
-                    ],
-                    "color": "green_background",
-                    "icon": {"type": "emoji", "emoji": "✅"}
-                }
-            }
-        ])
-        
-        # Create the page
-        response = client.pages.create(
-            parent={"database_id": database_id},
-            icon={"type": "emoji", "emoji": "🌌"},
-            properties={
-                "Name": {"title": [{"text": {"content": title[:100]}}]}
-            },
-            children=children[:50]  # Limit total blocks
-        )
-        
-        if response and 'id' in response:
-            page_id = response['id']
-            page_url = f"https://notion.so/{page_id.replace('-', '')}"
-            return page_url
-        
-        return None
-        
-    except ImportError:
-        st.warning("⚠️ Install notion-client to enable Notion publishing: pip install notion-client")
-        return None
-    except Exception as e:
-        st.error(f"❌ Notion publishing failed: {str(e)}")
-        return None
+# Page config first
+st.set_page_config(page_title="WhisperForge", page_icon="\U0001f30c", layout="wide")
 
-def generate_ai_title(transcript: str) -> str:
-    """Generate an AI title for the content"""
-    try:
-        from core.content_generation import generate_content
-        
-        prompt = f"""Generate a concise, descriptive title (max 60 characters) for this audio transcript:
+# Apply beautiful theme
+apply_aurora_theme()
 
-{transcript[:500]}...
 
-Title should be:
-- Clear and specific
-- Professional
-- Capture the main topic
-- No quotes or special characters
+# === SIMPLE AUTHENTICATION ===
 
-Title:"""
-        
-        title = generate_content(prompt, "OpenAI", "gpt-4", {})
-        return title.strip().replace('"', '').replace("'", "")[:60]
-    except:
-        return f"WhisperForge Content - {datetime.now().strftime('%Y-%m-%d %H:%M')}"
 
-# === SIMPLE AUTHENTICATION ===
 def init_session():
-    """Initialize simple session state"""
-    if 'authenticated' not in st.session_state:
+    """Initialize simple session state and wire up the DI container."""
+    if "authenticated" not in st.session_state:
         st.session_state.authenticated = False
-    if 'user_id' not in st.session_state:
+    if "user_id" not in st.session_state:
         st.session_state.user_id = None
-    if 'user_email' not in st.session_state:
+    if "user_email" not in st.session_state:
         st.session_state.user_email = None
 
+    # Initialise the service container once per process
+    if "services_initialised" not in st.session_state:
+        set_services(Services(config=Config.from_env()))
+        st.session_state.services_initialised = True
+
+
 def show_login():
-    """Simple test login"""
+    """Simple test login."""
     create_aurora_header()
-    
-    st.markdown("### 🔐 Login to WhisperForge")
-    
-    # Test login button
-    if st.button("🚀 Login with Test Account", type="primary", use_container_width=True):
+    st.markdown("### Login to WhisperForge")
+
+    if st.button("Login with Test Account", type="primary", use_container_width=True):
         st.session_state.authenticated = True
         st.session_state.user_id = 1
         st.session_state.user_email = "test@whisperforge.ai"
-        st.success("✅ Logged in successfully!")
+        st.success("Logged in successfully!")
         time.sleep(1)
         st.rerun()
-    
+
     st.markdown("---")
     st.markdown("**Demo Mode**: Click above to access WhisperForge")
 
-# === CORE PROCESSING PIPELINE ===
-
-def show_processing_pipeline(current_step=0, step_progress=0, total_progress=0, status_message="", processing_time=""):
-    """Display beautiful Aurora-styled processing pipeline visualization"""
-    
-    # Define the 6-step pipeline
-    pipeline_steps = [
-        {
-            "icon": "🎤",
-            "title": "Transcription",
-            "description": "Converting audio to text using Whisper AI",
-            "status": "pending"
-        },
-        {
-            "icon": "💡",
-            "title": "Wisdom Extraction",
-            "description": "Extracting key insights and wisdom",
-            "status": "pending"
-        },
-        {
-            "icon": "📋",
-            "title": "Outline Creation",
-            "description": "Structuring content with clear outline",
-            "status": "pending"
-        },
-        {
-            "icon": "📰",
-            "title": "Article Generation",
-            "description": "Creating comprehensive article content",
-            "status": "pending"
-        },
-        {
-            "icon": "📱",
-            "title": "Social Content",
-            "description": "Generating social media posts",
-            "status": "pending"
-        },
-        {
-            "icon": "🌌",
-            "title": "Notion Publishing",
-            "description": "Publishing to Notion workspace",
-            "status": "pending"
-        }
+
+# === NAVIGATION ===
+
+
+def create_aurora_navigation():
+    """Create Aurora navigation header with 5 tabs and pipeline badges."""
+    # Build pipeline badge HTML from session results
+    badge_data = [
+        ("transcript", "\U0001f399\ufe0f", "Transcript"),
+        ("wisdom", "\U0001f4a1", "Wisdom"),
+        ("outline", "\U0001f4cb", "Outline"),
+        ("article", "\U0001f4f0", "Article"),
+        ("social_content", "\U0001f4f1", "Social"),
     ]
-    
-    # Update step statuses based on current progress
-    for i, step in enumerate(pipeline_steps):
-        if i < current_step:
-            step["status"] = "completed"
-        elif i == current_step:
-            step["status"] = "active"
-        else:
-            step["status"] = "pending"
-    
-    # Create the pipeline visualization HTML
-    steps_html = ""
-    for i, step in enumerate(pipeline_steps):
-        progress_width = step_progress if i == current_step else (100 if step["status"] == "completed" else 0)
-        
-        status_text = {
-            "pending": "Waiting",
-            "active": "Processing",
-            "completed": "Complete",
-            "error": "Error"
-        }.get(step["status"], "Waiting")
-        
-        steps_html += f"""
-        <div class="aurora-pipeline-step {step['status']}">
-            <div class="aurora-step-progress" style="width: {progress_width}%;"></div>
-            <span class="aurora-step-icon">{step['icon']}</span>
-            <h4 class="aurora-step-title">{step['title']}</h4>
-            <p class="aurora-step-description">{step['description']}</p>
-            <div class="aurora-step-status">{status_text}</div>
-        </div>
-        """
-    
-    # Create the complete pipeline HTML
-    pipeline_html = f"""
-    <div class="aurora-pipeline-container">
-        <div class="aurora-pipeline-header">
-            <h3 class="aurora-pipeline-title">Content Transformation Pipeline</h3>
-            <p class="aurora-pipeline-subtitle">6-Step AI-Powered Processing</p>
-        </div>
-        
-        <div class="aurora-overall-progress">
-            <div class="aurora-progress-header">
-                <span class="aurora-progress-label">Overall Progress</span>
-                <span class="aurora-progress-percentage">{total_progress}%</span>
-            </div>
-            <div class="aurora-progress-bar-container">
-                <div class="aurora-progress-bar" style="width: {total_progress}%;"></div>
-            </div>
-        </div>
-        
-        <div class="aurora-pipeline-steps">
-            {steps_html}
-        </div>
-        
-        {f'''
-        <div class="aurora-processing-status active">
-            <span class="aurora-status-icon">⚡</span>
-            <span class="aurora-status-text">{status_message}</span>
-            <span class="aurora-status-time">{processing_time}</span>
-        </div>
-        ''' if status_message else ''}
-    </div>
-    """
-    
-    st.markdown(pipeline_html, unsafe_allow_html=True)
-
-def process_audio_pipeline(audio_file):
-    """Core audio to content pipeline with beautiful Aurora visualization"""
-    import time
-    from datetime import datetime
-    
-    results = {}
-    start_time = time.time()
-    
-    # Load custom prompts
-    custom_prompts = load_custom_prompts()
-    if custom_prompts:
-        st.info(f"📝 Using {len(custom_prompts)} custom prompts")
-    
-    # Initialize beautiful pipeline visualization
-    pipeline_placeholder = st.empty()
-    
-    # Create real-time content display containers
-    st.markdown("### 🌌 Live Content Generation")
-    
-    # Create expandable containers for each step
-    transcript_container = st.expander("🎙️ Transcription", expanded=False)
-    wisdom_container = st.expander("💡 Wisdom Extraction", expanded=False)
-    outline_container = st.expander("📋 Outline Creation", expanded=False)
-    article_container = st.expander("📝 Article Generation", expanded=False)
-    social_container = st.expander("📱 Social Content", expanded=False)
-    notion_container = st.expander("🌌 Notion Publishing", expanded=False)
-    
-    try:
-        # Step 1: Transcription
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=0, 
-                step_progress=0, 
-                total_progress=0,
-                status_message="Starting transcription process...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        # Import transcription function
-        from core.content_generation import transcribe_audio
-        
-        # Create temporary file
-        import tempfile
-        import os
-        with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(audio_file.name)[1]) as tmp_file:
-            tmp_file.write(audio_file.getvalue())
-            tmp_file_path = tmp_file.name
-        
-        try:
-            # Transcription with progress updates
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=0, 
-                    step_progress=50, 
-                    total_progress=8,
-                    status_message="Transcribing audio with Whisper AI...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            transcript = transcribe_audio(tmp_file_path)
-            if not transcript or "Error" in transcript:
-                st.error(f"Transcription failed: {transcript}")
-                return None
-            
-            results['transcript'] = transcript
-            
-            # Stream transcript to UI immediately
-            with transcript_container:
-                st.markdown("**✅ Transcription Complete**")
-                st.text_area("Transcript", transcript, height=200, disabled=True)
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=0, 
-                    step_progress=100, 
-                    total_progress=17,
-                    status_message="Transcription complete!",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            # Step 2: Wisdom Extraction
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=1, 
-                    step_progress=0, 
-                    total_progress=17,
-                    status_message="Extracting wisdom and insights...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            from core.content_generation import generate_wisdom
-            wisdom_prompt = get_prompt_for_step('wisdom', custom_prompts)
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=1, 
-                    step_progress=50, 
-                    total_progress=25,
-                    status_message="Analyzing content for key insights...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            wisdom = generate_wisdom(transcript, custom_prompt=wisdom_prompt, knowledge_base={})
-            results['wisdom'] = wisdom
-            
-            # Stream wisdom to UI immediately
-            with wisdom_container:
-                st.markdown("**✅ Wisdom Extraction Complete**")
-                st.markdown(wisdom)
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=1, 
-                    step_progress=100, 
-                    total_progress=33,
-                    status_message="Wisdom extraction complete!",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            # Step 3: Outline Creation
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=2, 
-                    step_progress=0, 
-                    total_progress=33,
-                    status_message="Creating structured outline...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            from core.content_generation import generate_outline
-            outline_prompt = get_prompt_for_step('outline', custom_prompts)
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=2, 
-                    step_progress=50, 
-                    total_progress=42,
-                    status_message="Structuring content hierarchy...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            outline = generate_outline(transcript, wisdom, custom_prompt=outline_prompt, knowledge_base={})
-            results['outline'] = outline
-            
-            # Stream outline to UI immediately
-            with outline_container:
-                st.markdown("**✅ Outline Creation Complete**")
-                st.markdown(outline)
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=2, 
-                    step_progress=100, 
-                    total_progress=50,
-                    status_message="Outline creation complete!",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            # Step 4: Article Generation
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=3, 
-                    step_progress=0, 
-                    total_progress=50,
-                    status_message="Generating comprehensive article...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            from core.content_generation import generate_article
-            article_prompt = get_prompt_for_step('article', custom_prompts)
-            selected_template = st.session_state.get('article_template')
-            if selected_template:
-                template_text = load_template(selected_template)
-                if template_text:
-                    article_prompt = template_text + "\n" + article_prompt
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=3, 
-                    step_progress=50, 
-                    total_progress=58,
-                    status_message="Writing detailed article content...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            article = generate_article(transcript, wisdom, outline, custom_prompt=article_prompt, knowledge_base={})
-            results['article'] = article
-            
-            # Stream article to UI immediately
-            with article_container:
-                st.markdown("**✅ Article Generation Complete**")
-                st.markdown(article)
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=3, 
-                    step_progress=100, 
-                    total_progress=67,
-                    status_message="Article generation complete!",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            # Step 5: Social Content
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=4, 
-                    step_progress=0, 
-                    total_progress=67,
-                    status_message="Creating social media content...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            from core.content_generation import generate_social_content
-            social_prompt = get_prompt_for_step('social', custom_prompts)
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=4, 
-                    step_progress=50, 
-                    total_progress=75,
-                    status_message="Generating social media posts...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            social = generate_social_content(wisdom, outline, article, custom_prompt=social_prompt, knowledge_base={})
-            results['social_content'] = social
-            
-            # Stream social content to UI immediately
-            with social_container:
-                st.markdown("**✅ Social Content Creation Complete**")
-                st.markdown(social)
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=4, 
-                    step_progress=100, 
-                    total_progress=83,
-                    status_message="Social content creation complete!",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            # Step 6: Auto-publish to Notion
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=5, 
-                    step_progress=0, 
-                    total_progress=83,
-                    status_message="Publishing to Notion workspace...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            if os.getenv("NOTION_API_KEY") and os.getenv("NOTION_DATABASE_ID"):
-                # Generate AI title
-                ai_title = generate_ai_title(transcript)
-                
-                with pipeline_placeholder.container():
-                    show_processing_pipeline(
-                        current_step=5, 
-                        step_progress=30, 
-                        total_progress=88,
-                        status_message="Creating Notion page structure...",
-                        processing_time=f"{time.time() - start_time:.1f}s"
-                    )
-                
-                with pipeline_placeholder.container():
-                    show_processing_pipeline(
-                        current_step=5, 
-                        step_progress=60, 
-                        total_progress=92,
-                        status_message="Uploading content to Notion...",
-                        processing_time=f"{time.time() - start_time:.1f}s"
-                    )
-                
-                # Publish to Notion
-                notion_url = create_notion_page(ai_title, results)
-                if notion_url:
-                    results['notion_url'] = notion_url
-                    
-                    # Stream Notion success to UI
-                    with notion_container:
-                        st.markdown("**✅ Notion Publishing Complete**")
-                        st.markdown(f"**Page Title:** {ai_title}")
-                        st.markdown(f"🔗 [Open in Notion]({notion_url})")
-                else:
-                    # Stream Notion failure to UI
-                    with notion_container:
-                        st.markdown("**⚠️ Notion Publishing Failed**")
-                        st.warning("Check your Notion API configuration in Settings.")
-            else:
-                # Show disabled status in UI
-                with notion_container:
-                    st.markdown("**ℹ️ Notion Publishing Disabled**")
-                    st.info("Configure Notion API in Settings to enable auto-publishing.")
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=5, 
-                    step_progress=90, 
-                    total_progress=96,
-                    status_message="Saving to database...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            # Save to Supabase database
-            try:
-                save_content_to_db(results)
-            except Exception as e:
-                st.warning(f"⚠️ Content saved locally but database save failed: {e}")
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=5, 
-                    step_progress=100, 
-                    total_progress=100,
-                    status_message="Pipeline complete! All content generated successfully.",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            # Aurora completion celebration
-            st.markdown("""
-            <div class="aurora-celebration">
-                <h1 class="aurora-celebration-title">Pipeline Complete!</h1>
-                <p class="aurora-celebration-subtitle">Your content has been transformed with AI magic</p>
-            </div>
-            """, unsafe_allow_html=True)
-            
-            # Clear the pipeline display after a moment
-            time.sleep(2)
-            pipeline_placeholder.empty()
-            
-            return results
-            
-        finally:
-            # Cleanup temporary file
-            if os.path.exists(tmp_file_path):
-                os.unlink(tmp_file_path)
-                
-    except Exception as e:
-        # Show error state
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=0, 
-                step_progress=0, 
-                total_progress=0,
-                status_message=f"Error: {str(e)}",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        st.error(f"Pipeline failed: {str(e)}")
-        return None
-
-def process_audio_pipeline_live(audio_file):
-    """Run pipeline with StreamingPipelineController"""
-    from core.streaming_pipeline import get_pipeline_controller
-
-    controller = get_pipeline_controller()
-    controller.start_pipeline(audio_file)
-    while controller.process_next_step():
-        pass
-    return controller.get_results()
-
-def process_audio_pipeline_with_transcript(transcript: str):
-    """Process audio pipeline with pre-transcribed content using beautiful Aurora visualization"""
-    import time
-    from datetime import datetime
-    
-    results = {'transcript': transcript}
-    start_time = time.time()
-    
-    # Load custom prompts
-    custom_prompts = load_custom_prompts()
-    if custom_prompts:
-        st.info(f"📝 Using {len(custom_prompts)} custom prompts")
-    
-    # Initialize beautiful pipeline visualization (starting from step 1)
-    pipeline_placeholder = st.empty()
-    
-    # Create real-time content display containers
-    st.markdown("### 🌌 Live Content Generation")
-    
-    # Create expandable containers for each step (skip transcription)
-    wisdom_container = st.expander("💡 Wisdom Extraction", expanded=False)
-    outline_container = st.expander("📋 Outline Creation", expanded=False)
-    article_container = st.expander("📝 Article Generation", expanded=False)
-    social_container = st.expander("📱 Social Content", expanded=False)
-    notion_container = st.expander("🌌 Notion Publishing", expanded=False)
-    
-    try:
-        # Show initial state with transcription already complete
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=1, 
-                step_progress=0, 
-                total_progress=17,
-                status_message=f"Using pre-transcribed content ({len(transcript)} characters)",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        # Step 2: Wisdom Extraction
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=1, 
-                step_progress=0, 
-                total_progress=17,
-                status_message="Extracting wisdom and insights...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        from core.content_generation import generate_wisdom
-        wisdom_prompt = get_prompt_for_step('wisdom', custom_prompts)
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=1, 
-                step_progress=50, 
-                total_progress=25,
-                status_message="Analyzing content for key insights...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        wisdom = generate_wisdom(transcript, custom_prompt=wisdom_prompt, knowledge_base={})
-        results['wisdom'] = wisdom
-        
-        # Stream wisdom to UI immediately
-        with wisdom_container:
-            st.markdown("**✅ Wisdom Extraction Complete**")
-            st.markdown(wisdom)
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=1, 
-                step_progress=100, 
-                total_progress=33,
-                status_message="Wisdom extraction complete!",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        # Step 3: Outline Creation
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=2, 
-                step_progress=0, 
-                total_progress=33,
-                status_message="Creating structured outline...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        from core.content_generation import generate_outline
-        outline_prompt = get_prompt_for_step('outline', custom_prompts)
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=2, 
-                step_progress=50, 
-                total_progress=42,
-                status_message="Structuring content hierarchy...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        outline = generate_outline(transcript, wisdom, custom_prompt=outline_prompt, knowledge_base={})
-        results['outline'] = outline
-        
-        # Stream outline to UI immediately
-        with outline_container:
-            st.markdown("**✅ Outline Creation Complete**")
-            st.markdown(outline)
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=2, 
-                step_progress=100, 
-                total_progress=50,
-                status_message="Outline creation complete!",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        # Step 4: Article Generation
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=3, 
-                step_progress=0, 
-                total_progress=50,
-                status_message="Generating comprehensive article...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        from core.content_generation import generate_article
-        article_prompt = get_prompt_for_step('article', custom_prompts)
-        selected_template = st.session_state.get('article_template')
-        if selected_template:
-            template_text = load_template(selected_template)
-            if template_text:
-                article_prompt = template_text + "\n" + article_prompt
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=3, 
-                step_progress=50, 
-                total_progress=58,
-                status_message="Writing detailed article content...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        article = generate_article(transcript, wisdom, outline, custom_prompt=article_prompt, knowledge_base={})
-        results['article'] = article
-        
-        # Stream article to UI immediately
-        with article_container:
-            st.markdown("**✅ Article Generation Complete**")
-            st.markdown(article)
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=3, 
-                step_progress=100, 
-                total_progress=67,
-                status_message="Article generation complete!",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        # Step 5: Social Content
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=4, 
-                step_progress=0, 
-                total_progress=67,
-                status_message="Creating social media content...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        from core.content_generation import generate_social_content
-        social_prompt = get_prompt_for_step('social', custom_prompts)
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=4, 
-                step_progress=50, 
-                total_progress=75,
-                status_message="Generating social media posts...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        social = generate_social_content(wisdom, outline, article, custom_prompt=social_prompt, knowledge_base={})
-        results['social_content'] = social
-        
-        # Stream social content to UI immediately
-        with social_container:
-            st.markdown("**✅ Social Content Creation Complete**")
-            st.markdown(social)
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=4, 
-                step_progress=100, 
-                total_progress=83,
-                status_message="Social content creation complete!",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        # Step 6: Auto-publish to Notion
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=5, 
-                step_progress=0, 
-                total_progress=83,
-                status_message="Publishing to Notion workspace...",
-                processing_time=f"{time.time() - start_time:.1f}s"
+
+    badge_spans = []
+    results = st.session_state.get("current_results", {})
+
+    for key, icon, label in badge_data:
+        has_content = bool(results.get(key))
+        state_class = "badge-complete" if has_content else "badge-pending"
+
+        if has_content:
+            word_count = len(str(results[key]).split())
+            badge_spans.append(
+                '<span class="pipeline-badge '
+                + state_class
+                + '" title="'
+                + str(word_count)
+                + ' words">'
+                + icon
+                + " "
+                + label
+                + "</span>"
             )
-        
-        if os.getenv("NOTION_API_KEY") and os.getenv("NOTION_DATABASE_ID"):
-            # Generate AI title
-            ai_title = generate_ai_title(transcript)
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=5, 
-                    step_progress=30, 
-                    total_progress=88,
-                    status_message="Creating Notion page structure...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            with pipeline_placeholder.container():
-                show_processing_pipeline(
-                    current_step=5, 
-                    step_progress=60, 
-                    total_progress=92,
-                    status_message="Uploading content to Notion...",
-                    processing_time=f"{time.time() - start_time:.1f}s"
-                )
-            
-            # Publish to Notion
-            notion_url = create_notion_page(ai_title, results)
-            if notion_url:
-                results['notion_url'] = notion_url
-                
-                # Stream Notion success to UI
-                with notion_container:
-                    st.markdown("**✅ Notion Publishing Complete**")
-                    st.markdown(f"**Page Title:** {ai_title}")
-                    st.markdown(f"🔗 [Open in Notion]({notion_url})")
-            else:
-                # Stream Notion failure to UI
-                with notion_container:
-                    st.markdown("**⚠️ Notion Publishing Failed**")
-                    st.warning("Check your Notion API configuration in Settings.")
         else:
-            # Show disabled status in UI
-            with notion_container:
-                st.markdown("**ℹ️ Notion Publishing Disabled**")
-                st.info("Configure Notion API in Settings to enable auto-publishing.")
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=5, 
-                step_progress=90, 
-                total_progress=96,
-                status_message="Saving to database...",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        # Save to Supabase database
-        try:
-            save_content_to_db(results)
-        except Exception as e:
-            st.warning(f"⚠️ Content saved locally but database save failed: {e}")
-        
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=5, 
-                step_progress=100, 
-                total_progress=100,
-                status_message="Pipeline complete! All content generated successfully.",
-                processing_time=f"{time.time() - start_time:.1f}s"
-            )
-        
-        # Aurora completion celebration
-        st.markdown("""
-        <div class="aurora-celebration">
-            <h1 class="aurora-celebration-title">Pipeline Complete!</h1>
-            <p class="aurora-celebration-subtitle">Your content has been transformed with AI magic</p>
-        </div>
-        """, unsafe_allow_html=True)
-        
-        # Clear the pipeline display after a moment
-        time.sleep(2)
-        pipeline_placeholder.empty()
-        
-        return results
-        
-    except Exception as e:
-        # Show error state
-        with pipeline_placeholder.container():
-            show_processing_pipeline(
-                current_step=1, 
-                step_progress=0, 
-                total_progress=17,
-                status_message=f"Error: {str(e)}",
-                processing_time=f"{time.time() - start_time:.1f}s"
+            badge_spans.append(
+                '<span class="pipeline-badge '
+                + state_class
+                + '" title="No results yet">'
+                + icon
+                + " "
+                + label
+                + "</span>"
             )
-        st.error(f"Pipeline failed: {str(e)}")
-        return None
 
-def save_content_to_db(content_data):
-    """Save generated content to database"""
-    try:
-        db = get_supabase_client()
-        if db and st.session_state.user_id:
-            content_id = db.save_content(st.session_state.user_id, {
-                **content_data,
-                'title': f"WhisperForge Content {datetime.now().strftime('%Y-%m-%d %H:%M')}",
-                'created_at': datetime.now().isoformat()
-            })
-            if content_id:
-                st.success(f"💾 Content saved to database (ID: {content_id})")
-    except Exception as e:
-        st.warning(f"Database save failed: {e}")
-
-# === CONTENT DISPLAY ===
-def create_enhanced_aurora_content_card(title, content, content_type="text", icon="📄"):
-    """Create a beautiful enhanced Aurora content card with copy functionality and animations"""
-    import uuid
-    
-    # Generate unique IDs for this card
-    card_id = f"card_{uuid.uuid4().hex[:8]}"
-    copy_btn_id = f"copy_{uuid.uuid4().hex[:8]}"
-    expand_btn_id = f"expand_{uuid.uuid4().hex[:8]}"
-    full_content_id = f"full_{uuid.uuid4().hex[:8]}"
-    
-    # Calculate content stats
-    word_count = len(content.split()) if content else 0
-    char_count = len(content) if content else 0
-    
-    # Determine if content needs truncation
-    preview_length = 300
-    needs_expansion = len(content) > preview_length
-    preview_content = content[:preview_length] + "..." if needs_expansion else content
-    
-    # Content type specific styling
-    type_class = content_type.lower()
-    
-    # Create the enhanced card HTML
-    safe_content = content.replace('`', '\\`')
-
-    card_html = f"""
-    <div class="aurora-content-card {type_class}" id="{card_id}">
-        <div class="aurora-content-card-header">
-            <h3 class="aurora-content-card-title">
-                <span class="aurora-content-card-icon">{icon}</span>
-                {title}
-            </h3>
-            <div class="aurora-content-card-actions">
-                <button class="aurora-content-action-btn copy-btn" id="{copy_btn_id}" onclick="copyToClipboard('{card_id}', '{copy_btn_id}')">
-                    <span>📋</span>
-                    <span class="copy-text">Copy</span>
-                </button>
-                <button class="aurora-content-action-btn" onclick="downloadContent('{title}', `{safe_content}`)">
-                    <span>💾</span>
-                    <span>Save</span>
-                </button>
-            </div>
-        </div>
-        
-        <div class="aurora-content-card-body">
-            <div class="aurora-content-preview" id="preview_{card_id}">
-                {preview_content}
-            </div>
-            
-            {f'''
-            <div class="aurora-content-full" id="{full_content_id}">
-                {content[preview_length:]}
-            </div>
-            ''' if needs_expansion else ''}
-            
-            <div class="aurora-content-stats">
-                <div class="aurora-content-word-count">
-                    <span>📊</span>
-                    <span>{word_count} words • {char_count} characters</span>
-                </div>
-                
-                {f'''
-                <button class="aurora-content-expand-btn" id="{expand_btn_id}" onclick="toggleExpand('{full_content_id}', '{expand_btn_id}')">
-                    <span>Show more</span>
-                    <span class="aurora-content-expand-icon">▼</span>
-                </button>
-                ''' if needs_expansion else ''}
-            </div>
-        </div>
-        
-        <!-- Hidden content for copying -->
-        <textarea id="content_{card_id}" style="position: absolute; left: -9999px; opacity: 0;">{content}</textarea>
-    </div>
-    
-    <script>
-    function copyToClipboard(cardId, btnId) {{
-        const content = document.getElementById('content_' + cardId).value;
-        const button = document.getElementById(btnId);
-        const copyText = button.querySelector('.copy-text');
-        
-        navigator.clipboard.writeText(content).then(function() {{
-            // Success feedback
-            button.classList.add('copied');
-            copyText.textContent = 'Copied!';
-            
-            // Reset after 2 seconds
-            setTimeout(() => {{
-                button.classList.remove('copied');
-                copyText.textContent = 'Copy';
-            }}, 2000);
-        }}).catch(function(err) {{
-            console.error('Failed to copy: ', err);
-            // Fallback for older browsers
-            const textarea = document.getElementById('content_' + cardId);
-            textarea.select();
-            document.execCommand('copy');
-            
-            button.classList.add('copied');
-            copyText.textContent = 'Copied!';
-            setTimeout(() => {{
-                button.classList.remove('copied');
-                copyText.textContent = 'Copy';
-            }}, 2000);
-        }});
-    }}
-    
-    function toggleExpand(fullContentId, btnId) {{
-        const fullContent = document.getElementById(fullContentId);
-        const button = document.getElementById(btnId);
-        const buttonText = button.querySelector('span:first-child');
-        const icon = button.querySelector('.aurora-content-expand-icon');
-        
-        if (fullContent.classList.contains('expanded')) {{
-            fullContent.classList.remove('expanded');
-            button.classList.remove('expanded');
-            buttonText.textContent = 'Show more';
-        }} else {{
-            fullContent.classList.add('expanded');
-            button.classList.add('expanded');
-            buttonText.textContent = 'Show less';
-        }}
-    }}
-    
-    function downloadContent(title, content) {{
-        const blob = new Blob([content], {{ type: 'text/plain' }});
-        const url = window.URL.createObjectURL(blob);
-        const a = document.createElement('a');
-        a.href = url;
-        a.download = title.replace(/[^a-z0-9]/gi, '_').toLowerCase() + '.txt';
-        document.body.appendChild(a);
-        a.click();
-        document.body.removeChild(a);
-        window.URL.revokeObjectURL(url);
-    }}
-    </script>
-    """
-    
-    st.markdown(card_html, unsafe_allow_html=True)
-
-def show_results(results):
-    """Display generated content with beautiful Aurora styling and enhanced UX"""
-    if not results:
-        return
-    
-    # Aurora header for results with enhanced styling
-    st.markdown("""
-    <div class="aurora-results-header">
-        <h1 class="aurora-results-title">✨ Content Generated Successfully!</h1>
-        <p class="aurora-results-subtitle">Your audio has been transformed with AI magic</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Aurora Notion link if available with enhanced styling
-    if results.get('notion_url'):
-        st.markdown(f"""
-        <div class="aurora-notion-link">
-            <a href="{results['notion_url']}" target="_blank" class="aurora-notion-button">
-                🌌 View in Notion Workspace
-            </a>
-        </div>
-        """, unsafe_allow_html=True)
-        st.markdown("---")
-    
-    # Enhanced content overview stats
-    total_words = sum(len(str(results.get(key, '')).split()) for key in ['transcript', 'wisdom', 'outline', 'article', 'social_content'])
-    content_types = len([k for k in ['transcript', 'wisdom', 'outline', 'article', 'social_content'] if results.get(k)])
-    
-    st.markdown(f"""
-    <div class="aurora-content-overview">
-        <div class="aurora-overview-stats">
-            <div class="aurora-stat-item">
-                <span class="aurora-stat-icon">📊</span>
-                <span class="aurora-stat-value">{total_words:,}</span>
-                <span class="aurora-stat-label">Total Words</span>
-            </div>
-            <div class="aurora-stat-item">
-                <span class="aurora-stat-icon">📄</span>
-                <span class="aurora-stat-value">{content_types}</span>
-                <span class="aurora-stat-label">Content Types</span>
-            </div>
-            <div class="aurora-stat-item">
-                <span class="aurora-stat-icon">⚡</span>
-                <span class="aurora-stat-value">AI</span>
-                <span class="aurora-stat-label">Generated</span>
-            </div>
-        </div>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Prepare tab data for custom Aurora tabs
-    tab_data = []
-    
-    # Add main content tabs
-    if results.get('transcript'):
-        tab_data.append({
-            'title': 'Transcript',
-            'icon': '🎙️',
-            'type': 'transcript',
-            'content': results['transcript']
-        })
-    
-    if results.get('wisdom'):
-        tab_data.append({
-            'title': 'Wisdom',
-            'icon': '💎',
-            'type': 'wisdom',
-            'content': results['wisdom']
-        })
-    
-    if results.get('outline'):
-        tab_data.append({
-            'title': 'Outline',
-            'icon': '📋',
-            'type': 'outline',
-            'content': results['outline']
-        })
-    
-    if results.get('article'):
-        tab_data.append({
-            'title': 'Article',
-            'icon': '📰',
-            'type': 'article',
-            'content': results['article']
-        })
-    
-    if results.get('social_content'):
-        tab_data.append({
-            'title': 'Social',
-            'icon': '📱',
-            'type': 'social',
-            'content': results['social_content']
-        })
-    
-    # Add Editor tab if editor content exists
-    if results.get('editor_notes') or results.get('revised_content'):
-        editor_content = ""
-        
-        if results.get('editor_notes'):
-            editor_content += "=== EDITOR NOTES ===\n\n"
-            for section, notes in results['editor_notes'].items():
-                if notes:
-                    editor_content += f"## {section.title()} Notes:\n{notes}\n\n"
-        
-        if results.get('revised_content'):
-            editor_content += "\n=== REVISED CONTENT ===\n\n"
-            for section, content in results['revised_content'].items():
-                if content:
-                    editor_content += f"## Revised {section.title()}:\n{content}\n\n"
-        
-        tab_data.append({
-            'title': 'Editor Review',
-            'icon': '📝',
-            'type': 'editor',
-            'content': editor_content
-        })
-    
-    # Display the custom Aurora tabs
-    if tab_data:
-        create_aurora_tabs(tab_data, default_tab=0)
-    else:
-        st.warning("No content available to display.")
-    
-    # Add export all functionality
-    st.markdown("---")
-    st.markdown("""
-    <div class="aurora-export-section">
-        <h3 class="aurora-export-title">📦 Additional Export Options</h3>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        if st.button("📄 Export as Text", use_container_width=True):
-            export_content = create_text_export(results)
-            st.download_button(
-                label="💾 Download Text File",
-                data=export_content,
-                file_name=f"whisperforge_content_{datetime.now().strftime('%Y%m%d_%H%M')}.txt",
-                mime="text/plain",
-                use_container_width=True
-            )
-        if st.button("📝 Export as Markdown", use_container_width=True):
-            md_content = export_to_markdown(results)
-            st.download_button(
-                label="💾 Download Markdown",
-                data=md_content,
-                file_name=f"whisperforge_{datetime.now().strftime('%Y%m%d_%H%M')}.md",
-                mime="text/markdown",
-                use_container_width=True
-            )
+    badges_html = " ".join(badge_spans)
 
-    with col2:
-        if st.button("📊 Export as JSON", use_container_width=True):
-            import json
-            json_content = json.dumps(results, indent=2, ensure_ascii=False)
-            st.download_button(
-                label="💾 Download JSON File",
-                data=json_content,
-                file_name=f"whisperforge_data_{datetime.now().strftime('%Y%m%d_%H%M')}.json",
-                mime="application/json",
-                use_container_width=True
-            )
-        if st.button("📑 Export as Word", use_container_width=True):
-            doc_bytes = export_to_word(results)
-            st.download_button(
-                label="💾 Download Word File",
-                data=doc_bytes,
-                file_name=f"whisperforge_{datetime.now().strftime('%Y%m%d_%H%M')}.docx",
-                mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-                use_container_width=True
-            )
+    header_html = (
+        '<div class="aurora-header">'
+        '<div class="aurora-glow"></div>'
+        '<h1 class="aurora-title">WhisperForge Aurora</h1>'
+        '<p class="aurora-subtitle">Transform Audio into Structured Content with AI</p>'
+        '<div class="aurora-pipeline">' + badges_html + "</div>"
+        "</div>"
+    )
+    st.markdown(header_html, unsafe_allow_html=True)
 
-    with col3:
-        if st.button("📋 Copy All Content", use_container_width=True):
-            all_content = create_text_export(results)
-            st.code(all_content, language="text")
-            st.success("✅ Content displayed above - use your browser's copy function!")
-        if st.button("📕 Export as PDF", use_container_width=True):
-            pdf_bytes = export_to_pdf(results)
-            st.download_button(
-                label="💾 Download PDF",
-                data=pdf_bytes,
-                file_name=f"whisperforge_{datetime.now().strftime('%Y%m%d_%H%M')}.pdf",
-                mime="application/pdf",
-                use_container_width=True
-            )
+    return st.tabs(["Transform", "Content Library", "Settings", "Knowledge Base", "Prompts"])
+
+
+# === PAGE FUNCTIONS ===
 
-def create_text_export(results):
-    """Create a formatted text export of all content"""
-    export_lines = []
-    export_lines.append("=" * 60)
-    export_lines.append("WHISPERFORGE CONTENT EXPORT")
-    export_lines.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-    export_lines.append("=" * 60)
-    export_lines.append("")
-    
-    sections = [
-        ("AUDIO TRANSCRIPT", results.get('transcript', '')),
-        ("EXTRACTED WISDOM", results.get('wisdom', '')),
-        ("CONTENT OUTLINE", results.get('outline', '')),
-        ("FULL ARTICLE", results.get('article', '')),
-        ("SOCIAL MEDIA CONTENT", results.get('social_content', ''))
-    ]
-    
-    for title, content in sections:
-        if content:
-            export_lines.append(f"## {title}")
-            export_lines.append("-" * 40)
-            export_lines.append(content)
-            export_lines.append("")
-            export_lines.append("")
-    
-    if results.get('notion_url'):
-        export_lines.append("## NOTION LINK")
-        export_lines.append("-" * 40)
-        export_lines.append(results['notion_url'])
-        export_lines.append("")
-
-    return "\n".join(export_lines)
-
-def export_to_markdown(results):
-    """Export results to Markdown format"""
-    lines = ["# WhisperForge Content Export"]
-    sections = [
-        ("## Transcript", results.get('transcript', '')),
-        ("## Wisdom", results.get('wisdom', '')),
-        ("## Outline", results.get('outline', '')),
-        ("## Article", results.get('article', '')),
-        ("## Social Content", results.get('social_content', '')),
-    ]
-    for title, content in sections:
-        if content:
-            lines.append(title)
-            lines.append("")
-            lines.append(content)
-            lines.append("")
-    if results.get('notion_url'):
-        lines.append("## Notion Link")
-        lines.append(results['notion_url'])
-    return "\n".join(lines)
-
-def export_to_word(results):
-    """Export results to a Word document"""
-    from docx import Document
-    from io import BytesIO
-
-    doc = Document()
-    doc.add_heading("WhisperForge Content Export", level=1)
-    for title, content in [
-        ("Transcript", results.get('transcript', '')),
-        ("Wisdom", results.get('wisdom', '')),
-        ("Outline", results.get('outline', '')),
-        ("Article", results.get('article', '')),
-        ("Social Content", results.get('social_content', '')),
-    ]:
-        if content:
-            doc.add_heading(title, level=2)
-            doc.add_paragraph(content)
-    if results.get('notion_url'):
-        doc.add_heading("Notion Link", level=2)
-        doc.add_paragraph(results['notion_url'])
-
-    bio = BytesIO()
-    doc.save(bio)
-    bio.seek(0)
-    return bio.read()
-
-def export_to_pdf(results):
-    """Export results to a PDF file"""
-    from fpdf import FPDF
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_auto_page_break(auto=True, margin=15)
-    pdf.set_font("Arial", size=12)
-    text = create_text_export(results)
-    for line in text.split("\n"):
-        pdf.cell(0, 10, txt=line, ln=1)
-    return pdf.output(dest='S').encode('latin-1')
-
-# === NAVIGATION & PAGES ===
-def create_aurora_navigation():
-    """Beautiful Aurora bioluminescent navigation - Clean and professional"""
-    st.markdown("""
-    <div class="aurora-header">
-        <div class="aurora-glow"></div>
-        <h1 class="aurora-title">WhisperForge Aurora</h1>
-        <p class="aurora-subtitle">Transform Audio into Structured Content with AI</p>
-        <div class="aurora-pipeline">
-            <span class="pipeline-badge">Transcription</span>
-            <span class="pipeline-badge">Wisdom</span>
-            <span class="pipeline-badge">Article</span>
-            <span class="pipeline-badge">Social</span>
-            <span class="pipeline-badge">Notion</span>
-        </div>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Clean navigation tabs without emojis
-    tabs = st.tabs([
-        "Transform", 
-        "Content Library", 
-        "Settings", 
-        "Knowledge Base",
-        "Prompts"
-    ])
-    
-    return tabs
 
 def show_transform_page():
-    """Clean transformation page focused on file upload and processing"""
-    
-    # Simple Aurora-styled header using main CSS
-    st.markdown("""
+    """Main transformation page: file upload and processing."""
+    st.markdown(
+        """
     <div class="transform-header">
         <h2 class="transform-title">Transform Audio</h2>
         <p class="transform-subtitle">Upload your audio and watch it transform into structured content</p>
     </div>
-    """, unsafe_allow_html=True)
-    
-    # Beautiful Aurora upload method selector
-    st.markdown("""
+    """,
+        unsafe_allow_html=True,
+    )
+
+    if "upload_method" not in st.session_state:
+        st.session_state.upload_method = "Standard Upload"
+
+    # Upload method cards
+    sel_std = "selected" if st.session_state.upload_method == "Standard Upload" else ""
+    sel_lg = "selected" if st.session_state.upload_method == "Large File Upload" else ""
+    st.markdown(
+        f"""
     <div class="aurora-upload-method-selector">
-        <div class="aurora-upload-method-card" id="standard-upload">
-            <span class="aurora-upload-method-icon">⚡</span>
+        <div class="aurora-upload-method-card {sel_std}">
+            <span class="aurora-upload-method-icon">\u26a1</span>
             <h3 class="aurora-upload-method-title">Standard Upload</h3>
-            <p class="aurora-upload-method-description">Perfect for most audio files up to 25MB with instant processing</p>
+            <p class="aurora-upload-method-description">
+                Perfect for most audio files up to 25MB with instant processing</p>
             <div class="aurora-upload-method-features">
                 <div class="aurora-upload-feature">
-                    <span class="aurora-upload-feature-icon">⚡</span>
-                    <span>Instant processing</span>
-                </div>
+                    <span class="aurora-upload-feature-icon">\u26a1</span> Instant processing</div>
                 <div class="aurora-upload-feature">
-                    <span class="aurora-upload-feature-icon">🎵</span>
-                    <span>Audio preview</span>
-                </div>
+                    <span class="aurora-upload-feature-icon">\U0001f3b5</span> Audio preview</div>
                 <div class="aurora-upload-feature">
-                    <span class="aurora-upload-feature-icon">📊</span>
-                    <span>Up to 25MB</span>
-                </div>
+                    <span class="aurora-upload-feature-icon">\U0001f4be</span> Up to 25MB</div>
             </div>
         </div>
-        <div class="aurora-upload-method-card" id="large-upload">
-            <span class="aurora-upload-method-icon">🚀</span>
+        <div class="aurora-upload-method-card {sel_lg}">
+            <span class="aurora-upload-method-icon">\U0001f680</span>
             <h3 class="aurora-upload-method-title">Large File Upload</h3>
-            <p class="aurora-upload-method-description">Advanced processing for large files up to 2GB with intelligent chunking</p>
+            <p class="aurora-upload-method-description">
+                Advanced processing for large files up to 2GB with intelligent chunking</p>
             <div class="aurora-upload-method-features">
                 <div class="aurora-upload-feature">
-                    <span class="aurora-upload-feature-icon">🔧</span>
-                    <span>FFmpeg chunking</span>
-                </div>
+                    <span class="aurora-upload-feature-icon">\U0001f527</span> FFmpeg chunking</div>
                 <div class="aurora-upload-feature">
-                    <span class="aurora-upload-feature-icon">⚡</span>
-                    <span>Parallel processing</span>
-                </div>
+                    <span class="aurora-upload-feature-icon">\u26a1</span> Parallel processing</div>
                 <div class="aurora-upload-feature">
-                    <span class="aurora-upload-feature-icon">📈</span>
-                    <span>Up to 2GB</span>
-                </div>
+                    <span class="aurora-upload-feature-icon">\U0001f4be</span> Up to 2GB</div>
             </div>
         </div>
     </div>
-    
-    <script>
-    document.addEventListener('DOMContentLoaded', function() {
-        // Add click handlers to upload method cards
-        const standardCard = document.getElementById('standard-upload');
-        const largeCard = document.getElementById('large-upload');
-        
-        if (standardCard && largeCard) {
-            // Initialize with first option selected
-            standardCard.classList.add('selected');
-            
-            standardCard.addEventListener('click', function() {
-                // Remove selected class from both cards
-                standardCard.classList.remove('selected');
-                largeCard.classList.remove('selected');
-                
-                // Add selected class to clicked card
-                standardCard.classList.add('selected');
-                
-                // Find and trigger the radio button
-                setTimeout(() => {
-                    const radioButtons = document.querySelectorAll('input[type="radio"]');
-                    radioButtons.forEach(radio => {
-                        if (radio.nextElementSibling && radio.nextElementSibling.textContent.includes('Standard Upload')) {
-                            radio.checked = true;
-                            radio.dispatchEvent(new Event('change', { bubbles: true }));
-                        }
-                    });
-                }, 100);
-            });
-            
-            largeCard.addEventListener('click', function() {
-                // Remove selected class from both cards
-                standardCard.classList.remove('selected');
-                largeCard.classList.remove('selected');
-                
-                // Add selected class to clicked card
-                largeCard.classList.add('selected');
-                
-                // Find and trigger the radio button
-                setTimeout(() => {
-                    const radioButtons = document.querySelectorAll('input[type="radio"]');
-                    radioButtons.forEach(radio => {
-                        if (radio.nextElementSibling && radio.nextElementSibling.textContent.includes('Large File Upload')) {
-                            radio.checked = true;
-                            radio.dispatchEvent(new Event('change', { bubbles: true }));
-                        }
-                    });
-                }, 100);
-            });
-        }
-    });
-    </script>
-    """, unsafe_allow_html=True)
-    
-    # Enhanced file upload selection with session state
-    if 'upload_method' not in st.session_state:
-        st.session_state.upload_method = "Standard Upload"
-    
+    """,
+        unsafe_allow_html=True,
+    )
+
     upload_method = st.radio(
         "Choose upload method:",
         ["Standard Upload", "Large File Upload"],
         index=0 if st.session_state.upload_method == "Standard Upload" else 1,
         help="Standard upload for smaller files, Enhanced upload for large files with FFmpeg processing",
-        label_visibility="collapsed"
+        label_visibility="collapsed",
     )
-    
     st.session_state.upload_method = upload_method
-    
+
     if upload_method == "Standard Upload":
-        # Beautiful standard file upload zone
-        st.markdown("""
-        <div class="aurora-file-upload-zone">
-            <div class="aurora-upload-icon-container">
-                <div class="aurora-upload-icon">🎵</div>
-                <div class="aurora-upload-pulse"></div>
-            </div>
-            <h3 class="aurora-upload-title">Drop your audio file here</h3>
-            <p class="aurora-upload-subtitle">Or click to browse and select a file</p>
-            <div class="aurora-upload-formats">
-                <span class="aurora-format-badge">MP3</span>
-                <span class="aurora-format-badge">WAV</span>
-                <span class="aurora-format-badge">M4A</span>
-                <span class="aurora-format-badge">FLAC</span>
-                <span class="aurora-format-badge">OGG</span>
-            </div>
-        </div>
-        """, unsafe_allow_html=True)
-        
-        # Standard file upload
-        uploaded_files = st.file_uploader(
-            "Upload your audio file",
-            type=['mp3', 'wav', 'm4a', 'flac', 'ogg'],
-            help="Upload audio file for processing (max 25MB)",
-            label_visibility="collapsed",
-            accept_multiple_files=True
-        )
-
-        if uploaded_files:
-            for uploaded_file in uploaded_files:
-                # Beautiful file preview card
-                file_size = len(uploaded_file.getvalue()) / (1024 * 1024)
-                file_extension = uploaded_file.name.split('.')[-1].upper()
-            
-            st.markdown(f"""
-            <div class="aurora-file-preview">
-                <div class="aurora-file-preview-header">
-                    <div class="aurora-file-info">
-                        <div class="aurora-file-icon">🎵</div>
-                        <div class="aurora-file-details">
-                            <h4>{uploaded_file.name}</h4>
-                            <p>{file_size:.1f} MB • {file_extension} Format</p>
-                        </div>
-                    </div>
-                    <div class="aurora-file-actions">
-                        <div class="aurora-file-action-btn">Ready to process</div>
-                    </div>
-                </div>
-            </div>
-            """, unsafe_allow_html=True)
-            
-                # Enhanced audio player
-                if file_size < 50:  # Only show player for files under 50MB
-                    st.markdown('<div class="aurora-audio-player">', unsafe_allow_html=True)
-                    st.audio(uploaded_file.getvalue())
-                    st.markdown('</div>', unsafe_allow_html=True)
-                else:
-                    st.info("Audio preview disabled for large files to conserve memory")
-
-                # Beautiful process button
-                if st.button(f"Transform {uploaded_file.name}", key=f"process_{uploaded_file.name}", type="primary", use_container_width=True):
-                    if not os.getenv("OPENAI_API_KEY"):
-                        st.error("Please enter your OpenAI API key in the sidebar")
-                        return
-
-                    with st.container():
-                        if st.session_state.get('live_stream', False):
-                            results = process_audio_pipeline_live(uploaded_file)
-                        else:
-                            results = process_audio_pipeline(uploaded_file)
-                        if results:
-                            st.session_state.current_results = results
-    
+        _show_standard_upload()
     else:
-        # Enhanced large file upload
-        st.markdown("### Enhanced Large File Processing")
-        
-        # Initialize enhanced processor
-        processor = EnhancedLargeFileProcessor()
-        
-        # Create enhanced upload interface
-        uploaded_file = processor.create_enhanced_upload_interface()
-        
-        if uploaded_file:
-            # Validate file
-            validation = processor.validate_file(uploaded_file)
-            
-            if not validation["valid"]:
-                st.error(f"{validation['error']}")
-                return
-            
-            file_size_mb = validation["size_mb"]
-            requires_chunking = validation["requires_chunking"]
-            
-            # Beautiful processing metrics
-            st.markdown(f"""
-            <div class="aurora-processing-metrics">
-                <div class="aurora-metric-card">
-                    <div class="aurora-metric-value">{file_size_mb:.1f}</div>
-                    <div class="aurora-metric-label">File Size (MB)</div>
-                </div>
-                <div class="aurora-metric-card">
-                    <div class="aurora-metric-value">{"FFmpeg" if requires_chunking else "Standard"}</div>
-                    <div class="aurora-metric-label">Processing Method</div>
-                </div>
-                <div class="aurora-metric-card">
-                    <div class="aurora-metric-value">{validation["format"].upper()}</div>
-                    <div class="aurora-metric-label">Format</div>
-                </div>
-            </div>
-            """, unsafe_allow_html=True)
-            
-            # Audio preview disabled for large files to conserve memory
-            if file_size_mb < 50:
-                st.markdown('<div class="aurora-audio-player">', unsafe_allow_html=True)
+        _show_large_file_upload()
+
+    if "current_results" in st.session_state:
+        show_results(st.session_state.current_results)
+
+
+def _show_standard_upload():
+    """Handle standard file upload (up to 25MB)."""
+    st.markdown(
+        """
+    <div class="aurora-file-upload-zone">
+        <div class="aurora-upload-icon-container">
+            <span class="aurora-upload-icon">\U0001f3b5</span>
+            <div class="aurora-upload-pulse"></div>
+        </div>
+        <h3 class="aurora-upload-title">Drop your audio file here</h3>
+        <p class="aurora-upload-subtitle">Or click to browse and select a file</p>
+        <div class="aurora-upload-formats">
+            <span class="aurora-format-badge">MP3</span>
+            <span class="aurora-format-badge">WAV</span>
+            <span class="aurora-format-badge">M4A</span>
+            <span class="aurora-format-badge">FLAC</span>
+            <span class="aurora-format-badge">OGG</span>
+        </div>
+    </div>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    uploaded_files = st.file_uploader(
+        "Upload your audio file",
+        type=["mp3", "wav", "m4a", "flac", "ogg"],
+        help="Upload audio file for processing (max 25MB)",
+        label_visibility="collapsed",
+        accept_multiple_files=True,
+    )
+
+    if uploaded_files:
+        for uploaded_file in uploaded_files:
+            file_size = len(uploaded_file.getvalue()) / (1024 * 1024)
+
+            if file_size < 50:
                 st.audio(uploaded_file.getvalue())
-                st.markdown('</div>', unsafe_allow_html=True)
-            else:
-                st.info("Audio preview disabled for large files to conserve memory")
-            
-            # Enhanced process button
-            if st.button("Transform Large File to Content", type="primary", use_container_width=True):
+
+            if st.button(
+                f"Transform {uploaded_file.name}",
+                key=f"process_{uploaded_file.name}",
+                type="primary",
+                use_container_width=True,
+            ):
                 if not os.getenv("OPENAI_API_KEY"):
-                    st.error("Please enter your OpenAI API key in the sidebar")
+                    st.error("Please enter your OpenAI API key in Settings")
                     return
-                
+
                 with st.container():
-                    # Process with enhanced large file processor
-                    processing_result = processor.process_large_file(uploaded_file)
-                    
-                    if processing_result["success"]:
-                        transcript = processing_result["transcript"]
-                        
-                        # Show processing summary
-                        st.success(f"Large file processing complete!")
-                        
-                        # Beautiful success metrics
-                        st.markdown(f"""
-                        <div class="aurora-processing-metrics">
-                            <div class="aurora-metric-card">
-                                <div class="aurora-metric-value">{len(transcript):,}</div>
-                                <div class="aurora-metric-label">Characters</div>
-                            </div>
-                            <div class="aurora-metric-card">
-                                <div class="aurora-metric-value">{processing_result["method"]}</div>
-                                <div class="aurora-metric-label">Method Used</div>
-                            </div>
-                            <div class="aurora-metric-card">
-                                <div class="aurora-metric-value">{processing_result.get("chunks_processed", 1)}</div>
-                                <div class="aurora-metric-label">Chunks Processed</div>
-                            </div>
-                        </div>
-                        """, unsafe_allow_html=True)
-                        
-                        # Continue with pipeline using pre-transcribed content
-                        st.markdown("---")
-                        results = process_audio_pipeline_with_transcript(transcript)
-                        
-                        if results:
-                            # Store results in session state
-                            st.session_state.current_results = results
-                    else:
-                        st.error(f"Large file processing failed: {processing_result['error']}")
-                        
-                        # Fallback to standard processing for smaller files
-                        if file_size_mb < 100:
-                            st.info("Attempting fallback to standard processing...")
-                            try:
-                                if st.session_state.get('live_stream', False):
-                                    results = process_audio_pipeline_live(uploaded_file)
-                                else:
-                                    results = process_audio_pipeline(uploaded_file)
-                                if results:
-                                    st.session_state.current_results = results
-                            except Exception as e:
-                                st.error(f"Fallback processing also failed: {str(e)}")
-    
-    # Show results if available
-    if 'current_results' in st.session_state:
-        show_results(st.session_state.current_results)
+                    results = process_pipeline(audio_file=uploaded_file)
+                    if results:
+                        st.session_state.current_results = results
+
+
+def _show_large_file_upload():
+    """Handle large file upload with FFmpeg chunking."""
+    st.markdown("### Enhanced Large File Processing")
+    processor = EnhancedLargeFileProcessor()
+    uploaded_file = processor.create_enhanced_upload_interface()
+
+    if uploaded_file:
+        validation = processor.validate_file(uploaded_file)
+        if not validation["valid"]:
+            st.error(f"{validation['error']}")
+            return
+
+        file_size_mb = validation["size_mb"]
+
+        if file_size_mb < 50:
+            st.audio(uploaded_file.getvalue())
+
+        if st.button("Transform Large File to Content", type="primary", use_container_width=True):
+            if not os.getenv("OPENAI_API_KEY"):
+                st.error("Please enter your OpenAI API key in Settings")
+                return
+
+            with st.container():
+                processing_result = processor.process_large_file(uploaded_file)
+
+                if processing_result["success"]:
+                    transcript = processing_result["transcript"]
+                    st.success("Large file processing complete!")
+                    st.markdown("---")
+                    results = process_pipeline(transcript=transcript)
+                    if results:
+                        st.session_state.current_results = results
+                else:
+                    st.error(f"Large file processing failed: {processing_result['error']}")
+
+                    if file_size_mb < 100:
+                        st.info("Attempting fallback to standard processing...")
+                        try:
+                            results = process_pipeline(audio_file=uploaded_file)
+                            if results:
+                                st.session_state.current_results = results
+                        except Exception as e:
+                            st.error(f"Fallback processing also failed: {e!s}")
+
 
 def show_content_library():
-    """Content library/history page"""
-    st.markdown("### 📚 Content Library")
-    
-    # Get content from database
+    """Content library/history page."""
+    st.markdown("### Content Library")
+
     try:
         db = get_supabase_client()
         if db:
-            # Fetch recent content
-            response = db.client.table('content').select('*').order('created_at', desc=True).limit(20).execute()
-            
+            response = db.client.table("content").select("*").order("created_at", desc=True).limit(20).execute()
+
             if response.data:
-                st.success(f"📊 Found {len(response.data)} content items")
-                
-                # Search and filter
+                st.success(f"Found {len(response.data)} content items")
+
                 col1, col2 = st.columns([3, 1])
                 with col1:
-                    search_term = st.text_input("🔍 Search content", placeholder="Search by title or content...")
+                    search_term = st.text_input("Search content", placeholder="Search by title or content...")
                 with col2:
-                    content_type = st.selectbox("Filter by type", ["All", "Article", "Social", "Outline"])
-                
-                # Display content cards in a two-column grid
+                    st.selectbox("Filter by type", ["All", "Article", "Social", "Outline"])
+
                 cols = st.columns(2)
                 for idx, item in enumerate(response.data):
-                    if search_term and search_term.lower() not in item.get('title', '').lower():
+                    if search_term and search_term.lower() not in item.get("title", "").lower():
                         continue
 
-                    with cols[idx % 2].expander(f"📄 {item.get('title', 'Untitled')} - {item.get('created_at', '')[:10]}"):
-                        col1, col2 = st.columns([3, 1])
-                        
-                        with col1:
-                            st.markdown(f"**Created:** {item.get('created_at', 'Unknown')}")
-                            if item.get('transcript'):
-                                st.markdown("**Transcript Preview:**")
-                                st.text(item['transcript'][:200] + "..." if len(item['transcript']) > 200 else item['transcript'])
-                        
-                        with col2:
-                            if st.button(f"🔄 Reprocess", key=f"reprocess_{item.get('id')}"):
-                                st.info("Reprocessing feature coming soon!")
-                            
-                            if st.button(f"📤 Export", key=f"export_{item.get('id')}"):
-                                st.info("Export feature coming soon!")
-                        
-                        # Show generated content
-                        if item.get('wisdom'):
-                            st.markdown("**💡 Wisdom:**")
-                            st.text_area("", item['wisdom'], height=100, disabled=True, key=f"wisdom_{item.get('id')}")
-                        
-                        if item.get('article'):
-                            st.markdown("**📰 Article:**") 
-                            st.text_area("", item['article'], height=150, disabled=True, key=f"article_{item.get('id')}")
-                        
-                        if item.get('social_content'):
-                            st.markdown("**📱 Social Content:**")
-                            st.text_area("", item['social_content'], height=100, disabled=True, key=f"social_{item.get('id')}")
+                    with cols[idx % 2].expander(f"{item.get('title', 'Untitled')} - {item.get('created_at', '')[:10]}"):
+                        if item.get("transcript"):
+                            st.markdown("**Transcript Preview:**")
+                            preview = item["transcript"][:200]
+                            st.text(preview + "..." if len(item["transcript"]) > 200 else preview)
+
+                        if item.get("wisdom"):
+                            st.markdown("**Wisdom:**")
+                            st.text_area("", item["wisdom"], height=100, disabled=True, key=f"wisdom_{item.get('id')}")
+
+                        if item.get("article"):
+                            st.markdown("**Article:**")
+                            st.text_area(
+                                "", item["article"], height=150, disabled=True, key=f"article_{item.get('id')}"
+                            )
             else:
-                st.info("📭 No content found. Process some audio files to see them here!")
+                st.info("No content found. Process some audio files to see them here!")
         else:
-            st.error("❌ Database connection failed")
+            st.error("Database connection failed")
     except Exception as e:
-        st.error(f"❌ Error loading content library: {e}")
-
-def show_settings_page():
-    """Settings and configuration page"""
-    st.markdown("### ⚙️ Settings & Configuration")
-    
-    # API Keys section
-    st.markdown("#### 🔑 API Keys")
-    with st.expander("🔧 API Configuration", expanded=True):
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            # OpenAI settings
-            st.markdown("**OpenAI Configuration**")
-            openai_key = st.text_input("OpenAI API Key", type="password", 
-                                      value=os.getenv("OPENAI_API_KEY", ""),
-                                      help="Your OpenAI API key")
-            if openai_key:
-                os.environ["OPENAI_API_KEY"] = openai_key
-                st.success("✅ OpenAI key configured")
-            
-            # Model selection
-            model_choice = st.selectbox("OpenAI Model", 
-                                       ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo"],
-                                       help="Choose the OpenAI model for content generation")
-            st.session_state.openai_model = model_choice
-        
-        with col2:
-            # Notion settings
-            st.markdown("**Notion Configuration**")
-            notion_key = st.text_input("Notion API Key", type="password",
-                                      value=os.getenv("NOTION_API_KEY", ""),
-                                      help="Your Notion integration token")
-            if notion_key:
-                os.environ["NOTION_API_KEY"] = notion_key
-            
-            notion_db = st.text_input("Notion Database ID",
-                                     value=os.getenv("NOTION_DATABASE_ID", ""),
-                                     help="Your Notion database ID")
-            if notion_db:
-                os.environ["NOTION_DATABASE_ID"] = notion_db
-            
-            if notion_key and notion_db:
-                st.success("✅ Notion configured")
-    
-    # Pipeline settings
-    st.markdown("#### 🔄 Pipeline Configuration")
-    with st.expander("⚙️ Processing Pipeline", expanded=True):
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            st.markdown("**Core Features**")
-            auto_notion = st.checkbox("Auto-publish to Notion",
-                                     value=st.session_state.get('auto_notion', True))
-            st.session_state.auto_notion = auto_notion
-
-            live_stream = st.checkbox(
-                "Live Streaming",
-                value=st.session_state.get('live_stream', False),
-                help="Show step-by-step streaming results"
-            )
-            st.session_state.live_stream = live_stream
-            
-            large_file_mode = st.checkbox("Enhanced Large File Processing", 
-                                        value=st.session_state.get('large_file_mode', True),
-                                        help="Use FFmpeg for files larger than 25MB")
-            st.session_state.large_file_mode = large_file_mode
-        
-        with col2:
-            st.markdown("**Quality Settings**")
-            content_length = st.selectbox("Article Length", 
-                                        ["Short (500-800 words)", "Medium (800-1200 words)", "Long (1200+ words)"])
-            
-            tone_style = st.selectbox("Content Tone",
-                                    ["Professional", "Conversational", "Academic", "Creative"])
-
-            st.session_state.content_length = content_length
-            st.session_state.tone_style = tone_style
-
-            templates = [f.replace('.md','') for f in os.listdir('templates')] if os.path.exists('templates') else []
-            if templates:
-                template_choice = st.selectbox("Article Template", templates)
-                st.session_state.article_template = template_choice
-            else:
-                st.session_state.article_template = None
-    
-    # System status
-    st.markdown("#### 🔍 System Status")
-    with st.expander("📊 Connection Status", expanded=False):
-        if st.button("🧪 Test All Connections"):
-            with st.spinner("Testing all connections..."):
-                # Test OpenAI
-                try:
-                    if os.getenv("OPENAI_API_KEY"):
-                        st.success("✅ OpenAI API key configured")
-                    else:
-                        st.error("❌ OpenAI API key missing")
-                except Exception as e:
-                    st.error(f"❌ OpenAI error: {e}")
-                
-                # Test Supabase
-                try:
-                    db = get_supabase_client()
-                    if db and db.test_connection():
-                        st.success("✅ Supabase connected")
-                    else:
-                        st.error("❌ Supabase connection failed")
-                except Exception as e:
-                    st.error(f"❌ Supabase error: {e}")
-                
-                # Test Notion
-                try:
-                    if os.getenv("NOTION_API_KEY") and os.getenv("NOTION_DATABASE_ID"):
-                        from notion_client import Client
-                        client = Client(auth=os.getenv("NOTION_API_KEY"))
-                        database = client.databases.retrieve(database_id=os.getenv("NOTION_DATABASE_ID"))
-                        st.success("✅ Notion connected")
-                    else:
-                        st.warning("⚠️ Notion not configured")
-                except Exception as e:
-                    st.error(f"❌ Notion error: {e}")
-
-def show_knowledge_base():
-    """Knowledge base management page"""
-    st.markdown("### 🧠 Knowledge Base")
-    
-    # Check if knowledge base files exist
-    kb_path = "prompts/default/knowledge_base"
-    
-    st.markdown("""
-    The knowledge base provides context and expertise to enhance content generation.
-    Add domain-specific information, style guides, and reference materials here.
-    """)
-    
-    # Knowledge base sections
-    tabs = st.tabs(["📖 View Knowledge", "➕ Add Knowledge", "🔧 Manage Files"])
-    
-    with tabs[0]:
-        st.markdown("#### 📖 Current Knowledge Base")
-        try:
-            if os.path.exists(kb_path):
-                files = [f for f in os.listdir(kb_path) if f.endswith('.md')]
-                if files:
-                    selected_file = st.selectbox("Select knowledge file:", files)
-                    if selected_file:
-                        file_path = os.path.join(kb_path, selected_file)
-                        with open(file_path, 'r') as f:
-                            content = f.read()
-                        
-                        st.markdown(f"**File:** `{selected_file}`")
-                        create_enhanced_aurora_content_card("Knowledge Content", content, "text", "📖")
-                else:
-                    st.info("📭 No knowledge files found")
-            else:
-                st.info("📁 Knowledge base directory not found")
-        except Exception as e:
-            st.error(f"❌ Error reading knowledge base: {e}")
-    
-    with tabs[1]:
-        st.markdown("#### ➕ Add New Knowledge")
-        
-        col1, col2 = st.columns([2, 1])
-        with col1:
-            kb_title = st.text_input("Knowledge Title", placeholder="e.g., 'Marketing Guidelines'")
-        with col2:
-            kb_category = st.selectbox("Category", ["General", "Style Guide", "Domain Expertise", "Templates"])
-        
-        kb_content = st.text_area("Knowledge Content", 
-                                 placeholder="Enter your knowledge content here...",
-                                 height=300)
-        
-        if st.button("💾 Save Knowledge", type="primary"):
-            if kb_title and kb_content:
-                try:
-                    os.makedirs(kb_path, exist_ok=True)
-                    filename = f"{kb_title.lower().replace(' ', '_')}.md"
-                    file_path = os.path.join(kb_path, filename)
-                    
-                    with open(file_path, 'w') as f:
-                        f.write(f"# {kb_title}\n\n")
-                        f.write(f"**Category:** {kb_category}\n\n")
-                        f.write(kb_content)
-                    
-                    st.success(f"✅ Knowledge saved as `{filename}`")
-                except Exception as e:
-                    st.error(f"❌ Error saving knowledge: {e}")
-            else:
-                st.error("❌ Please provide both title and content")
-    
-    with tabs[2]:
-        st.markdown("#### 🔧 Manage Knowledge Files")
-        
-        try:
-            if os.path.exists(kb_path):
-                files = [f for f in os.listdir(kb_path) if f.endswith('.md')]
-                if files:
-                    for file in files:
-                        col1, col2, col3 = st.columns([3, 1, 1])
-                        with col1:
-                            st.markdown(f"📄 `{file}`")
-                        with col2:
-                            if st.button("📝 Edit", key=f"edit_{file}"):
-                                st.info("Edit functionality coming soon!")
-                        with col3:
-                            if st.button("🗑️ Delete", key=f"delete_{file}"):
-                                try:
-                                    os.remove(os.path.join(kb_path, file))
-                                    st.success(f"✅ Deleted `{file}`")
-                                    st.rerun()
-                                except Exception as e:
-                                    st.error(f"❌ Error deleting file: {e}")
-                else:
-                    st.info("📭 No knowledge files found")
-            else:
-                st.info("📁 Knowledge base directory not found")
-        except Exception as e:
-            st.error(f"❌ Error managing files: {e}")
-
-def show_prompts_page():
-    """Prompts management page"""
-    st.markdown("### 📝 Prompts Management")
-    
-    st.markdown("""
-    Customize the AI prompts used in each step of the content generation pipeline.
-    Fine-tune the output style, format, and focus for your specific needs.
-    """)
-    
-    # Prompt categories
-    prompt_types = {
-        "wisdom": "💡 Wisdom Extraction",
-        "outline": "📋 Content Outline", 
-        "article": "📰 Article Generation",
-        "social": "📱 Social Media Posts"
-    }
-    
-    tabs = st.tabs(list(prompt_types.values()) + ["🔧 Advanced"])
-    
-    for i, (prompt_key, prompt_name) in enumerate(prompt_types.items()):
-        with tabs[i]:
-            st.markdown(f"#### {prompt_name}")
-            
-            # Load current prompt - Map UI keys to actual pipeline files
-            file_mapping = {
-                "wisdom": "wisdom_extraction.md",
-                "outline": "outline_creation.md", 
-                "article": "article_generation.md",
-                "social": "social_media.md"
-            }
-            prompt_file = f"prompts/default/{file_mapping[prompt_key]}"
-            current_prompt = ""
-            
-            try:
-                if os.path.exists(prompt_file):
-                    with open(prompt_file, 'r') as f:
-                        current_prompt = f.read()
-                else:
-                    current_prompt = f"# {prompt_name} Prompt\n\nDefault prompt for {prompt_key} generation."
-            except Exception as e:
-                st.error(f"❌ Error loading prompt: {e}")
-            
-            # Edit prompt
-            new_prompt = st.text_area(
-                f"Edit {prompt_name} Prompt",
-                value=current_prompt,
-                height=400,
-                help=f"Customize the prompt used for {prompt_key} generation"
-            )
-            
-            col1, col2, col3 = st.columns([1, 1, 2])
-            with col1:
-                if st.button(f"💾 Save", key=f"save_{prompt_key}"):
-                    try:
-                        os.makedirs("prompts/default", exist_ok=True)
-                        with open(prompt_file, 'w') as f:
-                            f.write(new_prompt)
-                        st.success(f"✅ {prompt_name} prompt saved!")
-                    except Exception as e:
-                        st.error(f"❌ Error saving prompt: {e}")
-            
-            with col2:
-                if st.button(f"🔄 Reset", key=f"reset_{prompt_key}"):
-                    st.info("Reset to default functionality coming soon!")
-            
-            with col3:
-                st.markdown(f"**File:** `{prompt_file}`")
-    
-    # Advanced settings
-    with tabs[-1]:
-        st.markdown("#### 🔧 Advanced Prompt Settings")
-        
-        col1, col2 = st.columns(2)
-        with col1:
-            st.markdown("**Global Settings**")
-            temperature = st.slider("Temperature (Creativity)", 0.0, 1.0, 0.7, 0.1)
-            max_tokens = st.number_input("Max Tokens", 100, 4000, 2000)
-            
-        with col2:
-            st.markdown("**Prompt Templates**")
-            if st.button("📥 Import Prompt Set"):
-                st.info("Import functionality coming soon!")
-            if st.button("📤 Export Prompt Set"):
-                st.info("Export functionality coming soon!")
-        
-        st.session_state.temperature = temperature
-        st.session_state.max_tokens = max_tokens
-
-def create_aurora_tabs(tab_data, default_tab=0):
-    """Create beautiful Aurora-styled tabs with a simplified, reliable approach"""
-    import uuid
-    
-    # Generate unique ID for this tab group
-    tab_group_id = f"tabs_{uuid.uuid4().hex[:8]}"
-    
-    # Initialize session state for this tab group
-    if f"{tab_group_id}_active" not in st.session_state:
-        st.session_state[f"{tab_group_id}_active"] = default_tab
-    
-    # Calculate content stats for display
-    tab_stats = []
-    for tab in tab_data:
-        content = tab.get('content', '')
-        word_count = len(str(content).split()) if content else 0
-        tab_stats.append(word_count)
-    
-    # Create the tabs container with Aurora styling
-    st.markdown("""
-    <div class="aurora-tabs-container">
-        <div class="aurora-tabs-actions">
-            <h3 class="aurora-tabs-title">
-                <span>📄</span>
-                Generated Content
-            </h3>
-        </div>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Create tab selector using Streamlit's selectbox with Aurora styling
-    tab_options = []
-    for i, tab in enumerate(tab_data):
-        word_count = tab_stats[i]
-        tab_options.append(f"{tab['icon']} {tab['title']} ({word_count} words)")
-    
-    # Custom styled selectbox
-    st.markdown("""
-    <style>
-    .aurora-tab-selector .stSelectbox > div > div {
-        background: var(--aurora-bg-glass) !important;
-        border: 2px solid var(--aurora-border) !important;
-        border-radius: var(--aurora-radius) !important;
-        color: var(--aurora-text) !important;
-    }
-    .aurora-tab-selector .stSelectbox > div > div:hover {
-        border-color: var(--aurora-border-hover) !important;
-        box-shadow: var(--aurora-glow-subtle) !important;
-    }
-    </style>
-    """, unsafe_allow_html=True)
-    
-    # Tab selector
-    with st.container():
-        st.markdown('<div class="aurora-tab-selector">', unsafe_allow_html=True)
-        selected_tab_label = st.selectbox(
-            "Select Content Type:",
-            tab_options,
-            index=st.session_state[f"{tab_group_id}_active"],
-            key=f"tab_select_{tab_group_id}",
-            label_visibility="collapsed"
-        )
-        st.markdown('</div>', unsafe_allow_html=True)
-    
-    # Find selected tab index
-    selected_index = 0
-    for i, option in enumerate(tab_options):
-        if option == selected_tab_label:
-            selected_index = i
-            break
-    
-    # Update session state
-    st.session_state[f"{tab_group_id}_active"] = selected_index
-    
-    # Display selected content with Aurora styling
-    if 0 <= selected_index < len(tab_data):
-        active_tab = tab_data[selected_index]
-        
-        # Add quick actions for the active tab
-        col1, col2, col3, col4 = st.columns([1, 1, 1, 2])
-        
-        with col1:
-            if st.button("📋 Copy", key=f"copy_{tab_group_id}_{selected_index}", use_container_width=True):
-                st.code(active_tab.get('content', ''), language='text')
-                st.success("✅ Content displayed above - copy with Ctrl+A, Ctrl+C")
-        
-        with col2:
-            content = active_tab.get('content', '')
-            if content:
-                st.download_button(
-                    label="💾 Download",
-                    data=content,
-                    file_name=f"{active_tab['title'].lower().replace(' ', '_')}.txt",
-                    mime="text/plain",
-                    key=f"download_{tab_group_id}_{selected_index}",
-                    use_container_width=True
-                )
-        
-        with col3:
-            if st.button("📊 Stats", key=f"stats_{tab_group_id}_{selected_index}", use_container_width=True):
-                word_count = len(str(content).split())
-                char_count = len(str(content))
-                st.info(f"📊 **{active_tab['title']}**: {word_count} words, {char_count} characters")
-        
-        # Display the content using our enhanced content card
-        st.markdown('<div class="aurora-tab-content">', unsafe_allow_html=True)
-        create_enhanced_aurora_content_card(
-            title=active_tab['title'],
-            content=active_tab.get('content', ''),
-            content_type=active_tab.get('type', 'text'),
-            icon=active_tab['icon']
-        )
-        st.markdown('</div>', unsafe_allow_html=True)
-    
-    return selected_index
+        st.error(f"Error loading content library: {e}")
+
 
 # === MAIN APP ===
+
+
 def show_main_app():
-    """Main application interface with navigation"""
-    # Create navigation
+    """Main application interface with navigation."""
     tabs = create_aurora_navigation()
-    
-    # Show different pages based on selected tab
-    with tabs[0]:  # Transform
+
+    with tabs[0]:
         show_transform_page()
-    
-    with tabs[1]:  # Content Library
+    with tabs[1]:
         show_content_library()
-    
-    with tabs[2]:  # Settings
+    with tabs[2]:
         show_settings_page()
-    
-    with tabs[3]:  # Knowledge Base
+    with tabs[3]:
         show_knowledge_base()
-    
-    with tabs[4]:  # Prompts
+    with tabs[4]:
         show_prompts_page()
 
-# === ENTRY POINT ===
+
 def main():
-    """Application entry point"""
+    """Application entry point."""
     init_session()
-    
+
     if st.session_state.authenticated:
         show_main_app()
     else:
         show_login()
 
+
 if __name__ == "__main__":
-    main() 
\ No newline at end of file
+    main()
diff --git a/core/__init__.py b/core/__init__.py
index f9878a5..489621f 100644
--- a/core/__init__.py
+++ b/core/__init__.py
@@ -11,8 +11,4 @@
 # Core modules - some legacy imports removed for cleanup
 from .config import Config, get_config, set_config
 
-__all__ = [
-    "Config",
-    "get_config", 
-    "set_config"
-] 
\ No newline at end of file
+__all__ = ["Config", "get_config", "set_config"]
diff --git a/core/api_clients.py b/core/api_clients.py
new file mode 100644
index 0000000..7a4a110
--- /dev/null
+++ b/core/api_clients.py
@@ -0,0 +1,75 @@
+"""API client factory functions for WhisperForge.
+
+Provides lazy-initialised clients for OpenAI, Anthropic, and Grok APIs.
+Each factory returns ``None`` when the required API key is missing or the
+underlying package is not installed, allowing callers to degrade
+gracefully.
+"""
+
+import logging
+import os
+
+logger = logging.getLogger(__name__)
+
+
+def get_openai_client():
+    """Get OpenAI client with API key.
+
+    Returns None only when the key is missing or the package isn't installed.
+    Other errors (e.g. network, auth) propagate so callers can react.
+    """
+    try:
+        import openai
+    except ImportError:
+        logger.error("OpenAI package not installed")
+        return None
+
+    api_key = os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        return None
+
+    return openai.OpenAI(api_key=api_key)
+
+
+def get_anthropic_client():
+    """Get Anthropic client with API key.
+
+    Returns None only when the key is missing or the package isn't installed.
+    Other errors propagate so callers can react.
+    """
+    try:
+        import anthropic
+    except ImportError:
+        logger.error("Anthropic package not installed")
+        return None
+
+    api_key = os.getenv("ANTHROPIC_API_KEY")
+    if not api_key:
+        return None
+
+    return anthropic.Anthropic(api_key=api_key)
+
+
+def get_grok_api_key():
+    """Get Grok API key"""
+    return os.getenv("GROK_API_KEY")
+
+
+def get_grok_client():
+    """Get a Grok client via the OpenAI-compatible API.
+
+    Returns an ``openai.OpenAI`` instance pointed at the Grok endpoint,
+    or ``None`` when the API key is missing or the ``openai`` package is
+    not installed.
+    """
+    try:
+        import openai
+    except ImportError:
+        logger.error("OpenAI package not installed (required for Grok client)")
+        return None
+
+    api_key = get_grok_api_key()
+    if not api_key:
+        return None
+
+    return openai.OpenAI(api_key=api_key, base_url="https://api.x.ai/v1")
diff --git a/core/auth_wrapper.py b/core/auth_wrapper.py
index dd86db3..622a5ea 100644
--- a/core/auth_wrapper.py
+++ b/core/auth_wrapper.py
@@ -4,301 +4,331 @@
 Provides backward compatibility while adding session persistence
 """
 
-import streamlit as st
-from typing import Optional, Dict, Any
-from .session_manager import get_session_manager
-from .supabase_integration import get_supabase_client
-from .utils import hash_password, verify_password, legacy_hash_password
+from typing import Any
+
+from postgrest.exceptions import APIError
+
 from core.logging_config import logger
 
+from .exceptions import AuthenticationError, DatabaseError
+from .session_manager import get_session_manager
+from .utils import hash_password, legacy_hash_password, verify_password
+
 
 class AuthWrapper:
     """
     Authentication wrapper that provides persistent sessions
     while maintaining compatibility with existing auth patterns
     """
-    
+
     def __init__(self):
         self.session_manager = get_session_manager()
         self.supabase_client = None
         self._init_supabase()
-    
+
     def _init_supabase(self):
         """Initialize Supabase client"""
         try:
             from .supabase_integration import get_supabase_client
+
             self.supabase_client = get_supabase_client()
-        except Exception as e:
+        except (APIError, ValueError) as e:
             logger.log_error(e, "Failed to initialize Supabase")
             self.supabase_client = None
-    
+        except DatabaseError as e:
+            logger.log_error(e, "Unexpected error initializing Supabase")
+            self.supabase_client = None
+
     def is_authenticated(self) -> bool:
         """Check if user is authenticated (backward compatible)"""
         return self.session_manager.is_authenticated()
-    
-    def get_user_id(self) -> Optional[str]:
+
+    def get_user_id(self) -> str | None:
         """Get current user ID (backward compatible)"""
         return self.session_manager.get_user_id()
-    
-    def get_user_email(self) -> Optional[str]:
+
+    def get_user_email(self) -> str | None:
         """Get current user email (backward compatible)"""
         return self.session_manager.get_user_email()
-    
+
     def authenticate_user(self, email: str, password: str) -> bool:
         """
         Authenticate user with Supabase and create persistent session
         Maintains backward compatibility with existing auth logic
         """
         try:
-            logger.logger.info(f"Authentication attempt for: {email}")
-            
+            logger.info(f"Authentication attempt for: {email}")
+
             if not self.supabase_client:
                 logger.log_error(Exception("Supabase client not available"), "Authentication failed")
                 return False
-            
+
             # Get user by email from Supabase
             result = self.supabase_client.client.table("users").select("*").eq("email", email).execute()
-            
+
             if not result.data:
-                logger.logger.warning(f"User not found: {email}")
+                logger.warning(f"User not found: {email}")
                 return False
-            
+
             user = result.data[0]
             stored_password = user.get("password", "")
-            
+
             # Verify password (bcrypt or legacy)
             password_valid = False
-            password_migrated = False
-            
-            if stored_password.startswith('$2b$'):
+
+            if stored_password.startswith("$2b$"):
                 # bcrypt password
                 password_valid = verify_password(password, stored_password)
             else:
                 # Legacy password - check and migrate
                 if legacy_hash_password(password) == stored_password:
                     password_valid = True
-                    password_migrated = True
-                    
+
                     # Migrate to bcrypt
                     try:
                         new_hash = hash_password(password)
-                        self.supabase_client.client.table("users").update(
-                            {"password": new_hash}
-                        ).eq("id", user["id"]).execute()
-                        logger.logger.info(f"Password migrated to bcrypt for user: {email}")
-                    except Exception as e:
+                        self.supabase_client.client.table("users").update({"password": new_hash}).eq(
+                            "id", user["id"]
+                        ).execute()
+                        logger.info(f"Password migrated to bcrypt for user: {email}")
+                    except (APIError, ValueError) as e:
                         logger.log_error(e, "Failed to migrate password")
-            
+                    except DatabaseError as e:
+                        logger.log_error(e, "Unexpected error migrating password")
+
             if password_valid:
                 # Create persistent session using SessionManager
                 if self.session_manager.authenticate_user(user["id"], email):
-                    logger.logger.info(f"User authenticated successfully: {email}")
-                    
+                    logger.info(f"User authenticated successfully: {email}")
+
                     # Load user preferences from database
                     self._load_user_preferences(user["id"])
-                    
+
                     return True
                 else:
-                    logger.log_error(Exception(f"Failed to create persistent session for: {email}"), "Authentication failed")
+                    logger.log_error(
+                        Exception(f"Failed to create persistent session for: {email}"), "Authentication failed"
+                    )
                     return False
             else:
-                logger.logger.warning(f"Invalid password for user: {email}")
+                logger.warning(f"Invalid password for user: {email}")
                 return False
-        
-        except Exception as e:
+
+        except (APIError, ValueError) as e:
             logger.log_error(e, f"Authentication error for {email}")
             return False
-    
+        except (AuthenticationError, DatabaseError) as e:
+            logger.log_error(e, f"Unexpected authentication error for {email}")
+            return False
+
     def register_user(self, email: str, password: str) -> bool:
         """Register new user and create session"""
         try:
             if not self.supabase_client:
                 logger.log_error(Exception("Supabase client not available for registration"), "Registration failed")
                 return False
-            
+
             # Check if user already exists
             existing = self.supabase_client.client.table("users").select("id").eq("email", email).execute()
             if existing.data:
-                logger.logger.warning(f"User already exists: {email}")
+                logger.warning(f"User already exists: {email}")
                 return False
-            
+
             # Hash password
             hashed_password = hash_password(password)
-            
+
             # Create user in database
-            user_data = {
-                "email": email,
-                "password": hashed_password,
-                "created_at": "now()"
-            }
-            
+            user_data = {"email": email, "password": hashed_password, "created_at": "now()"}
+
             result = self.supabase_client.client.table("users").insert(user_data).execute()
-            
+
             if result.data:
                 user = result.data[0]
-                logger.logger.info(f"User registered successfully: {email}")
-                
+                logger.info(f"User registered successfully: {email}")
+
                 # Create session for new user
                 if self.session_manager.authenticate_user(user["id"], email):
-                    logger.logger.info(f"Session created for new user: {email}")
+                    logger.info(f"Session created for new user: {email}")
                     return True
                 else:
-                    logger.log_error(Exception(f"Failed to create session for new user: {email}"), "Registration failed")
+                    logger.log_error(
+                        Exception(f"Failed to create session for new user: {email}"), "Registration failed"
+                    )
                     return False
             else:
                 logger.log_error(Exception(f"Failed to create user in database: {email}"), "Registration failed")
                 return False
-        
-        except Exception as e:
+
+        except (APIError, ValueError) as e:
             logger.log_error(e, f"Registration error for {email}")
             return False
-    
+        except (AuthenticationError, DatabaseError) as e:
+            logger.log_error(e, f"Unexpected registration error for {email}")
+            return False
+
     def logout(self) -> bool:
         """Log out user and clear session"""
         try:
             email = self.get_user_email()
             if self.session_manager.logout():
-                logger.logger.info(f"User logged out: {email}")
+                logger.info(f"User logged out: {email}")
                 return True
             else:
                 logger.log_error(Exception(f"Failed to logout user: {email}"), "Logout failed")
                 return False
-        except Exception as e:
+        except (APIError, ValueError) as e:
             logger.log_error(e, "Logout error")
             return False
-    
+        except (AuthenticationError, DatabaseError) as e:
+            logger.log_error(e, "Unexpected logout error")
+            return False
+
     def _load_user_preferences(self, user_id: str):
         """Load user preferences from database into session"""
         try:
             if not self.supabase_client:
                 return
-            
+
             # Load API keys
-            api_keys_result = self.supabase_client.client.table("api_keys").select(
-                "key_name, key_value"
-            ).eq("user_id", user_id).execute()
-            
+            api_keys_result = (
+                self.supabase_client.client.table("api_keys")
+                .select("key_name, key_value")
+                .eq("user_id", user_id)
+                .execute()
+            )
+
             api_keys = {}
             for item in api_keys_result.data:
                 api_keys[item["key_name"]] = item["key_value"]
-            
+
             # Load custom prompts
-            prompts_result = self.supabase_client.client.table("prompts").select(
-                "prompt_type, content"
-            ).eq("user_id", user_id).execute()
-            
+            prompts_result = (
+                self.supabase_client.client.table("prompts")
+                .select("prompt_type, content")
+                .eq("user_id", user_id)
+                .execute()
+            )
+
             prompts = {}
             for item in prompts_result.data:
                 prompts[item["prompt_type"]] = item["content"]
-            
+
             # Store in session preferences
             self.session_manager.set_preference("api_keys", api_keys)
             self.session_manager.set_preference("custom_prompts", prompts)
-            
-            logger.logger.debug(f"Loaded preferences for user: {user_id}")
-            
-        except Exception as e:
+
+            logger.debug(f"Loaded preferences for user: {user_id}")
+
+        except (APIError, ValueError) as e:
             logger.log_error(e, "Failed to load user preferences")
-    
-    def get_api_keys(self) -> Dict[str, str]:
+        except DatabaseError as e:
+            logger.log_error(e, "Unexpected error loading user preferences")
+
+    def get_api_keys(self) -> dict[str, str]:
         """Get user API keys from session cache"""
         return self.session_manager.get_preference("api_keys", {})
-    
+
     def update_api_key(self, key_name: str, key_value: str) -> bool:
         """Update API key in database and session cache"""
         try:
             if not self.supabase_client or not self.is_authenticated():
                 return False
-            
+
             user_id = self.get_user_id()
-            
+
             # Update in database
-            result = self.supabase_client.client.table("api_keys").upsert({
-                "user_id": user_id,
-                "key_name": key_name,
-                "key_value": key_value,
-                "updated_at": "now()"
-            }).execute()
-            
+            result = (
+                self.supabase_client.client.table("api_keys")
+                .upsert({"user_id": user_id, "key_name": key_name, "key_value": key_value, "updated_at": "now()"})
+                .execute()
+            )
+
             if result.data:
                 # Update session cache
                 api_keys = self.get_api_keys()
                 api_keys[key_name] = key_value
                 self.session_manager.set_preference("api_keys", api_keys)
-                
-                logger.logger.info(f"API key updated: {key_name}")
+
+                logger.info(f"API key updated: {key_name}")
                 return True
             else:
                 logger.log_error(Exception(f"Failed to update API key: {key_name}"), "API key update failed")
                 return False
-        
-        except Exception as e:
+
+        except (APIError, ValueError) as e:
             logger.log_error(e, f"Error updating API key {key_name}")
             return False
-    
-    def get_custom_prompts(self) -> Dict[str, str]:
+        except DatabaseError as e:
+            logger.log_error(e, f"Unexpected error updating API key {key_name}")
+            return False
+
+    def get_custom_prompts(self) -> dict[str, str]:
         """Get user custom prompts from session cache"""
         return self.session_manager.get_preference("custom_prompts", {})
-    
+
     def update_custom_prompt(self, prompt_type: str, content: str) -> bool:
         """Update custom prompt in database and session cache"""
         try:
             if not self.supabase_client or not self.is_authenticated():
                 return False
-            
+
             user_id = self.get_user_id()
-            
+
             # Update in database
-            result = self.supabase_client.client.table("prompts").upsert({
-                "user_id": user_id,
-                "prompt_type": prompt_type,
-                "content": content,
-                "updated_at": "now()"
-            }).execute()
-            
+            result = (
+                self.supabase_client.client.table("prompts")
+                .upsert({"user_id": user_id, "prompt_type": prompt_type, "content": content, "updated_at": "now()"})
+                .execute()
+            )
+
             if result.data:
                 # Update session cache
                 prompts = self.get_custom_prompts()
                 prompts[prompt_type] = content
                 self.session_manager.set_preference("custom_prompts", prompts)
-                
+
                 logger.info(f"Custom prompt updated: {prompt_type}")
                 return True
             else:
-                logger.log_error(Exception(f"Failed to update custom prompt: {prompt_type}"), "Custom prompt update failed")
+                logger.log_error(
+                    Exception(f"Failed to update custom prompt: {prompt_type}"), "Custom prompt update failed"
+                )
                 return False
-        
-        except Exception as e:
+
+        except (APIError, ValueError) as e:
             logger.log_error(e, f"Error updating custom prompt {prompt_type}")
             return False
-    
+        except DatabaseError as e:
+            logger.log_error(e, f"Unexpected error updating custom prompt {prompt_type}")
+            return False
+
     # Session Manager delegation methods
-    
+
     def get_preference(self, key: str, default: Any = None) -> Any:
         """Get user preference (delegated to SessionManager)"""
         return self.session_manager.get_preference(key, default)
-    
+
     def set_preference(self, key: str, value: Any) -> bool:
         """Set user preference (delegated to SessionManager)"""
         return self.session_manager.set_preference(key, value)
-    
+
     def get_current_page(self) -> str:
         """Get current page (delegated to SessionManager)"""
         return self.session_manager.get_current_page()
-    
+
     def set_current_page(self, page: str) -> None:
         """Set current page (delegated to SessionManager)"""
         self.session_manager.set_current_page(page)
-    
+
     def is_pipeline_active(self) -> bool:
         """Check if pipeline is active (delegated to SessionManager)"""
         return self.session_manager.is_pipeline_active()
-    
+
     def set_pipeline_active(self, active: bool) -> None:
         """Set pipeline active state (delegated to SessionManager)"""
         self.session_manager.set_pipeline_active(active)
-    
-    def get_session_info(self) -> Dict[str, Any]:
+
+    def get_session_info(self) -> dict[str, Any]:
         """Get session information for debugging"""
         return self.session_manager.get_session_info()
 
@@ -306,6 +336,7 @@ def get_session_info(self) -> Dict[str, Any]:
 # Global auth wrapper instance
 _auth_wrapper = None
 
+
 def get_auth() -> AuthWrapper:
     """Get global authentication wrapper instance"""
     global _auth_wrapper
@@ -319,22 +350,27 @@ def authenticate_user(email: str, password: str) -> bool:
     """Backward compatible authentication function"""
     return get_auth().authenticate_user(email, password)
 
+
 def register_user_supabase(email: str, password: str) -> bool:
     """Backward compatible registration function"""
     return get_auth().register_user(email, password)
 
-def get_user_api_keys_supabase() -> Dict[str, str]:
+
+def get_user_api_keys_supabase() -> dict[str, str]:
     """Backward compatible API keys function"""
     return get_auth().get_api_keys()
 
+
 def update_api_key_supabase(key_name: str, key_value: str) -> bool:
     """Backward compatible API key update function"""
     return get_auth().update_api_key(key_name, key_value)
 
-def get_user_prompts_supabase() -> Dict[str, str]:
+
+def get_user_prompts_supabase() -> dict[str, str]:
     """Backward compatible prompts function"""
     return get_auth().get_custom_prompts()
 
+
 def save_user_prompt_supabase(prompt_type: str, content: str) -> bool:
     """Backward compatible prompt save function"""
-    return get_auth().update_custom_prompt(prompt_type, content) 
\ No newline at end of file
+    return get_auth().update_custom_prompt(prompt_type, content)
diff --git a/core/config.py b/core/config.py
index b59d038..a9f28e3 100644
--- a/core/config.py
+++ b/core/config.py
@@ -5,11 +5,11 @@
 Centralized configuration for WhisperForge v2.0
 """
 
+import logging
 import os
-from typing import Dict, Optional, Any
 from dataclasses import dataclass, field
 from pathlib import Path
-import logging
+from typing import Any
 
 logger = logging.getLogger(__name__)
 
@@ -19,19 +19,19 @@ class AIProviderConfig:
     """Configuration for AI providers"""
 
     name: str
-    api_key: Optional[str] = None
-    base_url: Optional[str] = None
-    models: Dict[str, Any] = field(default_factory=dict)
-    rate_limits: Dict[str, int] = field(default_factory=dict)
+    api_key: str | None = None
+    base_url: str | None = None
+    models: dict[str, Any] = field(default_factory=dict)
+    rate_limits: dict[str, int] = field(default_factory=dict)
 
 
 @dataclass
 class NotionConfig:
     """Configuration for Notion integration"""
 
-    api_key: Optional[str] = None
-    database_id: Optional[str] = None
-    template_id: Optional[str] = None
+    api_key: str | None = None
+    database_id: str | None = None
+    template_id: str | None = None
 
 
 @dataclass
@@ -46,9 +46,7 @@ class Config:
 
     # AI Providers
     openai: AIProviderConfig = field(default_factory=lambda: AIProviderConfig("openai"))
-    anthropic: AIProviderConfig = field(
-        default_factory=lambda: AIProviderConfig("anthropic")
-    )
+    anthropic: AIProviderConfig = field(default_factory=lambda: AIProviderConfig("anthropic"))
     grok: AIProviderConfig = field(default_factory=lambda: AIProviderConfig("grok"))
 
     # Integrations
@@ -99,9 +97,7 @@ def from_env(cls) -> "Config":
     def from_file(cls, config_path: Path) -> "Config":
         """Load configuration (simplified version without YAML)"""
         # For now, just use environment variables
-        logger.info(
-            "Using environment variables for configuration (YAML support disabled)"
-        )
+        logger.info("Using environment variables for configuration (YAML support disabled)")
         return cls.from_env()
 
     def validate(self) -> bool:
@@ -116,7 +112,7 @@ def validate(self) -> bool:
         for dir_path in [self.data_dir, self.prompts_dir, self.temp_dir]:
             try:
                 dir_path.mkdir(parents=True, exist_ok=True)
-            except Exception as e:
+            except OSError as e:
                 errors.append(f"Cannot create directory {dir_path}: {e}")
 
         if errors:
@@ -139,7 +135,7 @@ def get_available_providers(self) -> list[str]:
 
 
 # Global config instance
-_config: Optional[Config] = None
+_config: Config | None = None
 
 
 def get_config() -> Config:
diff --git a/core/constants.py b/core/constants.py
new file mode 100644
index 0000000..a37334b
--- /dev/null
+++ b/core/constants.py
@@ -0,0 +1,70 @@
+"""
+Application-wide constants for WhisperForge.
+
+These are compile-time invariants. For runtime configuration
+(API keys, environment, etc.), see core/config.py.
+"""
+
+# ---------------------------------------------------------------------------
+# File size limits
+# ---------------------------------------------------------------------------
+MAX_UPLOAD_SIZE_BYTES: int = 2 * 1024 * 1024 * 1024  # 2 GB
+LARGE_FILE_THRESHOLD_BYTES: int = 100 * 1024 * 1024  # 100 MB (triggers FFmpeg)
+LARGE_FILE_THRESHOLD_MB: int = 20  # Pipeline routing threshold
+
+# ---------------------------------------------------------------------------
+# Audio processing
+# ---------------------------------------------------------------------------
+CHUNK_DURATION_MINUTES: int = 10  # FFmpeg chunk length
+MAX_PARALLEL_CHUNKS_STANDARD: int = 3  # pydub-based processing
+MAX_PARALLEL_CHUNKS_FFMPEG: int = 4  # FFmpeg-based processing
+AUDIO_SAMPLE_RATE: int = 16_000  # Hz for Whisper input
+AUDIO_CHANNELS: int = 1  # Mono for Whisper input
+
+# ---------------------------------------------------------------------------
+# Transcription success thresholds
+# ---------------------------------------------------------------------------
+CHUNK_SUCCESS_THRESHOLD_STANDARD: float = 0.8  # file_upload.py
+CHUNK_SUCCESS_THRESHOLD_FFMPEG: float = 0.7  # large_file_processor.py
+
+# ---------------------------------------------------------------------------
+# Subprocess timeouts (seconds)
+# ---------------------------------------------------------------------------
+FFMPEG_VERSION_CHECK_TIMEOUT: int = 5
+FFPROBE_TIMEOUT: int = 30
+FFMPEG_CHUNK_TIMEOUT: int = 300
+
+# ---------------------------------------------------------------------------
+# Database defaults
+# ---------------------------------------------------------------------------
+DEFAULT_USAGE_QUOTA_MINUTES: int = 60  # New user monthly quota
+DEFAULT_CONTENT_QUERY_LIMIT: int = 50  # get_user_content()
+DEFAULT_ANALYTICS_DAYS: int = 30  # get_user_analytics()
+
+# ---------------------------------------------------------------------------
+# Content generation
+# ---------------------------------------------------------------------------
+MAX_TOKENS_DEFAULT: int = 1500
+MAX_TOKENS_ARTICLE: int = 2000
+TRANSCRIPT_PREVIEW_LENGTH: int = 2000
+ARTICLE_PREVIEW_LENGTH: int = 1500
+
+# ---------------------------------------------------------------------------
+# Notion integration
+# ---------------------------------------------------------------------------
+NOTION_CHUNK_SIZE: int = 1800
+MAX_NOTION_BLOCKS: int = 50
+NOTION_TITLE_EXCERPT_LENGTH: int = 500
+NOTION_TITLE_MAX_TOKENS: int = 30
+NOTION_TITLE_MAX_LENGTH: int = 60
+
+# ---------------------------------------------------------------------------
+# Content display
+# ---------------------------------------------------------------------------
+CONTENT_PREVIEW_LENGTH: int = 300
+
+# ---------------------------------------------------------------------------
+# Session
+# ---------------------------------------------------------------------------
+SESSION_EXPIRY_DAYS: int = 7
+DEFAULT_PAGE: str = "Transform"
diff --git a/core/content_display.py b/core/content_display.py
new file mode 100644
index 0000000..d7ade6f
--- /dev/null
+++ b/core/content_display.py
@@ -0,0 +1,411 @@
+"""
+Content Display Components
+===========================
+
+Aurora-styled content cards, tab interfaces, and result views for generated content.
+"""
+
+import html as html_mod
+import uuid
+from datetime import datetime
+
+import streamlit as st
+
+from .constants import CONTENT_PREVIEW_LENGTH
+from .export import create_text_export, export_to_markdown, export_to_word
+
+
+def create_enhanced_aurora_content_card(title, content, content_type="text", icon="\U0001f4c4"):
+    """Create an Aurora content card with copy/download/expand functionality."""
+    card_id = f"card_{uuid.uuid4().hex[:8]}"
+    copy_btn_id = f"copy_{uuid.uuid4().hex[:8]}"
+    expand_btn_id = f"expand_{uuid.uuid4().hex[:8]}"
+    full_content_id = f"full_{uuid.uuid4().hex[:8]}"
+
+    word_count = len(content.split()) if content else 0
+    char_count = len(content) if content else 0
+
+    preview_length = CONTENT_PREVIEW_LENGTH
+    needs_expansion = len(content) > preview_length
+    preview_content = content[:preview_length] + "..." if needs_expansion else content
+
+    type_class = content_type.lower()
+
+    # Escape user-controlled content for safe HTML embedding
+    safe_title = html_mod.escape(title)
+    safe_icon = html_mod.escape(icon)
+    escaped_content = html_mod.escape(content)
+    escaped_preview = html_mod.escape(preview_content)
+    js_safe_content = content.replace("\\", "\\\\").replace("`", "\\`").replace("${", "\\${")
+
+    card_html = f"""
+    <div class="aurora-content-card {type_class}" id="{card_id}">
+        <div class="aurora-content-card-header">
+            <h3 class="aurora-content-card-title">
+                <span class="aurora-content-card-icon">{safe_icon}</span>
+                {safe_title}
+            </h3>
+            <div class="aurora-content-card-actions">
+                <button class="aurora-content-action-btn copy-btn" id="{copy_btn_id}"
+                        onclick="copyToClipboard('{card_id}', '{copy_btn_id}')">
+                    <span>\U0001f4cb</span>
+                    <span class="copy-text">Copy</span>
+                </button>
+                <button class="aurora-content-action-btn"
+                        onclick="downloadContent('{safe_title}', `{js_safe_content}`)">
+                    <span>\U0001f4be</span>
+                    <span>Save</span>
+                </button>
+            </div>
+        </div>
+
+        <div class="aurora-content-card-body">
+            <div class="aurora-content-preview" id="preview_{card_id}">
+                {escaped_preview}
+            </div>
+
+            {
+        f'''
+            <div class="aurora-content-full" id="{full_content_id}">
+                {html_mod.escape(content[preview_length:])}
+            </div>
+            '''
+        if needs_expansion
+        else ""
+    }
+
+            <div class="aurora-content-stats">
+                <div class="aurora-content-word-count">
+                    <span>\U0001f4ca</span>
+                    <span>{word_count} words \u2022 {char_count} characters</span>
+                </div>
+
+                {
+        f'''
+                <button class="aurora-content-expand-btn" id="{expand_btn_id}"
+                        onclick="toggleExpand('{full_content_id}', '{expand_btn_id}')">
+                    <span>Show more</span>
+                    <span class="aurora-content-expand-icon">&#x25BC;</span>
+                </button>
+                '''
+        if needs_expansion
+        else ""
+    }
+            </div>
+        </div>
+
+        <!-- Hidden content for copying -->
+        <textarea id="content_{card_id}"
+                  style="position: absolute; left: -9999px; opacity: 0;">{escaped_content}</textarea>
+    </div>
+
+    <script>
+    function copyToClipboard(cardId, btnId) {{
+        const content = document.getElementById('content_' + cardId).value;
+        const button = document.getElementById(btnId);
+        const copyText = button.querySelector('.copy-text');
+
+        navigator.clipboard.writeText(content).then(function() {{
+            button.classList.add('copied');
+            copyText.textContent = 'Copied!';
+            setTimeout(() => {{
+                button.classList.remove('copied');
+                copyText.textContent = 'Copy';
+            }}, 2000);
+        }}).catch(function(err) {{
+            console.error('Failed to copy: ', err);
+            const textarea = document.getElementById('content_' + cardId);
+            textarea.select();
+            document.execCommand('copy');
+            button.classList.add('copied');
+            copyText.textContent = 'Copied!';
+            setTimeout(() => {{
+                button.classList.remove('copied');
+                copyText.textContent = 'Copy';
+            }}, 2000);
+        }});
+    }}
+
+    function toggleExpand(fullContentId, btnId) {{
+        const fullContent = document.getElementById(fullContentId);
+        const button = document.getElementById(btnId);
+        const buttonText = button.querySelector('span:first-child');
+
+        if (fullContent.classList.contains('expanded')) {{
+            fullContent.classList.remove('expanded');
+            button.classList.remove('expanded');
+            buttonText.textContent = 'Show more';
+        }} else {{
+            fullContent.classList.add('expanded');
+            button.classList.add('expanded');
+            buttonText.textContent = 'Show less';
+        }}
+    }}
+
+    function downloadContent(title, content) {{
+        const blob = new Blob([content], {{ type: 'text/plain' }});
+        const url = window.URL.createObjectURL(blob);
+        const a = document.createElement('a');
+        a.href = url;
+        a.download = title.replace(/[^a-z0-9]/gi, '_').toLowerCase() + '.txt';
+        document.body.appendChild(a);
+        a.click();
+        document.body.removeChild(a);
+        window.URL.revokeObjectURL(url);
+    }}
+    </script>
+    """
+
+    st.markdown(card_html, unsafe_allow_html=True)
+
+
+def create_aurora_tabs(tab_data, default_tab=0):
+    """Create Aurora-styled tab interface for content selection."""
+    tab_group_id = f"tabs_{uuid.uuid4().hex[:8]}"
+
+    if f"{tab_group_id}_active" not in st.session_state:
+        st.session_state[f"{tab_group_id}_active"] = default_tab
+
+    tab_stats = []
+    for tab in tab_data:
+        content = tab.get("content", "")
+        word_count = len(str(content).split()) if content else 0
+        tab_stats.append(word_count)
+
+    st.markdown(
+        """
+    <div class="aurora-tabs-container">
+        <div class="aurora-tabs-actions">
+            <h3 class="aurora-tabs-title">
+                <span>\U0001f4c4</span>
+                Generated Content
+            </h3>
+        </div>
+    </div>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    tab_options = []
+    for i, tab in enumerate(tab_data):
+        word_count = tab_stats[i]
+        tab_options.append(f"{tab['icon']} {tab['title']} ({word_count} words)")
+
+    st.markdown(
+        """
+    <style>
+    .aurora-tab-selector .stSelectbox > div > div {
+        background: var(--aurora-bg-glass) !important;
+        border: 2px solid var(--aurora-border) !important;
+        border-radius: var(--aurora-radius) !important;
+        color: var(--aurora-text) !important;
+    }
+    .aurora-tab-selector .stSelectbox > div > div:hover {
+        border-color: var(--aurora-border-hover) !important;
+        box-shadow: var(--aurora-glow-subtle) !important;
+    }
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    with st.container():
+        st.markdown('<div class="aurora-tab-selector">', unsafe_allow_html=True)
+        selected_tab_label = st.selectbox(
+            "Select Content Type:",
+            tab_options,
+            index=st.session_state[f"{tab_group_id}_active"],
+            key=f"tab_select_{tab_group_id}",
+            label_visibility="collapsed",
+        )
+        st.markdown("</div>", unsafe_allow_html=True)
+
+    selected_index = 0
+    for i, option in enumerate(tab_options):
+        if option == selected_tab_label:
+            selected_index = i
+            break
+
+    st.session_state[f"{tab_group_id}_active"] = selected_index
+
+    if 0 <= selected_index < len(tab_data):
+        active_tab = tab_data[selected_index]
+
+        col1, col2, col3, _col4 = st.columns([1, 1, 1, 2])
+
+        with col1:
+            if st.button("\U0001f4cb Copy", key=f"copy_{tab_group_id}_{selected_index}", use_container_width=True):
+                st.code(active_tab.get("content", ""), language="text")
+                st.success("Content displayed above - copy with Ctrl+A, Ctrl+C")
+
+        with col2:
+            content = active_tab.get("content", "")
+            if content:
+                st.download_button(
+                    label="\U0001f4be Download",
+                    data=content,
+                    file_name=f"{active_tab['title'].lower().replace(' ', '_')}.txt",
+                    mime="text/plain",
+                    key=f"download_{tab_group_id}_{selected_index}",
+                    use_container_width=True,
+                )
+
+        with col3:
+            if st.button("\U0001f4ca Stats", key=f"stats_{tab_group_id}_{selected_index}", use_container_width=True):
+                word_count = len(str(content).split())
+                char_count = len(str(content))
+                st.info(f"\U0001f4ca **{active_tab['title']}**: {word_count} words, {char_count} characters")
+
+        st.markdown('<div class="aurora-tab-content">', unsafe_allow_html=True)
+        create_enhanced_aurora_content_card(
+            title=active_tab["title"],
+            content=active_tab.get("content", ""),
+            content_type=active_tab.get("type", "text"),
+            icon=active_tab["icon"],
+        )
+        st.markdown("</div>", unsafe_allow_html=True)
+
+    return selected_index
+
+
+def show_results(results):
+    """Display generated content with Aurora styling and export options."""
+    if not results:
+        return
+
+    st.markdown(
+        """
+    <div class="aurora-results-header">
+        <h1 class="aurora-results-title">Content Generated Successfully!</h1>
+        <p class="aurora-results-subtitle">Your audio has been transformed with AI magic</p>
+    </div>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    notion_url = results.get("notion_url", "")
+    if notion_url and notion_url.startswith("https://"):
+        safe_url = html_mod.escape(notion_url)
+        st.markdown(
+            f"""
+        <div class="aurora-notion-link">
+            <a href="{safe_url}" target="_blank" class="aurora-notion-button">
+                View in Notion Workspace
+            </a>
+        </div>
+        """,
+            unsafe_allow_html=True,
+        )
+        st.markdown("---")
+
+    # Content overview stats
+    total_words = sum(
+        len(str(results.get(key, "")).split())
+        for key in ["transcript", "wisdom", "outline", "article", "social_content"]
+    )
+    content_types = len([k for k in ["transcript", "wisdom", "outline", "article", "social_content"] if results.get(k)])
+
+    st.markdown(
+        f"""
+    <div class="aurora-content-overview">
+        <div class="aurora-overview-stats">
+            <div class="aurora-stat-item">
+                <span class="aurora-stat-value">{total_words:,}</span>
+                <span class="aurora-stat-label">Total Words</span>
+            </div>
+            <div class="aurora-stat-item">
+                <span class="aurora-stat-value">{content_types}</span>
+                <span class="aurora-stat-label">Content Types</span>
+            </div>
+        </div>
+    </div>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    # Result sections
+    result_sections = [
+        ("transcript", "Transcript", results.get("transcript", "")),
+        ("wisdom", "Wisdom", results.get("wisdom", "")),
+        ("article", "Article", results.get("article", "")),
+        ("social_content", "Social Content", results.get("social_content", "")),
+    ]
+
+    has_content = False
+    for key, title, content in result_sections:
+        if not content:
+            continue
+        has_content = True
+        st.markdown(f'<div id="result-{key}"></div>', unsafe_allow_html=True)
+        word_count = len(str(content).split())
+        with st.expander(f"{title} ({word_count} words)", expanded=False):
+            col1, col2, _col3 = st.columns([1, 1, 3])
+            with col1:
+                if st.button("Copy", key=f"copy_{key}", use_container_width=True):
+                    st.code(content, language="text")
+                    st.success("Content displayed above - copy with Ctrl+A, Ctrl+C")
+            with col2:
+                st.download_button(
+                    label="Download",
+                    data=content,
+                    file_name=f"{key}.txt",
+                    mime="text/plain",
+                    key=f"download_{key}",
+                    use_container_width=True,
+                )
+            st.markdown(content)
+
+    if not has_content:
+        st.warning("No content available to display.")
+
+    # Export section
+    st.markdown("---")
+    st.markdown(
+        """
+    <div class="aurora-export-section">
+        <h3 class="aurora-export-title">Additional Export Options</h3>
+    </div>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        if st.button("Export as Text", use_container_width=True):
+            export_content = create_text_export(results)
+            st.download_button(
+                label="Download Text File",
+                data=export_content,
+                file_name=f"whisperforge_content_{datetime.now().strftime('%Y%m%d_%H%M')}.txt",
+                mime="text/plain",
+                key="download_text_export",
+                use_container_width=True,
+            )
+
+    with col2:
+        if st.button("Export as Markdown", use_container_width=True):
+            md_content = export_to_markdown(results)
+            st.download_button(
+                label="Download Markdown",
+                data=md_content,
+                file_name=f"whisperforge_content_{datetime.now().strftime('%Y%m%d_%H%M')}.md",
+                mime="text/markdown",
+                key="download_md_export",
+                use_container_width=True,
+            )
+
+    with col3:
+        try:
+            if st.button("Export as Word", use_container_width=True):
+                word_bytes = export_to_word(results)
+                st.download_button(
+                    label="Download Word",
+                    data=word_bytes,
+                    file_name=f"whisperforge_content_{datetime.now().strftime('%Y%m%d_%H%M')}.docx",
+                    mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+                    key="download_word_export",
+                    use_container_width=True,
+                )
+        except ImportError:
+            st.info("Install python-docx for Word export")
diff --git a/core/content_generation.py b/core/content_generation.py
index d8242b8..1600ac2 100644
--- a/core/content_generation.py
+++ b/core/content_generation.py
@@ -1,151 +1,244 @@
 """
 Content Generation Module for WhisperForge
-Handles AI-powered content creation including transcription, wisdom extraction, and content generation
+Handles AI-powered content creation including transcription, wisdom extraction, and content generation.
+Supports multiple AI providers: OpenAI, Anthropic, and Grok (xAI).
 """
 
 import logging
 import os
-from typing import Dict, Optional
 
-from .utils import get_openai_client, get_prompt, DEFAULT_PROMPTS, get_enhanced_prompt
+from .api_clients import get_anthropic_client, get_grok_client, get_openai_client
+from .constants import (
+    ARTICLE_PREVIEW_LENGTH,
+    MAX_TOKENS_ARTICLE,
+    MAX_TOKENS_DEFAULT,
+    TRANSCRIPT_PREVIEW_LENGTH,
+)
+from .exceptions import APIClientError, PipelineError
+from .prompts import get_enhanced_prompt
 
-# Configure logging
 logger = logging.getLogger(__name__)
 
-def generate_wisdom(transcript: str, custom_prompt: str = None, knowledge_base: Dict[str, str] = None) -> str:
-    """Extract key insights and wisdom from a transcript"""
-    try:
-        # Use enhanced prompt system with automatic KB concatenation
-        system_prompt = custom_prompt or get_enhanced_prompt("wisdom_extraction", knowledge_base)
-        
-        openai_client = get_openai_client()
-        if not openai_client:
-            return "Error: OpenAI API key is not configured."
-            
-        response = openai_client.chat.completions.create(
-            model="gpt-4o",
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": f"Here's the transcription to analyze:\n\n{transcript}"}
-            ],
-            max_tokens=1500
-        )
-        
-        return response.choices[0].message.content
-        
-    except Exception as e:
-        logger.exception("Error in wisdom generation:")
-        return f"Error generating wisdom: {str(e)}"
-
-def generate_outline(transcript: str, wisdom: str, custom_prompt: str = None, knowledge_base: Dict[str, str] = None) -> str:
-    """Create a structured outline based on transcript and wisdom"""
-    try:
-        # Use enhanced prompt system with automatic KB concatenation
-        system_prompt = custom_prompt or get_enhanced_prompt("outline_creation", knowledge_base)
-        
-        content = f"TRANSCRIPT:\n{transcript}\n\nWISDOM:\n{wisdom}"
-        
-        openai_client = get_openai_client()
-        if not openai_client:
-            return "Error: OpenAI API key is not configured."
-            
-        response = openai_client.chat.completions.create(
-            model="gpt-4o",
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": content}
-            ],
-            max_tokens=1500
-        )
-        
-        return response.choices[0].message.content
-        
-    except Exception as e:
-        logger.exception("Error in outline generation:")
-        return f"Error generating outline: {str(e)}"
-
-def generate_article(transcript: str, wisdom: str, outline: str, custom_prompt: str = None, knowledge_base: Dict[str, str] = None) -> str:
-    """Generate a comprehensive article based on transcript, wisdom, and outline"""
+WHISPER_MODEL = os.getenv("WHISPER_MODEL", "whisper-1")
+
+DEFAULT_MODELS = {
+    "openai": os.getenv("GPT_MODEL", "gpt-4o"),
+    "anthropic": os.getenv("ANTHROPIC_MODEL", "claude-sonnet-4-20250514"),
+    "grok": os.getenv("GROK_MODEL", "grok-3"),
+}
+
+# Provider priority for fallback ordering
+_PROVIDER_PRIORITY = ("openai", "anthropic", "grok")
+
+# Map provider names to their client factory functions
+_CLIENT_FACTORIES = {
+    "openai": get_openai_client,
+    "anthropic": get_anthropic_client,
+    "grok": get_grok_client,
+}
+
+
+class ContentGenerationError(PipelineError):
+    """Raised when content generation fails"""
+
+    pass
+
+
+def _resolve_provider() -> str:
+    """Determine the active AI provider.
+
+    Checks ``st.session_state.ai_provider`` first (if Streamlit is running),
+    then falls back to the first provider with a configured API key in
+    priority order: openai > anthropic > grok.
+
+    Raises ``ContentGenerationError`` if no provider is available.
+    """
+    # Try Streamlit session state first
     try:
-        # Use enhanced prompt system with automatic KB concatenation
-        system_prompt = custom_prompt or get_enhanced_prompt("article_writing", knowledge_base)
-        
-        # Limit transcript length to avoid token limits
-        transcript_excerpt = transcript[:2000] if len(transcript) > 2000 else transcript
-        content = f"TRANSCRIPT:\n{transcript_excerpt}\n\nWISDOM:\n{wisdom}\n\nOUTLINE:\n{outline}"
-        
-        openai_client = get_openai_client()
-        if not openai_client:
-            return "Error: OpenAI API key is not configured."
-            
-        response = openai_client.chat.completions.create(
-            model="gpt-4o",
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": content}
-            ],
-            max_tokens=2000
-        )
-        
-        return response.choices[0].message.content
-        
-    except Exception as e:
-        logger.exception("Error in article generation:")
-        return f"Error generating article: {str(e)}"
-
-def generate_social_content(wisdom: str, outline: str, article: str, custom_prompt: str = None, knowledge_base: Dict[str, str] = None) -> str:
-    """Generate 5 distinct social media posts"""
+        import streamlit as st
+
+        preferred = st.session_state.get("ai_provider")
+        if preferred and _CLIENT_FACTORIES.get(preferred):
+            client = _CLIENT_FACTORIES[preferred]()
+            if client is not None:
+                return preferred
+            logger.warning("Preferred provider '%s' is not configured (missing API key). Trying fallbacks.", preferred)
+    except (ImportError, RuntimeError):
+        # Not running in Streamlit context
+        pass
+
+    # Fallback: first available provider
+    for provider in _PROVIDER_PRIORITY:
+        client = _CLIENT_FACTORIES[provider]()
+        if client is not None:
+            return provider
+
+    raise ContentGenerationError(
+        "No AI provider is configured. Set at least one of: OPENAI_API_KEY, ANTHROPIC_API_KEY, or GROK_API_KEY."
+    )
+
+
+def _call_openai_compatible(provider: str, system_prompt: str, user_content: str, max_tokens: int, model: str) -> str:
+    """Make a chat completion call using the OpenAI SDK shape.
+
+    Works for both OpenAI and Grok (xAI), since Grok exposes an
+    OpenAI-compatible API.
+    """
+    client = _CLIENT_FACTORIES[provider]()
+    if not client:
+        raise ContentGenerationError(f"{provider} client is not configured.")
+
+    response = client.chat.completions.create(
+        model=model,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_content},
+        ],
+        max_tokens=max_tokens,
+    )
+    return response.choices[0].message.content
+
+
+def _call_anthropic(system_prompt: str, user_content: str, max_tokens: int, model: str) -> str:
+    """Make a chat completion call using the Anthropic SDK."""
+    client = get_anthropic_client()
+    if not client:
+        raise ContentGenerationError("Anthropic client is not configured.")
+
+    response = client.messages.create(
+        model=model,
+        max_tokens=max_tokens,
+        system=system_prompt,
+        messages=[{"role": "user", "content": user_content}],
+    )
+    return response.content[0].text
+
+
+def _call_provider(provider: str, system_prompt: str, user_content: str, max_tokens: int) -> str:
+    """Dispatch a chat completion call to the appropriate provider backend."""
+    model = DEFAULT_MODELS[provider]
+
+    # Check for a session-state model override
     try:
-        # Use enhanced prompt system with automatic KB concatenation
-        system_prompt = custom_prompt or get_enhanced_prompt("social_media", knowledge_base)
-        
-        # Include article in content for richer context
-        article_excerpt = article[:1500] if len(article) > 1500 else article
-        content = f"WISDOM:\n{wisdom}\n\nOUTLINE:\n{outline}\n\nARTICLE:\n{article_excerpt}"
-        
-        openai_client = get_openai_client()
-        if not openai_client:
-            return "Error: OpenAI API key is not configured."
-            
-        response = openai_client.chat.completions.create(
-            model="gpt-4o",
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": content}
-            ],
-            max_tokens=1500
+        import streamlit as st
+
+        model_key = f"{provider}_model"
+        override = st.session_state.get(model_key)
+        if override:
+            model = override
+    except (ImportError, RuntimeError):
+        pass
+
+    if provider == "anthropic":
+        return _call_anthropic(system_prompt, user_content, max_tokens, model)
+    else:
+        # Both OpenAI and Grok use OpenAI-compatible SDK
+        return _call_openai_compatible(provider, system_prompt, user_content, max_tokens, model)
+
+
+def _chat_completion(system_prompt: str, user_content: str, max_tokens: int = MAX_TOKENS_DEFAULT) -> str:
+    """Run a chat completion with automatic provider resolution and fallback.
+
+    Resolves the preferred provider, attempts the call, and on failure
+    tries remaining providers in priority order before raising.
+    """
+    primary = _resolve_provider()
+
+    # Build ordered list: primary first, then remaining providers
+    providers_to_try = [primary] + [p for p in _PROVIDER_PRIORITY if p != primary]
+
+    last_error = None
+    for provider in providers_to_try:
+        # Skip providers that aren't configured
+        client = _CLIENT_FACTORIES[provider]()
+        if client is None:
+            continue
+
+        try:
+            return _call_provider(provider, system_prompt, user_content, max_tokens)
+        except ContentGenerationError:
+            raise
+        except (APIClientError, OSError) as e:
+            logger.warning("Provider '%s' failed: %s. Trying next provider.", provider, e)
+            last_error = e
+
+    raise ContentGenerationError(f"All AI providers failed. Last error: {last_error}")
+
+
+# ---------------------------------------------------------------------------
+# Whisper transcription (OpenAI-only)
+# ---------------------------------------------------------------------------
+
+
+def _get_whisper_client():
+    """Get an OpenAI client for Whisper transcription.
+
+    Whisper is only available through the OpenAI API, so this always
+    returns an OpenAI client or raises.
+    """
+    client = get_openai_client()
+    if not client:
+        raise ContentGenerationError(
+            "OpenAI API key is not configured. Whisper transcription requires an OpenAI API key."
         )
-        
-        return response.choices[0].message.content
-        
-    except Exception as e:
-        logger.exception("Error in social content generation:")
-        return f"Error generating social content: {str(e)}"
+    return client
+
 
 def transcribe_audio(audio_file) -> str:
     """Transcribe audio using OpenAI Whisper - handles both file paths and file objects"""
-    try:
-        openai_client = get_openai_client()
-        if not openai_client:
-            return "Error: OpenAI client not available."
-        
-        # Handle both file paths (strings) and file objects
-        if isinstance(audio_file, str):
-            # It's a file path, open it
-            with open(audio_file, 'rb') as f:
-                response = openai_client.audio.transcriptions.create(
-                    model="whisper-1",
-                    file=f
-                )
-        else:
-            # It's a file object, reset pointer and use directly
-            audio_file.seek(0)
-            response = openai_client.audio.transcriptions.create(
-                model="whisper-1",
-                file=audio_file
-            )
-        
-        return response.text
-        
-    except Exception as e:
-        return f"Transcription failed: {str(e)}" 
\ No newline at end of file
+    client = _get_whisper_client()
+
+    if isinstance(audio_file, str):
+        with open(audio_file, "rb") as f:
+            response = client.audio.transcriptions.create(model=WHISPER_MODEL, file=f)
+    else:
+        audio_file.seek(0)
+        response = client.audio.transcriptions.create(model=WHISPER_MODEL, file=audio_file)
+
+    return response.text
+
+
+# ---------------------------------------------------------------------------
+# Content generation functions
+# ---------------------------------------------------------------------------
+
+
+def generate_wisdom(transcript: str, custom_prompt: str = None, knowledge_base: dict[str, str] = None) -> str:
+    """Extract key insights and wisdom from a transcript"""
+    system_prompt = custom_prompt or get_enhanced_prompt("wisdom_extraction", knowledge_base)
+    return _chat_completion(system_prompt, f"Here's the transcription to analyze:\n\n{transcript}")
+
+
+def generate_outline(
+    transcript: str, wisdom: str, custom_prompt: str = None, knowledge_base: dict[str, str] = None
+) -> str:
+    """Create a structured outline based on transcript and wisdom"""
+    system_prompt = custom_prompt or get_enhanced_prompt("outline_creation", knowledge_base)
+    return _chat_completion(system_prompt, f"TRANSCRIPT:\n{transcript}\n\nWISDOM:\n{wisdom}")
+
+
+def generate_article(
+    transcript: str, wisdom: str, outline: str, custom_prompt: str = None, knowledge_base: dict[str, str] = None
+) -> str:
+    """Generate a comprehensive article based on transcript, wisdom, and outline"""
+    system_prompt = custom_prompt or get_enhanced_prompt("article_writing", knowledge_base)
+    transcript_excerpt = (
+        transcript[:TRANSCRIPT_PREVIEW_LENGTH] if len(transcript) > TRANSCRIPT_PREVIEW_LENGTH else transcript
+    )
+    return _chat_completion(
+        system_prompt,
+        f"TRANSCRIPT:\n{transcript_excerpt}\n\nWISDOM:\n{wisdom}\n\nOUTLINE:\n{outline}",
+        max_tokens=MAX_TOKENS_ARTICLE,
+    )
+
+
+def generate_social_content(
+    wisdom: str, outline: str, article: str, custom_prompt: str = None, knowledge_base: dict[str, str] = None
+) -> str:
+    """Generate 5 distinct social media posts"""
+    system_prompt = custom_prompt or get_enhanced_prompt("social_media", knowledge_base)
+    article_excerpt = article[:ARTICLE_PREVIEW_LENGTH] if len(article) > ARTICLE_PREVIEW_LENGTH else article
+    return _chat_completion(
+        system_prompt,
+        f"WISDOM:\n{wisdom}\n\nOUTLINE:\n{outline}\n\nARTICLE:\n{article_excerpt}",
+    )
diff --git a/core/exceptions.py b/core/exceptions.py
new file mode 100644
index 0000000..5f2e5d3
--- /dev/null
+++ b/core/exceptions.py
@@ -0,0 +1,30 @@
+"""
+WhisperForge custom exception hierarchy.
+
+All project-specific exceptions inherit from WhisperForgeError so callers
+can catch the entire family if needed.
+"""
+
+
+class WhisperForgeError(Exception):
+    """Base exception for all WhisperForge errors."""
+
+
+class DatabaseError(WhisperForgeError):
+    """Raised when a Supabase / database operation fails."""
+
+
+class AuthenticationError(WhisperForgeError):
+    """Raised when authentication or session operations fail."""
+
+
+class PipelineError(WhisperForgeError):
+    """Raised when a pipeline step fails."""
+
+
+class FileProcessingError(WhisperForgeError):
+    """Raised when file upload or audio processing fails."""
+
+
+class APIClientError(WhisperForgeError):
+    """Raised when an external API call fails."""
diff --git a/core/export.py b/core/export.py
new file mode 100644
index 0000000..33907bf
--- /dev/null
+++ b/core/export.py
@@ -0,0 +1,143 @@
+"""
+Content Export Utilities
+========================
+
+Export generated content to various file formats (Text, Markdown, Word, PDF).
+"""
+
+from datetime import datetime
+from io import BytesIO
+
+
+def create_text_export(results: dict) -> str:
+    """Create a formatted plain-text export of all content."""
+    lines = [
+        "=" * 60,
+        "WHISPERFORGE CONTENT EXPORT",
+        f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
+        "=" * 60,
+        "",
+    ]
+
+    sections = [
+        ("AUDIO TRANSCRIPT", results.get("transcript", "")),
+        ("EXTRACTED WISDOM", results.get("wisdom", "")),
+        ("CONTENT OUTLINE", results.get("outline", "")),
+        ("FULL ARTICLE", results.get("article", "")),
+        ("SOCIAL MEDIA CONTENT", results.get("social_content", "")),
+    ]
+
+    for title, content in sections:
+        if content:
+            lines.extend([f"## {title}", "-" * 40, content, "", ""])
+
+    if results.get("notion_url"):
+        lines.extend(["## NOTION LINK", "-" * 40, results["notion_url"], ""])
+
+    return "\n".join(lines)
+
+
+def export_to_markdown(results: dict) -> str:
+    """Export results to Markdown format."""
+    lines = ["# WhisperForge Content Export"]
+    sections = [
+        ("## Transcript", results.get("transcript", "")),
+        ("## Wisdom", results.get("wisdom", "")),
+        ("## Outline", results.get("outline", "")),
+        ("## Article", results.get("article", "")),
+        ("## Social Content", results.get("social_content", "")),
+    ]
+    for title, content in sections:
+        if content:
+            lines.extend([title, "", content, ""])
+    if results.get("notion_url"):
+        lines.extend(["## Notion Link", results["notion_url"]])
+    return "\n".join(lines)
+
+
+def export_to_word(results: dict) -> bytes:
+    """Export results to a Word document (.docx)."""
+    from docx import Document
+
+    doc = Document()
+    doc.add_heading("WhisperForge Content Export", level=1)
+    for title, content in [
+        ("Transcript", results.get("transcript", "")),
+        ("Wisdom", results.get("wisdom", "")),
+        ("Outline", results.get("outline", "")),
+        ("Article", results.get("article", "")),
+        ("Social Content", results.get("social_content", "")),
+    ]:
+        if content:
+            doc.add_heading(title, level=2)
+            doc.add_paragraph(content)
+    if results.get("notion_url"):
+        doc.add_heading("Notion Link", level=2)
+        doc.add_paragraph(results["notion_url"])
+
+    bio = BytesIO()
+    doc.save(bio)
+    bio.seek(0)
+    return bio.read()
+
+
+def export_to_pdf(results: dict) -> bytes:
+    """Export results to a PDF file."""
+    from fpdf import FPDF
+
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    pdf.set_font("Arial", size=12)
+    text = create_text_export(results)
+    for line in text.split("\n"):
+        pdf.cell(0, 10, txt=line, ln=1)
+    return pdf.output(dest="S").encode("latin-1")
+
+
+def create_json_download(results: dict) -> str:
+    """Create JSON format download of streaming results."""
+    import json
+
+    return json.dumps(results, indent=2, ensure_ascii=False)
+
+
+def create_markdown_download(results: dict) -> str:
+    """Create Markdown format download of streaming results."""
+    content = "# WhisperForge Content Generation Results\n\n"
+
+    sections = {
+        "transcription": "## Audio Transcription\n\n",
+        "wisdom_extraction": "## Key Insights & Wisdom\n\n",
+        "outline_creation": "## Content Outline\n\n",
+        "article_creation": "## Full Article\n\n",
+        "social_content": "## Social Media Content\n\n",
+        "image_prompts": "## Image Generation Prompts\n\n",
+    }
+
+    for key, header in sections.items():
+        if key in results:
+            content += header + results[key] + "\n\n---\n\n"
+
+    return content
+
+
+def create_text_download(results: dict) -> str:
+    """Create plain text format download of streaming results."""
+    content = "WHISPERFORGE CONTENT GENERATION RESULTS\n"
+    content += "=" * 50 + "\n\n"
+
+    sections = {
+        "transcription": "AUDIO TRANSCRIPTION\n" + "-" * 20 + "\n\n",
+        "wisdom_extraction": "KEY INSIGHTS & WISDOM\n" + "-" * 20 + "\n\n",
+        "outline_creation": "CONTENT OUTLINE\n" + "-" * 15 + "\n\n",
+        "article_creation": "FULL ARTICLE\n" + "-" * 12 + "\n\n",
+        "social_content": "SOCIAL MEDIA CONTENT\n" + "-" * 20 + "\n\n",
+        "image_prompts": "IMAGE GENERATION PROMPTS\n" + "-" * 25 + "\n\n",
+    }
+
+    for key, header in sections.items():
+        if key in results:
+            content += header + results[key] + "\n\n" + "=" * 50 + "\n\n"
+
+    return content
diff --git a/core/file_upload.py b/core/file_upload.py
index 374e48f..8ed4657 100644
--- a/core/file_upload.py
+++ b/core/file_upload.py
@@ -1,46 +1,67 @@
 """
-Enhanced File Upload Module for WhisperForge v3.0.0
-Supports large file processing up to 2GB with intelligent chunking and parallel transcription
+File Upload Module
+==================
+
+Upload zone UI, progress indicators, and pydub-based chunked processing
+for standard audio files. For FFmpeg-based large file processing (2GB+),
+see large_file_processor.py.
 """
 
-import asyncio
 import logging
 import math
-import mimetypes
 import os
 import tempfile
-import threading
 import time
 from concurrent.futures import ThreadPoolExecutor, as_completed
-from typing import Optional, List, Dict, Any, Tuple
 
+import openai
 import streamlit as st
 
-# Configure logging
+from .constants import CHUNK_SUCCESS_THRESHOLD_STANDARD, MAX_PARALLEL_CHUNKS_STANDARD, MAX_UPLOAD_SIZE_BYTES
+from .content_generation import ContentGenerationError
+from .exceptions import APIClientError, FileProcessingError
+from .large_file_processor import EnhancedLargeFileProcessor  # noqa: F401 - re-export
+
 logger = logging.getLogger(__name__)
 
+_UPLOAD_CSS_CACHE = None
+
+
+def _load_upload_css():
+    """Load upload CSS from static file (cached)."""
+    global _UPLOAD_CSS_CACHE  # noqa: PLW0603
+    if _UPLOAD_CSS_CACHE is None:
+        css_path = os.path.join("static", "css", "upload.css")
+        if os.path.exists(css_path):
+            with open(css_path, encoding="utf-8") as f:
+                _UPLOAD_CSS_CACHE = f"<style>{f.read()}</style>"
+        else:
+            _UPLOAD_CSS_CACHE = ""
+    return _UPLOAD_CSS_CACHE
+
+
 class FileUploadManager:
-    """🚀 ENHANCED: Large file upload manager with chunking and parallel processing"""
-    
+    """Large file upload manager with chunking and parallel processing."""
+
     def __init__(self):
         self.supported_formats = {
-            'audio': ['.mp3', '.wav', '.m4a', '.aac', '.ogg', '.flac', '.wma', '.webm', '.mpeg', '.mpga', '.oga'],
-            'video': ['.mp4', '.avi', '.mov', '.mkv', '.wmv', '.flv', '.webm'],
-            'text': ['.txt', '.md', '.pdf', '.docx']
+            "audio": [".mp3", ".wav", ".m4a", ".aac", ".ogg", ".flac", ".wma", ".webm", ".mpeg", ".mpga", ".oga"],
+            "video": [".mp4", ".avi", ".mov", ".mkv", ".wmv", ".flv", ".webm"],
+            "text": [".txt", ".md", ".pdf", ".docx"],
         }
-        self.max_file_size = 2 * 1024 * 1024 * 1024  # 2GB
-        self.chunk_size_mb = 20  # 20MB chunks for optimal processing
-        self.max_parallel_chunks = 4  # Process 4 chunks simultaneously
-        
-    def create_large_file_upload_zone(self) -> Optional[Any]:
-        """Create enhanced upload zone for large files"""
-        
-        # Enhanced upload zone HTML with large file support
-        upload_html = f"""
+        from .config import get_config
+
+        self.max_file_size = MAX_UPLOAD_SIZE_BYTES
+        self.chunk_size_mb = get_config().audio_chunk_size_mb
+        self.max_parallel_chunks = MAX_PARALLEL_CHUNKS_STANDARD
+
+    def create_upload_zone(self):
+        """Create enhanced upload zone for large files."""
+        upload_html = """
         <div class="large-upload-zone-container">
             <div class="large-upload-zone" id="large-upload-zone">
                 <div class="upload-icon">
-                    <div class="upload-icon-inner">🎵</div>
+                    <div class="upload-icon-inner">\U0001f3b5</div>
                 </div>
                 <div class="upload-text">
                     <h3>Drop your large audio files here</h3>
@@ -53,499 +74,326 @@ def create_large_file_upload_zone(self) -> Optional[Any]:
                 </div>
                 <div class="upload-features">
                     <div class="feature">
-                        <span class="feature-icon">⚡</span>
+                        <span class="feature-icon">\u26a1</span>
                         <span>Parallel Processing</span>
                     </div>
                     <div class="feature">
-                        <span class="feature-icon">📊</span>
+                        <span class="feature-icon">\U0001f4ca</span>
                         <span>Real-time Progress</span>
                     </div>
                     <div class="feature">
-                        <span class="feature-icon">🔄</span>
+                        <span class="feature-icon">\U0001f504</span>
                         <span>Auto-retry on Errors</span>
                     </div>
                 </div>
             </div>
         </div>
         """
-        
-        # Enhanced CSS for large file upload
-        upload_css = """
-        <style>
-        .large-upload-zone-container {
-            margin: 20px 0;
-        }
-        
-        .large-upload-zone {
-            border: 3px dashed rgba(0, 255, 255, 0.3);
-            border-radius: 16px;
-            padding: 50px 30px;
-            text-align: center;
-            background: linear-gradient(135deg, 
-                rgba(0, 255, 255, 0.03) 0%, 
-                rgba(64, 224, 208, 0.05) 100%);
-            transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
-            cursor: pointer;
-            position: relative;
-            overflow: hidden;
-        }
-        
-        .large-upload-zone:hover {
-            border-color: rgba(0, 255, 255, 0.6);
-            background: linear-gradient(135deg, 
-                rgba(0, 255, 255, 0.08) 0%, 
-                rgba(64, 224, 208, 0.12) 100%);
-            transform: translateY(-3px);
-            box-shadow: 0 12px 35px rgba(0, 255, 255, 0.2);
-        }
-        
-        .large-upload-zone::before {
-            content: "";
-            position: absolute;
-            top: 0;
-            left: -100%;
-            width: 100%;
-            height: 100%;
-            background: linear-gradient(90deg, 
-                transparent, 
-                rgba(0, 255, 255, 0.15), 
-                transparent);
-            transition: left 0.6s ease;
-        }
-        
-        .large-upload-zone:hover::before {
-            left: 100%;
-        }
-        
-        .upload-icon-inner {
-            font-size: 64px;
-            opacity: 0.8;
-            transition: all 0.4s ease;
-            display: inline-block;
-        }
-        
-        .large-upload-zone:hover .upload-icon-inner {
-            opacity: 1;
-            transform: scale(1.15) rotate(10deg);
-        }
-        
-        .upload-text h3 {
-            color: #00FFFF;
-            font-size: 1.5rem;
-            margin: 16px 0 8px 0;
-            font-weight: 600;
-        }
-        
-        .upload-text p {
-            color: rgba(255, 255, 255, 0.7);
-            margin: 0 0 20px 0;
-            font-size: 1rem;
-        }
-        
-        .upload-info {
-            display: flex;
-            justify-content: center;
-            gap: 15px;
-            flex-wrap: wrap;
-            margin-bottom: 20px;
-        }
-        
-        .supported-formats, .max-size, .chunk-info {
-            font-size: 0.85rem;
-            color: rgba(255, 255, 255, 0.6);
-            background: rgba(0, 255, 255, 0.1);
-            padding: 6px 12px;
-            border-radius: 6px;
-            border: 1px solid rgba(0, 255, 255, 0.2);
-        }
-        
-        .upload-features {
-            display: flex;
-            justify-content: center;
-            gap: 30px;
-            flex-wrap: wrap;
-        }
-        
-        .feature {
-            display: flex;
-            align-items: center;
-            gap: 8px;
-            color: rgba(255, 255, 255, 0.8);
-            font-size: 0.9rem;
-        }
-        
-        .feature-icon {
-            font-size: 1.2rem;
-        }
-        </style>
-        """
-        
-        st.markdown(upload_css, unsafe_allow_html=True)
+
+        st.markdown(_load_upload_css(), unsafe_allow_html=True)
         st.markdown(upload_html, unsafe_allow_html=True)
-        
-        # File uploader with large file support
+
         uploaded_file = st.file_uploader(
             "Choose an audio file",
-            type=['mp3', 'wav', 'm4a', 'aac', 'ogg', 'flac', 'wma', 'webm', 'mpeg', 'mpga', 'oga'],
+            type=["mp3", "wav", "m4a", "aac", "ogg", "flac", "wma", "webm", "mpeg", "mpga", "oga"],
             help="Upload audio files up to 2GB. Large files will be automatically chunked for optimal processing.",
-            label_visibility="collapsed"
+            label_visibility="collapsed",
         )
-        
+
         return uploaded_file
-    
-    def process_large_file(self, uploaded_file) -> Dict[str, Any]:
-        """🚀 Process large files with chunking and parallel transcription"""
-        
+
+    def process_large_file(self, uploaded_file) -> dict:
+        """Process large files with chunking and parallel transcription."""
         if not uploaded_file:
             return {"success": False, "error": "No file provided"}
-        
-        # Validate file
+
         validation = self.validate_large_file(uploaded_file)
         if not validation["valid"]:
             return {"success": False, "error": validation["error"]}
-        
+
         file_size_mb = len(uploaded_file.getvalue()) / (1024 * 1024)
-        
-        # Show file info
-        st.markdown(f"""
-        ### 📁 File Processing
-        **File:** {uploaded_file.name}  
-        **Size:** {file_size_mb:.1f} MB  
-        **Processing Strategy:** {"Chunked Parallel Processing" if file_size_mb > self.chunk_size_mb else "Direct Processing"}
-        """)
-        
+
+        st.markdown(
+            f"### \U0001f4c1 File Processing\n"
+            f"**File:** {uploaded_file.name}\n"
+            f"**Size:** {file_size_mb:.1f} MB\n"
+            f"**Processing Strategy:** {'Chunked Parallel Processing' if file_size_mb > self.chunk_size_mb else 'Direct Processing'}"
+        )
+
         if file_size_mb <= self.chunk_size_mb:
-            # Small file - process directly
             return self._process_small_file(uploaded_file)
         else:
-            # Large file - chunk and process in parallel
             return self._process_large_file_chunked(uploaded_file)
-    
-    def _process_small_file(self, uploaded_file) -> Dict[str, Any]:
-        """Process small files directly without chunking"""
-        
+
+    def _process_small_file(self, uploaded_file) -> dict:
+        """Process small files directly without chunking."""
         progress_container = st.empty()
-        
+
         with progress_container.container():
-            st.markdown("#### 🎵 Processing Audio")
+            st.markdown("#### \U0001f3b5 Processing Audio")
             progress_bar = st.progress(0.0, "Starting transcription...")
-            
+
             try:
-                # Import transcription function
                 from .content_generation import transcribe_audio
-                
-                # Update progress
+
                 progress_bar.progress(0.3, "Transcribing audio...")
-                
-                # Transcribe
                 transcript = transcribe_audio(uploaded_file)
-                
+
                 if not transcript or "Error" in transcript:
-                    progress_bar.progress(1.0, "❌ Transcription failed")
+                    progress_bar.progress(1.0, "\u274c Transcription failed")
                     return {"success": False, "error": transcript or "Transcription failed"}
-                
-                progress_bar.progress(1.0, "✅ Transcription complete!")
-                
-                return {
-                    "success": True,
-                    "transcript": transcript,
-                    "chunks": 1,
-                    "total_duration": "N/A"
-                }
-                
-            except Exception as e:
-                progress_bar.progress(1.0, f"❌ Error: {str(e)}")
+
+                progress_bar.progress(1.0, "\u2705 Transcription complete!")
+                return {"success": True, "transcript": transcript, "chunks": 1, "total_duration": "N/A"}
+
+            except (ContentGenerationError, openai.APIError, OSError) as e:
+                progress_bar.progress(1.0, f"\u274c Error: {e!s}")
                 return {"success": False, "error": str(e)}
-    
-    def _process_large_file_chunked(self, uploaded_file) -> Dict[str, Any]:
-        """🚀 Process large files with intelligent chunking and parallel transcription"""
-        
-        st.markdown("#### 🔄 Chunked Processing Pipeline")
-        
+            except (FileProcessingError, APIClientError) as e:
+                progress_bar.progress(1.0, f"\u274c Unexpected error: {e!s}")
+                return {"success": False, "error": str(e)}
+
+    def _process_large_file_chunked(self, uploaded_file) -> dict:
+        """Process large files with intelligent chunking and parallel transcription."""
+        st.markdown("#### \U0001f504 Chunked Processing Pipeline")
+
         try:
-            # Step 1: Create chunks
             chunks_info = self._create_audio_chunks(uploaded_file)
             if not chunks_info["success"]:
                 return chunks_info
-            
+
             chunks = chunks_info["chunks"]
             total_chunks = len(chunks)
-            
             st.markdown(f"**Created {total_chunks} chunks for parallel processing**")
-            
-            # Step 2: Create progress tracking containers
+
             progress_container = st.empty()
             chunks_container = st.empty()
-            
-            # Step 3: Process chunks in parallel with real-time updates
-            transcription_results = self._transcribe_chunks_parallel(
-                chunks, progress_container, chunks_container
-            )
-            
+
+            transcription_results = self._transcribe_chunks_parallel(chunks, progress_container, chunks_container)
+
             if not transcription_results["success"]:
                 return transcription_results
-            
-            # Step 4: Reassemble transcript
+
             final_transcript = self._reassemble_transcript(transcription_results["chunk_transcripts"])
-            
-            # Step 5: Cleanup temporary files
             self._cleanup_chunks(chunks)
-            
-            # Success!
+
             with progress_container.container():
-                st.success("✅ Large file processing complete!")
-                st.markdown(f"""
-                **Processing Summary:**
-                - Total chunks: {total_chunks}
-                - Successful transcriptions: {len(transcription_results['chunk_transcripts'])}
-                - Final transcript length: {len(final_transcript)} characters
-                """)
-            
+                st.success("\u2705 Large file processing complete!")
+                st.markdown(
+                    f"**Processing Summary:**\n"
+                    f"- Total chunks: {total_chunks}\n"
+                    f"- Successful transcriptions: {len(transcription_results['chunk_transcripts'])}\n"
+                    f"- Final transcript length: {len(final_transcript)} characters"
+                )
+
             return {
                 "success": True,
                 "transcript": final_transcript,
                 "chunks": total_chunks,
-                "processing_time": transcription_results.get("total_time", "N/A")
+                "processing_time": transcription_results.get("total_time", "N/A"),
             }
-            
-        except Exception as e:
+
+        except (ContentGenerationError, openai.APIError, OSError) as e:
             logger.exception("Error in large file processing:")
-            st.error(f"❌ Large file processing failed: {str(e)}")
+            st.error(f"\u274c Large file processing failed: {e!s}")
             return {"success": False, "error": str(e)}
-    
-    def _create_audio_chunks(self, uploaded_file) -> Dict[str, Any]:
-        """Create audio chunks for parallel processing"""
-        
+        except (FileProcessingError, APIClientError) as e:
+            logger.exception("Unexpected error in large file processing:")
+            st.error(f"\u274c Unexpected large file processing error: {e!s}")
+            return {"success": False, "error": str(e)}
+
+    def _create_audio_chunks(self, uploaded_file) -> dict:
+        """Create audio chunks for parallel processing."""
         try:
-            st.markdown("##### 📂 Creating Audio Chunks...")
-            
-            # Save uploaded file temporarily
+            from pydub import AudioSegment
+
+            st.markdown("##### \U0001f4c2 Creating Audio Chunks...")
+
             with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as temp_file:
                 uploaded_file.seek(0)
                 temp_file.write(uploaded_file.read())
                 temp_file_path = temp_file.name
-            
-            # Load audio with pydub
+
             audio = AudioSegment.from_file(temp_file_path)
             duration_ms = len(audio)
             duration_minutes = duration_ms / (1000 * 60)
-            
-            # Calculate chunk duration (aim for ~20MB chunks)
-            chunk_duration_ms = self.chunk_size_mb * 60 * 1000  # Convert MB to minutes to ms
+
+            chunk_duration_ms = self.chunk_size_mb * 60 * 1000
             num_chunks = math.ceil(duration_ms / chunk_duration_ms)
-            
+
             st.markdown(f"**Audio Duration:** {duration_minutes:.1f} minutes")
-            st.markdown(f"**Creating {num_chunks} chunks of ~{chunk_duration_ms/60000:.1f} minutes each**")
-            
+            st.markdown(f"**Creating {num_chunks} chunks of ~{chunk_duration_ms / 60000:.1f} minutes each**")
+
             chunks = []
             chunk_progress = st.progress(0.0, "Creating chunks...")
-            
+
             for i in range(num_chunks):
                 start_ms = i * chunk_duration_ms
                 end_ms = min((i + 1) * chunk_duration_ms, duration_ms)
-                
-                # Extract chunk
+
                 chunk = audio[start_ms:end_ms]
-                
-                # Save chunk to temporary file
-                chunk_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
-                chunk.export(chunk_file.name, format="wav")
-                
-                chunks.append({
-                    "index": i,
-                    "file_path": chunk_file.name,
-                    "start_time": start_ms / 1000,
-                    "end_time": end_ms / 1000,
-                    "duration": (end_ms - start_ms) / 1000
-                })
-                
-                # Update progress
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as chunk_file:
+                    chunk.export(chunk_file.name, format="wav")
+
+                chunks.append(
+                    {
+                        "index": i,
+                        "file_path": chunk_file.name,
+                        "start_time": start_ms / 1000,
+                        "end_time": end_ms / 1000,
+                        "duration": (end_ms - start_ms) / 1000,
+                    }
+                )
+
                 progress = (i + 1) / num_chunks
                 chunk_progress.progress(progress, f"Created chunk {i + 1}/{num_chunks}")
-            
-            # Cleanup original temp file
+
             os.unlink(temp_file_path)
-            
-            chunk_progress.progress(1.0, f"✅ Created {num_chunks} chunks successfully!")
-            
+            chunk_progress.progress(1.0, f"\u2705 Created {num_chunks} chunks successfully!")
+
             return {"success": True, "chunks": chunks}
-            
-        except Exception as e:
+
+        except OSError as e:
             logger.exception("Error creating audio chunks:")
-            return {"success": False, "error": f"Failed to create chunks: {str(e)}"}
-    
-    def _transcribe_chunks_parallel(self, chunks: List[Dict], progress_container, chunks_container) -> Dict[str, Any]:
-        """🚀 Transcribe chunks in parallel with real-time progress tracking"""
-        
+            return {"success": False, "error": f"Failed to create chunks: {e!s}"}
+        except FileProcessingError as e:
+            logger.exception("Unexpected error creating audio chunks:")
+            return {"success": False, "error": f"Unexpected error creating chunks: {e!s}"}
+
+    def _transcribe_chunks_parallel(self, chunks: list[dict], progress_container, chunks_container) -> dict:
+        """Transcribe chunks in parallel with real-time progress tracking."""
         total_chunks = len(chunks)
         completed_chunks = 0
         chunk_transcripts = {}
         chunk_statuses = {i: "waiting" for i in range(total_chunks)}
         start_time = time.time()
-        
-        # Import transcription function
+
         from .content_generation import get_openai_client
-        
-        def transcribe_single_chunk(chunk_info: Dict) -> Tuple[int, str, bool]:
-            """Transcribe a single chunk"""
+
+        def transcribe_single_chunk(chunk_info: dict) -> tuple[int, str, bool]:
+            """Transcribe a single chunk."""
             try:
                 chunk_index = chunk_info["index"]
                 chunk_file_path = chunk_info["file_path"]
-                
-                # Update status to processing
                 chunk_statuses[chunk_index] = "processing"
-                
-                # Get OpenAI client
+
                 openai_client = get_openai_client()
                 if not openai_client:
                     return chunk_index, "Error: OpenAI API key not configured", False
-                
-                # Transcribe chunk
+
                 with open(chunk_file_path, "rb") as audio_file:
-                    transcript = openai_client.audio.transcriptions.create(
-                        model="whisper-1",
-                        file=audio_file
-                    )
-                
+                    transcript = openai_client.audio.transcriptions.create(model="whisper-1", file=audio_file)
+
                 chunk_statuses[chunk_index] = "completed"
                 return chunk_index, transcript.text, True
-                
-            except Exception as e:
+
+            except (openai.APIError, OSError) as e:
                 chunk_statuses[chunk_index] = "error"
                 logger.exception(f"Error transcribing chunk {chunk_index}:")
-                return chunk_index, f"Error: {str(e)}", False
-        
-        # Process chunks in parallel
+                return chunk_index, f"Error: {e!s}", False
+            except APIClientError as e:
+                chunk_statuses[chunk_index] = "error"
+                logger.exception(f"Unexpected error transcribing chunk {chunk_index}:")
+                return chunk_index, f"Unexpected error: {e!s}", False
+
         with ThreadPoolExecutor(max_workers=self.max_parallel_chunks) as executor:
-            # Submit all chunks for processing
-            future_to_chunk = {
-                executor.submit(transcribe_single_chunk, chunk): chunk["index"] 
-                for chunk in chunks
-            }
-            
-            # Monitor progress in real-time
+            future_to_chunk = {executor.submit(transcribe_single_chunk, chunk): chunk["index"] for chunk in chunks}
+
             while completed_chunks < total_chunks:
-                # Update progress display
                 with progress_container.container():
                     overall_progress = completed_chunks / total_chunks
                     st.progress(overall_progress, f"Transcribing chunks: {completed_chunks}/{total_chunks}")
-                
-                # Update individual chunk statuses
+
                 with chunks_container.container():
-                    st.markdown("##### 🧩 Chunk Processing Status")
-                    
-                    # Create columns for chunk status display
+                    st.markdown("##### \U0001f9e9 Chunk Processing Status")
                     cols_per_row = 4
                     rows = math.ceil(total_chunks / cols_per_row)
-                    
+
                     for row in range(rows):
                         cols = st.columns(cols_per_row)
                         for col_idx in range(cols_per_row):
                             chunk_idx = row * cols_per_row + col_idx
                             if chunk_idx < total_chunks:
                                 status = chunk_statuses[chunk_idx]
-                                
-                                if status == "waiting":
-                                    icon, color, text = "⏳", "#FFA500", "Waiting"
-                                elif status == "processing":
-                                    icon, color, text = "🔄", "#00BFFF", "Processing"
-                                elif status == "completed":
-                                    icon, color, text = "✅", "#00FF7F", "Complete"
-                                else:  # error
-                                    icon, color, text = "❌", "#FF6B6B", "Error"
-                                
+                                status_map = {
+                                    "waiting": ("\u23f3", "#FFA500", "Waiting"),
+                                    "processing": ("\U0001f504", "#00BFFF", "Processing"),
+                                    "completed": ("\u2705", "#00FF7F", "Complete"),
+                                }
+                                icon, color, text = status_map.get(status, ("\u274c", "#FF6B6B", "Error"))
+
                                 with cols[col_idx]:
-                                    st.markdown(f"""
-                                    <div style="
-                                        text-align: center;
-                                        padding: 8px;
-                                        border-radius: 8px;
-                                        background: rgba(255, 255, 255, 0.05);
-                                        border: 1px solid {color}40;
-                                        margin: 4px 0;
-                                    ">
-                                        <div style="font-size: 1.2rem;">{icon}</div>
-                                        <div style="font-size: 0.8rem; color: {color};">Chunk {chunk_idx + 1}</div>
-                                        <div style="font-size: 0.7rem; color: rgba(255,255,255,0.7);">{text}</div>
-                                    </div>
-                                    """, unsafe_allow_html=True)
-                
-                # Check for completed futures
+                                    st.markdown(
+                                        f'<div style="text-align:center;padding:8px;border-radius:8px;'
+                                        f'background:rgba(255,255,255,0.05);border:1px solid {color}40;margin:4px 0">'
+                                        f'<div style="font-size:1.2rem">{icon}</div>'
+                                        f'<div style="font-size:0.8rem;color:{color}">Chunk {chunk_idx + 1}</div>'
+                                        f'<div style="font-size:0.7rem;color:rgba(255,255,255,0.7)">{text}</div></div>',
+                                        unsafe_allow_html=True,
+                                    )
+
                 for future in as_completed(future_to_chunk, timeout=1):
                     chunk_index, transcript, success = future.result()
-                    
                     if success:
                         chunk_transcripts[chunk_index] = transcript
-                    
                     completed_chunks += 1
                     break
-                
-                # Small delay to prevent excessive updates
+
                 time.sleep(0.5)
-        
-        # Final progress update
+
         with progress_container.container():
-            st.progress(1.0, f"✅ All chunks transcribed: {completed_chunks}/{total_chunks}")
-        
+            st.progress(1.0, f"\u2705 All chunks transcribed: {completed_chunks}/{total_chunks}")
+
         processing_time = time.time() - start_time
-        
-        # Check if we have enough successful transcriptions
         successful_chunks = len(chunk_transcripts)
-        if successful_chunks < total_chunks * 0.8:  # Require at least 80% success
-            return {
-                "success": False,
-                "error": f"Too many failed chunks: {successful_chunks}/{total_chunks} successful"
-            }
-        
+
+        if successful_chunks < total_chunks * CHUNK_SUCCESS_THRESHOLD_STANDARD:
+            return {"success": False, "error": f"Too many failed chunks: {successful_chunks}/{total_chunks} successful"}
+
         return {
             "success": True,
             "chunk_transcripts": chunk_transcripts,
             "total_time": f"{processing_time:.1f}s",
-            "success_rate": f"{successful_chunks}/{total_chunks}"
+            "success_rate": f"{successful_chunks}/{total_chunks}",
         }
-    
-    def _reassemble_transcript(self, chunk_transcripts: Dict[int, str]) -> str:
-        """Reassemble transcript from chunks in correct order"""
-        
-        # Sort chunks by index and concatenate
+
+    def _reassemble_transcript(self, chunk_transcripts: dict[int, str]) -> str:
+        """Reassemble transcript from chunks in correct order."""
         sorted_chunks = sorted(chunk_transcripts.items())
-        full_transcript = " ".join([transcript for _, transcript in sorted_chunks])
-        
-        return full_transcript
-    
-    def _cleanup_chunks(self, chunks: List[Dict]):
-        """Clean up temporary chunk files"""
+        return " ".join([transcript for _, transcript in sorted_chunks])
+
+    def _cleanup_chunks(self, chunks: list[dict]):
+        """Clean up temporary chunk files."""
         for chunk in chunks:
             try:
                 if os.path.exists(chunk["file_path"]):
                     os.unlink(chunk["file_path"])
-            except Exception as e:
+            except OSError as e:
                 logger.warning(f"Failed to cleanup chunk file {chunk['file_path']}: {e}")
-    
-    def validate_large_file(self, file) -> Dict[str, Any]:
-        """Validate large file upload"""
+
+    def validate_large_file(self, file) -> dict:
+        """Validate large file upload."""
         if not file:
             return {"valid": False, "error": "No file provided"}
-        
-        # Check file size
+
         file_size = len(file.getvalue())
         if file_size > self.max_file_size:
             size_gb = file_size / (1024 * 1024 * 1024)
             return {"valid": False, "error": f"File too large: {size_gb:.1f}GB (max 2GB)"}
-        
-        # Check file type
+
         file_extension = os.path.splitext(file.name)[1].lower()
-        if file_extension not in self.supported_formats['audio']:
+        if file_extension not in self.supported_formats["audio"]:
             return {"valid": False, "error": f"Unsupported format: {file_extension}"}
-        
+
         return {"valid": True}
 
 
-# Create alias for backward compatibility
+# Backward compatibility alias
 LargeFileUploadManager = FileUploadManager
 
+
 def create_upload_progress_indicator(filename: str, progress: float = 0.0):
-    """Create a progress indicator for file upload"""
+    """Create a progress indicator for file upload."""
     progress_html = f"""
     <div class="upload-progress-container">
         <div class="upload-progress-header">
@@ -557,661 +405,22 @@ def create_upload_progress_indicator(filename: str, progress: float = 0.0):
             <div class="upload-progress-shimmer" style="width: {progress}%"></div>
         </div>
         <div class="upload-status">
-            {'Uploading...' if progress < 100 else 'Upload complete!'}
+            {"Uploading..." if progress < 100 else "Upload complete!"}
         </div>
     </div>
     """
-    
-    progress_css = """
-    <style>
-    .upload-progress-container {
-        background: var(--bg-secondary);
-        border-radius: var(--card-radius);
-        padding: 15px;
-        margin: 10px 0;
-        border: 1px solid rgba(121, 40, 202, 0.2);
-    }
-    
-    .upload-progress-header {
-        display: flex;
-        justify-content: space-between;
-        align-items: center;
-        margin-bottom: 8px;
-    }
-    
-    .upload-filename {
-        color: var(--text-primary);
-        font-weight: 500;
-        font-size: 0.9rem;
-    }
-    
-    .upload-percentage {
-        color: var(--accent-primary);
-        font-family: var(--terminal-font);
-        font-weight: 600;
-    }
-    
-    .upload-progress-bar {
-        height: 4px;
-        background: rgba(255, 255, 255, 0.1);
-        border-radius: 2px;
-        position: relative;
-        overflow: hidden;
-        margin-bottom: 8px;
-    }
-    
-    .upload-progress-fill {
-        height: 100%;
-        background: linear-gradient(90deg, #7928CA, #FF0080);
-        border-radius: 2px;
-        transition: width 0.3s ease;
-    }
-    
-    .upload-progress-shimmer {
-        position: absolute;
-        top: 0;
-        left: 0;
-        height: 100%;
-        background: linear-gradient(90deg, 
-            transparent, 
-            rgba(255, 255, 255, 0.2), 
-            transparent);
-        animation: shimmer 1.5s ease-in-out infinite;
-    }
-    
-    .upload-status {
-        color: var(--text-secondary);
-        font-size: 0.8rem;
-        text-align: center;
-    }
-    
-    @keyframes shimmer {
-        0% { transform: translateX(-100%); }
-        100% { transform: translateX(100%); }
-    }
-    </style>
-    """
-    
-    return st.markdown(progress_css + progress_html, unsafe_allow_html=True)
+
+    css = _load_upload_css()
+    return st.markdown(css + progress_html, unsafe_allow_html=True)
+
 
 def simulate_upload_progress(filename: str, duration: float = 2.0):
-    """Simulate upload progress for demonstration"""
+    """Simulate upload progress for demonstration."""
     progress_container = st.empty()
-    
+
     steps = 20
     for i in range(steps + 1):
         progress = (i / steps) * 100
-        
         with progress_container:
             create_upload_progress_indicator(filename, progress)
-        
-        if i < steps:
-            time.sleep(duration / steps)
-    
-    return True
-
-
-class EnhancedLargeFileProcessor:
-    """🚀 Enhanced Large File Processor with FFmpeg for 2GB+ files
-    
-    Features:
-    - FFmpeg-based processing for memory efficiency
-    - Support for files up to 2GB
-    - Intelligent 10-minute audio chunking
-    - Parallel transcription with ThreadPoolExecutor
-    - Memory-efficient streaming without loading entire files into RAM
-    - Enhanced error handling with automatic fallback
-    """
-    
-    def __init__(self):
-        self.supported_formats = {
-            'audio': ['.mp3', '.wav', '.m4a', '.aac', '.ogg', '.flac', '.wma', '.webm', '.mpeg', '.mpga', '.oga'],
-            'video': ['.mp4', '.avi', '.mov', '.mkv', '.wmv', '.flv', '.webm']  # Extract audio from video
-        }
-        self.max_file_size = 2 * 1024 * 1024 * 1024  # 2GB
-        self.chunk_duration_minutes = 10  # 10-minute chunks optimized for Whisper
-        self.max_parallel_chunks = 4  # Process 4 chunks simultaneously
-        self.temp_dir = None
-        
-    def check_ffmpeg_availability(self) -> bool:
-        """Check if FFmpeg is available on the system"""
-        try:
-            import subprocess
-            result = subprocess.run(['ffmpeg', '-version'], 
-                                  capture_output=True, text=True, timeout=5)
-            return result.returncode == 0
-        except (subprocess.TimeoutExpired, FileNotFoundError, Exception):
-            return False
-    
-    def get_audio_info(self, file_path: str) -> Dict[str, Any]:
-        """Get audio file information using ffprobe"""
-        try:
-            import subprocess
-            import json
-            
-            cmd = [
-                'ffprobe', '-v', 'quiet', '-print_format', 'json',
-                '-show_format', '-show_streams', file_path
-            ]
-            
-            result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
-            if result.returncode != 0:
-                return {"error": f"ffprobe failed: {result.stderr}"}
-            
-            data = json.loads(result.stdout)
-            format_info = data.get('format', {})
-            
-            # Find audio stream
-            audio_stream = None
-            for stream in data.get('streams', []):
-                if stream.get('codec_type') == 'audio':
-                    audio_stream = stream
-                    break
-            
-            if not audio_stream:
-                return {"error": "No audio stream found"}
-            
-            duration = float(format_info.get('duration', 0))
-            size = int(format_info.get('size', 0))
-            
-            return {
-                "duration": duration,
-                "size": size,
-                "format": format_info.get('format_name', 'unknown'),
-                "codec": audio_stream.get('codec_name', 'unknown'),
-                "sample_rate": int(audio_stream.get('sample_rate', 0)),
-                "channels": int(audio_stream.get('channels', 0))
-            }
-            
-        except Exception as e:
-            return {"error": f"Failed to get audio info: {str(e)}"}
-    
-    def validate_file(self, uploaded_file) -> Dict[str, Any]:
-        """Enhanced file validation for large files"""
-        if not uploaded_file:
-            return {"valid": False, "error": "No file provided"}
-        
-        # Check file size
-        file_size = len(uploaded_file.getvalue())
-        if file_size > self.max_file_size:
-            size_gb = file_size / (1024 * 1024 * 1024)
-            return {"valid": False, "error": f"File too large: {size_gb:.1f}GB (max 2GB)"}
-        
-        # Check file extension
-        file_extension = os.path.splitext(uploaded_file.name)[1].lower()
-        all_formats = self.supported_formats['audio'] + self.supported_formats['video']
-        if file_extension not in all_formats:
-            return {"valid": False, "error": f"Unsupported format: {file_extension}"}
-        
-        # Check FFmpeg availability for large files
-        if file_size > 100 * 1024 * 1024 and not self.check_ffmpeg_availability():  # 100MB+
-            return {
-                "valid": False, 
-                "error": "FFmpeg required for large files but not available. Please install FFmpeg."
-            }
-        
-        return {
-            "valid": True,
-            "size": file_size,
-            "size_mb": file_size / (1024 * 1024),
-            "requires_chunking": file_size > 100 * 1024 * 1024,  # Chunk files > 100MB
-            "format": file_extension
-        }
-    
-    def create_enhanced_upload_interface(self) -> Optional[Any]:
-        """Create enhanced upload interface for large files"""
-        
-        # Enhanced upload zone HTML
-        upload_html = f"""
-        <div class="enhanced-upload-container">
-            <div class="enhanced-upload-zone" id="enhanced-upload-zone">
-                <div class="upload-icon-large">
-                    <div class="upload-icon-inner">🎵</div>
-                    <div class="upload-pulse"></div>
-                </div>
-                <div class="upload-content">
-                    <h2>Enhanced Large File Upload</h2>
-                    <p class="upload-subtitle">Powered by FFmpeg • Up to 2GB • Intelligent Chunking</p>
-                    <div class="upload-features-grid">
-                        <div class="feature-card">
-                            <span class="feature-icon">⚡</span>
-                            <span class="feature-text">10-min chunks</span>
-                        </div>
-                        <div class="feature-card">
-                            <span class="feature-icon">🔄</span>
-                            <span class="feature-text">Parallel processing</span>
-                        </div>
-                        <div class="feature-card">
-                            <span class="feature-icon">💾</span>
-                            <span class="feature-text">Memory efficient</span>
-                        </div>
-                        <div class="feature-card">
-                            <span class="feature-icon">🎯</span>
-                            <span class="feature-text">Auto-retry</span>
-                        </div>
-                    </div>
-                    <div class="supported-formats-enhanced">
-                        <div class="format-group">
-                            <strong>Audio:</strong> MP3, WAV, M4A, AAC, OGG, FLAC, WEBM
-                        </div>
-                        <div class="format-group">
-                            <strong>Video:</strong> MP4, AVI, MOV, MKV, WMV (audio extraction)
-                        </div>
-                    </div>
-                </div>
-            </div>
-        </div>
-        """
-        
-        # Enhanced CSS
-        upload_css = """
-        <style>
-        .enhanced-upload-container {
-            margin: 25px 0;
-        }
-        
-        .enhanced-upload-zone {
-            border: 3px dashed var(--aurora-border);
-            border-radius: var(--aurora-radius-large);
-            padding: var(--aurora-spacing-large);
-            text-align: center;
-            background: var(--aurora-bg-glass);
-            transition: all 0.5s cubic-bezier(0.4, 0, 0.2, 1);
-            cursor: pointer;
-            position: relative;
-            overflow: hidden;
-        }
-        
-        .enhanced-upload-zone:hover {
-            border-color: var(--aurora-border-hover);
-            background: var(--aurora-bg-glass);
-            transform: translateY(-5px);
-            box-shadow: var(--aurora-glow);
-        }
-        
-        .upload-icon-large {
-            position: relative;
-            margin-bottom: 20px;
-        }
-        
-        .upload-icon-inner {
-            font-size: 80px;
-            color: var(--aurora-primary);
-            opacity: 0.9;
-            transition: all 0.5s ease;
-            display: inline-block;
-            position: relative;
-            z-index: 2;
-        }
-        
-        .upload-pulse {
-            position: absolute;
-            top: 50%;
-            left: 50%;
-            transform: translate(-50%, -50%);
-            width: 120px;
-            height: 120px;
-            border: 2px solid var(--aurora-border);
-            border-radius: 50%;
-            animation: aurora-upload-pulse 3s ease-in-out infinite;
-        }
-        
-        .enhanced-upload-zone:hover .upload-icon-inner {
-            transform: scale(1.2) rotate(15deg);
-            opacity: 1;
-        }
-        
-        .upload-content h2 {
-            color: var(--aurora-primary);
-            font-size: 1.8rem;
-            margin: 0 0 8px 0;
-            font-weight: 700;
-        }
-        
-        .upload-subtitle {
-            color: var(--aurora-text-muted);
-            margin: 0 0 25px 0;
-            font-size: 1.1rem;
-            font-weight: 500;
-        }
-        
-        .upload-features-grid {
-            display: grid;
-            grid-template-columns: repeat(auto-fit, minmax(120px, 1fr));
-            gap: 15px;
-            margin: 25px 0;
-            max-width: 500px;
-            margin-left: auto;
-            margin-right: auto;
-        }
-        
-        .feature-card {
-            background: var(--aurora-bg-glass);
-            border: 1px solid var(--aurora-border);
-            border-radius: var(--aurora-radius);
-            padding: 12px 8px;
-            display: flex;
-            flex-direction: column;
-            align-items: center;
-            gap: 6px;
-            transition: all 0.3s ease;
-        }
-        
-        .feature-card:hover {
-            background: var(--aurora-bg-glass);
-            border-color: var(--aurora-border-hover);
-            transform: translateY(-2px);
-            box-shadow: var(--aurora-glow-subtle);
-        }
-        
-        .feature-icon {
-            font-size: 1.5rem;
-            color: var(--aurora-primary);
-        }
-        
-        .feature-text {
-            font-size: 0.85rem;
-            color: var(--aurora-text);
-            font-weight: 500;
-        }
-        
-        .supported-formats-enhanced {
-            margin-top: 20px;
-            display: flex;
-            justify-content: center;
-            gap: 20px;
-            flex-wrap: wrap;
-        }
-        
-        .format-group {
-            background: var(--aurora-bg-card);
-            border: 1px solid var(--aurora-border);
-            border-radius: var(--aurora-radius-small);
-            padding: 8px 12px;
-            font-size: 0.85rem;
-            color: var(--aurora-text-muted);
-        }
-        
-        .format-group strong {
-            color: var(--aurora-primary);
-        }
-        
-        @keyframes aurora-upload-pulse {
-            0%, 100% {
-                transform: translate(-50%, -50%) scale(1);
-                opacity: 0.6;
-            }
-            50% {
-                transform: translate(-50%, -50%) scale(1.2);
-                opacity: 0.3;
-            }
-        }
-        </style>
-        """
-        
-        st.markdown(upload_css, unsafe_allow_html=True)
-        st.markdown(upload_html, unsafe_allow_html=True)
-        
-        # Enhanced file uploader
-        uploaded_file = st.file_uploader(
-            "Choose an audio or video file",
-            type=['mp3', 'wav', 'm4a', 'aac', 'ogg', 'flac', 'wma', 'webm', 'mpeg', 'mpga', 'oga', 
-                  'mp4', 'avi', 'mov', 'mkv', 'wmv', 'flv'],
-            help="Upload audio/video files up to 2GB. Large files automatically use FFmpeg chunking for optimal processing.",
-            label_visibility="collapsed"
-        )
-        
-        return uploaded_file
-    
-    def process_large_file(self, uploaded_file) -> Dict[str, Any]:
-        """Enhanced large file processing with FFmpeg"""
-        
-        # Validate file first
-        validation = self.validate_file(uploaded_file)
-        if not validation["valid"]:
-            return {"success": False, "error": validation["error"]}
-        
-        file_size_mb = validation["size_mb"]
-        requires_chunking = validation["requires_chunking"]
-        
-        st.info(f"📁 **File:** {uploaded_file.name} ({file_size_mb:.1f} MB)")
-        
-        if requires_chunking:
-            st.info("🔧 **Processing Method:** FFmpeg chunking (large file detected)")
-            return self._process_with_ffmpeg_chunking(uploaded_file)
-        else:
-            st.info("⚡ **Processing Method:** Standard processing (small file)")
-            return self._process_standard(uploaded_file)
-    
-    def _process_standard(self, uploaded_file) -> Dict[str, Any]:
-        """Process smaller files using standard method"""
-        try:
-            from core.content_generation import transcribe_audio
-            
-            # Create temporary file
-            with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp_file:
-                tmp_file.write(uploaded_file.getvalue())
-                tmp_file_path = tmp_file.name
-            
-            try:
-                # Transcribe directly
-                with st.spinner("🎯 Transcribing audio..."):
-                    transcript = transcribe_audio(tmp_file_path)
-                
-                return {
-                    "success": True,
-                    "transcript": transcript,
-                    "method": "standard",
-                    "chunks_processed": 1
-                }
-                
-            finally:
-                # Cleanup
-                if os.path.exists(tmp_file_path):
-                    os.unlink(tmp_file_path)
-                    
-        except Exception as e:
-            return {"success": False, "error": f"Standard processing failed: {str(e)}"}
-    
-    def _process_with_ffmpeg_chunking(self, uploaded_file) -> Dict[str, Any]:
-        """Process large files using FFmpeg chunking"""
-        
-        # Setup temporary directory
-        self.temp_dir = tempfile.mkdtemp(prefix="whisperforge_chunks_")
-        
-        try:
-            # Save uploaded file
-            input_file_path = os.path.join(self.temp_dir, uploaded_file.name)
-            with open(input_file_path, 'wb') as f:
-                f.write(uploaded_file.getvalue())
-            
-            # Get audio information
-            st.info("🔍 Analyzing audio file...")
-            audio_info = self.get_audio_info(input_file_path)
-            
-            if "error" in audio_info:
-                return {"success": False, "error": audio_info["error"]}
-            
-            duration = audio_info["duration"]
-            st.success(f"📊 **Duration:** {duration/60:.1f} minutes | **Format:** {audio_info['format']} | **Codec:** {audio_info['codec']}")
-            
-            # Create chunks using FFmpeg
-            st.info("✂️ Creating audio chunks...")
-            chunks_result = self._create_ffmpeg_chunks(input_file_path, duration)
-            
-            if not chunks_result["success"]:
-                return chunks_result
-            
-            chunks = chunks_result["chunks"]
-            st.success(f"✅ Created {len(chunks)} chunks of ~{self.chunk_duration_minutes} minutes each")
-            
-            # Process chunks in parallel
-            st.info("🚀 Starting parallel transcription...")
-            transcription_result = self._transcribe_chunks_parallel_ffmpeg(chunks)
-            
-            if not transcription_result["success"]:
-                return transcription_result
-            
-            # Reassemble transcript
-            full_transcript = self._reassemble_transcript_ffmpeg(transcription_result["chunk_transcripts"])
-            
-            return {
-                "success": True,
-                "transcript": full_transcript,
-                "method": "ffmpeg_chunking",
-                "chunks_processed": len(chunks),
-                "processing_time": transcription_result.get("total_time", "unknown"),
-                "success_rate": transcription_result.get("success_rate", "unknown")
-            }
-            
-        except Exception as e:
-            return {"success": False, "error": f"FFmpeg processing failed: {str(e)}"}
-        
-        finally:
-            # Cleanup temporary directory
-            self._cleanup_temp_dir()
-    
-    def _create_ffmpeg_chunks(self, input_file_path: str, duration: float) -> Dict[str, Any]:
-        """Create audio chunks using FFmpeg"""
-        try:
-            import subprocess
-            
-            chunk_duration_seconds = self.chunk_duration_minutes * 60
-            num_chunks = math.ceil(duration / chunk_duration_seconds)
-            chunks = []
-            
-            progress_bar = st.progress(0, f"Creating chunks: 0/{num_chunks}")
-            
-            for i in range(num_chunks):
-                start_time = i * chunk_duration_seconds
-                chunk_filename = f"chunk_{i:03d}.wav"
-                chunk_path = os.path.join(self.temp_dir, chunk_filename)
-                
-                # FFmpeg command to extract chunk with audio optimization
-                cmd = [
-                    'ffmpeg', '-i', input_file_path,
-                    '-ss', str(start_time),
-                    '-t', str(chunk_duration_seconds),
-                    '-ar', '16000',  # 16kHz sample rate (optimal for Whisper)
-                    '-ac', '1',      # Mono audio
-                    '-acodec', 'pcm_s16le',  # PCM format
-                    '-y',  # Overwrite output files
-                    chunk_path
-                ]
-                
-                result = subprocess.run(cmd, capture_output=True, text=True, timeout=300)
-                
-                if result.returncode != 0:
-                    return {"success": False, "error": f"FFmpeg chunk creation failed: {result.stderr}"}
-                
-                # Verify chunk was created
-                if not os.path.exists(chunk_path) or os.path.getsize(chunk_path) == 0:
-                    continue  # Skip empty chunks
-                
-                chunks.append({
-                    "index": i,
-                    "file_path": chunk_path,
-                    "start_time": start_time,
-                    "duration": min(chunk_duration_seconds, duration - start_time)
-                })
-                
-                # Update progress
-                progress_bar.progress((i + 1) / num_chunks, f"Creating chunks: {i + 1}/{num_chunks}")
-            
-            return {"success": True, "chunks": chunks}
-            
-        except Exception as e:
-            return {"success": False, "error": f"Chunk creation failed: {str(e)}"}
-    
-    def _transcribe_chunks_parallel_ffmpeg(self, chunks: List[Dict]) -> Dict[str, Any]:
-        """Transcribe chunks in parallel using ThreadPoolExecutor"""
-        from core.content_generation import transcribe_audio
-        
-        chunk_transcripts = {}
-        total_chunks = len(chunks)
-        
-        # Create progress containers
-        progress_container = st.empty()
-        status_container = st.empty()
-        
-        def transcribe_single_chunk(chunk_info: Dict) -> Tuple[int, str, bool]:
-            """Transcribe a single chunk"""
-            try:
-                chunk_index = chunk_info["index"]
-                file_path = chunk_info["file_path"]
-                
-                transcript = transcribe_audio(file_path)
-                return chunk_index, transcript, True
-                
-            except Exception as e:
-                logger.error(f"Failed to transcribe chunk {chunk_info['index']}: {e}")
-                return chunk_info["index"], f"[Transcription failed for chunk {chunk_info['index']}]", False
-        
-        start_time = time.time()
-        completed_chunks = 0
-        
-        # Process chunks in parallel
-        with ThreadPoolExecutor(max_workers=self.max_parallel_chunks) as executor:
-            # Submit all chunks
-            future_to_chunk = {
-                executor.submit(transcribe_single_chunk, chunk): chunk 
-                for chunk in chunks
-            }
-            
-            # Process completed futures
-            for future in as_completed(future_to_chunk):
-                chunk_index, transcript, success = future.result()
-                
-                if success:
-                    chunk_transcripts[chunk_index] = transcript
-                
-                completed_chunks += 1
-                
-                # Update progress
-                progress = completed_chunks / total_chunks
-                with progress_container:
-                    st.progress(progress, f"Transcribing: {completed_chunks}/{total_chunks} chunks")
-                
-                with status_container:
-                    elapsed = time.time() - start_time
-                    if completed_chunks > 0:
-                        eta = (elapsed / completed_chunks) * (total_chunks - completed_chunks)
-                        st.info(f"⏱️ Elapsed: {elapsed:.1f}s | ETA: {eta:.1f}s | Success: {len(chunk_transcripts)}/{completed_chunks}")
-        
-        processing_time = time.time() - start_time
-        successful_chunks = len(chunk_transcripts)
-        
-        # Check success rate
-        if successful_chunks < total_chunks * 0.7:  # Require at least 70% success
-            return {
-                "success": False,
-                "error": f"Too many failed chunks: {successful_chunks}/{total_chunks} successful"
-            }
-        
-        return {
-            "success": True,
-            "chunk_transcripts": chunk_transcripts,
-            "total_time": f"{processing_time:.1f}s",
-            "success_rate": f"{successful_chunks}/{total_chunks}"
-        }
-    
-    def _reassemble_transcript_ffmpeg(self, chunk_transcripts: Dict[int, str]) -> str:
-        """Reassemble transcript from chunks in correct order"""
-        # Sort chunks by index and concatenate
-        sorted_chunks = sorted(chunk_transcripts.items())
-        full_transcript = " ".join([transcript for _, transcript in sorted_chunks])
-        
-        # Clean up transcript
-        full_transcript = full_transcript.strip()
-        
-        return full_transcript
-    
-    def _cleanup_temp_dir(self):
-        """Clean up temporary directory and all files"""
-        if self.temp_dir and os.path.exists(self.temp_dir):
-            try:
-                import shutil
-                shutil.rmtree(self.temp_dir)
-                self.temp_dir = None
-            except Exception as e:
-                logger.warning(f"Failed to cleanup temp directory {self.temp_dir}: {e}") 
\ No newline at end of file
+        time.sleep(duration / steps)
diff --git a/core/health_check.py b/core/health_check.py
index 806100b..9eb69c0 100644
--- a/core/health_check.py
+++ b/core/health_check.py
@@ -1,10 +1,11 @@
+"""Minimal health check utilities for WhisperForge."""
+
 from __future__ import annotations
 
-"""Minimal health check utilities for WhisperForge."""
+from dataclasses import asdict, dataclass
+from datetime import UTC, datetime
+from typing import Any
 
-from dataclasses import dataclass, asdict
-from datetime import datetime
-from typing import Any, Dict
 
 @dataclass
 class HealthStatus:
@@ -28,7 +29,7 @@ class HealthChecker:
     def get_health_status(self) -> HealthStatus:
         return HealthStatus(
             status="healthy",
-            timestamp=datetime.utcnow().isoformat(),
+            timestamp=datetime.now(UTC).isoformat(),
             uptime_seconds=0.0,
         )
 
@@ -40,7 +41,7 @@ def check_slo_violations(self) -> list:
         return []
 
     def get_metrics_json(self) -> str:
-        data: Dict[str, Any] = {
+        data: dict[str, Any] = {
             "health": asdict(self.get_health_status()),
             "slo_metrics": asdict(self.get_slo_metrics()),
         }
diff --git a/core/large_file_processor.py b/core/large_file_processor.py
new file mode 100644
index 0000000..f860885
--- /dev/null
+++ b/core/large_file_processor.py
@@ -0,0 +1,449 @@
+"""
+Enhanced Large File Processor
+=============================
+
+FFmpeg-based large file processing with intelligent chunking and parallel transcription.
+Supports files up to 2GB with memory-efficient streaming.
+"""
+
+import logging
+import math
+import os
+import subprocess
+import tempfile
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+import openai
+import streamlit as st
+
+from .constants import (
+    AUDIO_CHANNELS,
+    AUDIO_SAMPLE_RATE,
+    CHUNK_DURATION_MINUTES,
+    CHUNK_SUCCESS_THRESHOLD_FFMPEG,
+    FFMPEG_CHUNK_TIMEOUT,
+    FFMPEG_VERSION_CHECK_TIMEOUT,
+    FFPROBE_TIMEOUT,
+    LARGE_FILE_THRESHOLD_BYTES,
+    MAX_PARALLEL_CHUNKS_FFMPEG,
+    MAX_UPLOAD_SIZE_BYTES,
+)
+from .exceptions import APIClientError, FileProcessingError
+
+logger = logging.getLogger(__name__)
+
+
+class EnhancedLargeFileProcessor:
+    """Enhanced Large File Processor with FFmpeg for 2GB+ files.
+
+    Features:
+    - FFmpeg-based processing for memory efficiency
+    - Support for files up to 2GB
+    - Intelligent 10-minute audio chunking
+    - Parallel transcription with ThreadPoolExecutor
+    - Memory-efficient streaming without loading entire files into RAM
+    """
+
+    def __init__(self):
+        self.supported_formats = {
+            "audio": [".mp3", ".wav", ".m4a", ".aac", ".ogg", ".flac", ".wma", ".webm", ".mpeg", ".mpga", ".oga"],
+            "video": [".mp4", ".avi", ".mov", ".mkv", ".wmv", ".flv", ".webm"],
+        }
+        self.max_file_size = MAX_UPLOAD_SIZE_BYTES
+        self.chunk_duration_minutes = CHUNK_DURATION_MINUTES
+        self.max_parallel_chunks = MAX_PARALLEL_CHUNKS_FFMPEG
+        self.temp_dir = None
+
+    def check_ffmpeg_availability(self) -> bool:
+        """Check if FFmpeg is available on the system."""
+        try:
+            import subprocess
+
+            result = subprocess.run(
+                ["ffmpeg", "-version"], capture_output=True, text=True, timeout=FFMPEG_VERSION_CHECK_TIMEOUT
+            )  # noqa: S603
+            return result.returncode == 0
+        except (subprocess.TimeoutExpired, FileNotFoundError, Exception):
+            return False
+
+    def get_audio_info(self, file_path: str) -> dict:
+        """Get audio file information using ffprobe."""
+        try:
+            import json
+            import subprocess
+
+            cmd = ["ffprobe", "-v", "quiet", "-print_format", "json", "-show_format", "-show_streams", file_path]
+
+            result = subprocess.run(cmd, capture_output=True, text=True, timeout=FFPROBE_TIMEOUT)  # noqa: S603
+            if result.returncode != 0:
+                return {"error": f"ffprobe failed: {result.stderr}"}
+
+            data = json.loads(result.stdout)
+            format_info = data.get("format", {})
+
+            audio_stream = None
+            for stream in data.get("streams", []):
+                if stream.get("codec_type") == "audio":
+                    audio_stream = stream
+                    break
+
+            if not audio_stream:
+                return {"error": "No audio stream found"}
+
+            duration = float(format_info.get("duration", 0))
+            size = int(format_info.get("size", 0))
+
+            return {
+                "duration": duration,
+                "size": size,
+                "format": format_info.get("format_name", "unknown"),
+                "codec": audio_stream.get("codec_name", "unknown"),
+                "sample_rate": int(audio_stream.get("sample_rate", 0)),
+                "channels": int(audio_stream.get("channels", 0)),
+            }
+
+        except (subprocess.CalledProcessError, subprocess.TimeoutExpired, OSError) as e:
+            return {"error": f"Failed to get audio info: {e!s}"}
+        except FileProcessingError as e:
+            return {"error": f"Unexpected error getting audio info: {e!s}"}
+
+    def validate_file(self, uploaded_file) -> dict:
+        """Enhanced file validation for large files."""
+        if not uploaded_file:
+            return {"valid": False, "error": "No file provided"}
+
+        file_size = len(uploaded_file.getvalue())
+        if file_size > self.max_file_size:
+            size_gb = file_size / (1024 * 1024 * 1024)
+            return {"valid": False, "error": f"File too large: {size_gb:.1f}GB (max 2GB)"}
+
+        file_extension = os.path.splitext(uploaded_file.name)[1].lower()
+        all_formats = self.supported_formats["audio"] + self.supported_formats["video"]
+        if file_extension not in all_formats:
+            return {"valid": False, "error": f"Unsupported format: {file_extension}"}
+
+        if file_size > LARGE_FILE_THRESHOLD_BYTES and not self.check_ffmpeg_availability():
+            return {
+                "valid": False,
+                "error": "FFmpeg required for large files but not available. Please install FFmpeg.",
+            }
+
+        return {
+            "valid": True,
+            "size": file_size,
+            "size_mb": file_size / (1024 * 1024),
+            "requires_chunking": file_size > LARGE_FILE_THRESHOLD_BYTES,
+            "format": file_extension,
+        }
+
+    def create_enhanced_upload_interface(self):
+        """Create enhanced upload interface for large files."""
+        upload_html = """
+        <div class="enhanced-upload-container">
+            <div class="enhanced-upload-zone" id="enhanced-upload-zone">
+                <div class="upload-icon-large">
+                    <div class="upload-icon-inner">\U0001f3b5</div>
+                    <div class="upload-pulse"></div>
+                </div>
+                <div class="upload-content">
+                    <h2>Enhanced Large File Upload</h2>
+                    <p class="upload-subtitle">Powered by FFmpeg \u2022 Up to 2GB \u2022 Intelligent Chunking</p>
+                    <div class="upload-features-grid">
+                        <div class="feature-card">
+                            <span class="feature-icon">\u26a1</span>
+                            <span class="feature-text">10-min chunks</span>
+                        </div>
+                        <div class="feature-card">
+                            <span class="feature-icon">\U0001f504</span>
+                            <span class="feature-text">Parallel processing</span>
+                        </div>
+                        <div class="feature-card">
+                            <span class="feature-icon">\U0001f4be</span>
+                            <span class="feature-text">Memory efficient</span>
+                        </div>
+                        <div class="feature-card">
+                            <span class="feature-icon">\U0001f3af</span>
+                            <span class="feature-text">Auto-retry</span>
+                        </div>
+                    </div>
+                    <div class="supported-formats-enhanced">
+                        <div class="format-group">
+                            <strong>Audio:</strong> MP3, WAV, M4A, AAC, OGG, FLAC, WEBM
+                        </div>
+                        <div class="format-group">
+                            <strong>Video:</strong> MP4, AVI, MOV, MKV, WMV (audio extraction)
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+        """
+
+        from .file_upload import _load_upload_css
+
+        st.markdown(_load_upload_css(), unsafe_allow_html=True)
+        st.markdown(upload_html, unsafe_allow_html=True)
+
+        uploaded_file = st.file_uploader(
+            "Choose an audio or video file",
+            type=[
+                "mp3",
+                "wav",
+                "m4a",
+                "aac",
+                "ogg",
+                "flac",
+                "wma",
+                "webm",
+                "mpeg",
+                "mpga",
+                "oga",
+                "mp4",
+                "avi",
+                "mov",
+                "mkv",
+                "wmv",
+                "flv",
+            ],
+            help="Upload audio/video files up to 2GB. Large files automatically use FFmpeg chunking.",
+            label_visibility="collapsed",
+        )
+
+        return uploaded_file
+
+    def process_large_file(self, uploaded_file) -> dict:
+        """Enhanced large file processing with FFmpeg."""
+        validation = self.validate_file(uploaded_file)
+        if not validation["valid"]:
+            return {"success": False, "error": validation["error"]}
+
+        file_size_mb = validation["size_mb"]
+        requires_chunking = validation["requires_chunking"]
+
+        st.info(f"\U0001f4c1 **File:** {uploaded_file.name} ({file_size_mb:.1f} MB)")
+
+        if requires_chunking:
+            st.info("\U0001f527 **Processing Method:** FFmpeg chunking (large file detected)")
+            return self._process_with_ffmpeg_chunking(uploaded_file)
+        else:
+            st.info("\u26a1 **Processing Method:** Standard processing (small file)")
+            return self._process_standard(uploaded_file)
+
+    def _process_standard(self, uploaded_file) -> dict:
+        """Process smaller files using standard method."""
+        try:
+            from core.content_generation import transcribe_audio
+
+            with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp_file:
+                tmp_file.write(uploaded_file.getvalue())
+                tmp_file_path = tmp_file.name
+
+            try:
+                with st.spinner("\U0001f3af Transcribing audio..."):
+                    transcript = transcribe_audio(tmp_file_path)
+
+                return {"success": True, "transcript": transcript, "method": "standard", "chunks_processed": 1}
+            finally:
+                if os.path.exists(tmp_file_path):
+                    os.unlink(tmp_file_path)
+
+        except (openai.APIError, OSError) as e:
+            return {"success": False, "error": f"Standard processing failed: {e!s}"}
+        except (APIClientError, FileProcessingError) as e:
+            return {"success": False, "error": f"Unexpected standard processing error: {e!s}"}
+
+    def _process_with_ffmpeg_chunking(self, uploaded_file) -> dict:
+        """Process large files using FFmpeg chunking."""
+        self.temp_dir = tempfile.mkdtemp(prefix="whisperforge_chunks_")
+
+        try:
+            input_file_path = os.path.join(self.temp_dir, uploaded_file.name)
+            with open(input_file_path, "wb") as f:
+                f.write(uploaded_file.getvalue())
+
+            st.info("\U0001f50d Analyzing audio file...")
+            audio_info = self.get_audio_info(input_file_path)
+
+            if "error" in audio_info:
+                return {"success": False, "error": audio_info["error"]}
+
+            duration = audio_info["duration"]
+            st.success(
+                f"\U0001f4ca **Duration:** {duration / 60:.1f} minutes | "
+                f"**Format:** {audio_info['format']} | **Codec:** {audio_info['codec']}"
+            )
+
+            st.info("\u2702\ufe0f Creating audio chunks...")
+            chunks_result = self._create_ffmpeg_chunks(input_file_path, duration)
+
+            if not chunks_result["success"]:
+                return chunks_result
+
+            chunks = chunks_result["chunks"]
+            st.success(f"\u2705 Created {len(chunks)} chunks of ~{self.chunk_duration_minutes} minutes each")
+
+            st.info("\U0001f680 Starting parallel transcription...")
+            transcription_result = self._transcribe_chunks_parallel(chunks)
+
+            if not transcription_result["success"]:
+                return transcription_result
+
+            full_transcript = self._reassemble_transcript(transcription_result["chunk_transcripts"])
+
+            return {
+                "success": True,
+                "transcript": full_transcript,
+                "method": "ffmpeg_chunking",
+                "chunks_processed": len(chunks),
+                "processing_time": transcription_result.get("total_time", "unknown"),
+                "success_rate": transcription_result.get("success_rate", "unknown"),
+            }
+
+        except (subprocess.CalledProcessError, subprocess.TimeoutExpired, OSError) as e:
+            return {"success": False, "error": f"FFmpeg processing failed: {e!s}"}
+        except (FileProcessingError, APIClientError) as e:
+            return {"success": False, "error": f"Unexpected FFmpeg processing error: {e!s}"}
+
+        finally:
+            self._cleanup_temp_dir()
+
+    def _create_ffmpeg_chunks(self, input_file_path: str, duration: float) -> dict:
+        """Create audio chunks using FFmpeg."""
+        try:
+            import subprocess
+
+            chunk_duration_seconds = self.chunk_duration_minutes * 60
+            num_chunks = math.ceil(duration / chunk_duration_seconds)
+            chunks = []
+
+            progress_bar = st.progress(0, f"Creating chunks: 0/{num_chunks}")
+
+            for i in range(num_chunks):
+                start_time = i * chunk_duration_seconds
+                chunk_filename = f"chunk_{i:03d}.wav"
+                chunk_path = os.path.join(self.temp_dir, chunk_filename)
+
+                cmd = [
+                    "ffmpeg",
+                    "-i",
+                    input_file_path,
+                    "-ss",
+                    str(start_time),
+                    "-t",
+                    str(chunk_duration_seconds),
+                    "-ar",
+                    str(AUDIO_SAMPLE_RATE),
+                    "-ac",
+                    str(AUDIO_CHANNELS),
+                    "-acodec",
+                    "pcm_s16le",
+                    "-y",
+                    chunk_path,
+                ]
+
+                result = subprocess.run(cmd, capture_output=True, text=True, timeout=FFMPEG_CHUNK_TIMEOUT)  # noqa: S603
+
+                if result.returncode != 0:
+                    return {"success": False, "error": f"FFmpeg chunk creation failed: {result.stderr}"}
+
+                if not os.path.exists(chunk_path) or os.path.getsize(chunk_path) == 0:
+                    continue
+
+                chunks.append(
+                    {
+                        "index": i,
+                        "file_path": chunk_path,
+                        "start_time": start_time,
+                        "duration": min(chunk_duration_seconds, duration - start_time),
+                    }
+                )
+
+                progress_bar.progress((i + 1) / num_chunks, f"Creating chunks: {i + 1}/{num_chunks}")
+
+            return {"success": True, "chunks": chunks}
+
+        except (subprocess.CalledProcessError, subprocess.TimeoutExpired, OSError) as e:
+            return {"success": False, "error": f"Chunk creation failed: {e!s}"}
+        except FileProcessingError as e:
+            return {"success": False, "error": f"Unexpected chunk creation error: {e!s}"}
+
+    def _transcribe_chunks_parallel(self, chunks: list[dict]) -> dict:
+        """Transcribe chunks in parallel using ThreadPoolExecutor."""
+        from core.content_generation import transcribe_audio
+
+        chunk_transcripts = {}
+        total_chunks = len(chunks)
+
+        progress_container = st.empty()
+        status_container = st.empty()
+
+        def transcribe_single_chunk(chunk_info: dict) -> tuple[int, str, bool]:
+            """Transcribe a single chunk."""
+            try:
+                chunk_index = chunk_info["index"]
+                file_path = chunk_info["file_path"]
+                transcript = transcribe_audio(file_path)
+                return chunk_index, transcript, True
+            except (openai.APIError, OSError) as e:
+                logger.error(f"Failed to transcribe chunk {chunk_info['index']}: {e}")
+                return chunk_info["index"], f"[Transcription failed for chunk {chunk_info['index']}]", False
+            except APIClientError as e:
+                logger.error(f"Unexpected error transcribing chunk {chunk_info['index']}: {e}")
+                return chunk_info["index"], f"[Unexpected error for chunk {chunk_info['index']}]", False
+
+        start_time = time.time()
+        completed_chunks = 0
+
+        with ThreadPoolExecutor(max_workers=self.max_parallel_chunks) as executor:
+            future_to_chunk = {executor.submit(transcribe_single_chunk, chunk): chunk for chunk in chunks}
+
+            for future in as_completed(future_to_chunk):
+                chunk_index, transcript, success = future.result()
+
+                if success:
+                    chunk_transcripts[chunk_index] = transcript
+
+                completed_chunks += 1
+
+                progress = completed_chunks / total_chunks
+                with progress_container:
+                    st.progress(progress, f"Transcribing: {completed_chunks}/{total_chunks} chunks")
+
+                with status_container:
+                    elapsed = time.time() - start_time
+                    if completed_chunks > 0:
+                        eta = (elapsed / completed_chunks) * (total_chunks - completed_chunks)
+                        successful = len(chunk_transcripts)
+                        st.info(
+                            f"\u23f1\ufe0f Elapsed: {elapsed:.1f}s | ETA: {eta:.1f}s | Success: {successful}/{completed_chunks}"
+                        )
+
+        processing_time = time.time() - start_time
+        successful_chunks = len(chunk_transcripts)
+
+        if successful_chunks < total_chunks * CHUNK_SUCCESS_THRESHOLD_FFMPEG:
+            return {"success": False, "error": f"Too many failed chunks: {successful_chunks}/{total_chunks} successful"}
+
+        return {
+            "success": True,
+            "chunk_transcripts": chunk_transcripts,
+            "total_time": f"{processing_time:.1f}s",
+            "success_rate": f"{successful_chunks}/{total_chunks}",
+        }
+
+    def _reassemble_transcript(self, chunk_transcripts: dict[int, str]) -> str:
+        """Reassemble transcript from chunks in correct order."""
+        sorted_chunks = sorted(chunk_transcripts.items())
+        return " ".join([transcript for _, transcript in sorted_chunks]).strip()
+
+    def _cleanup_temp_dir(self):
+        """Clean up temporary directory and all files."""
+        if self.temp_dir and os.path.exists(self.temp_dir):
+            try:
+                import shutil
+
+                shutil.rmtree(self.temp_dir)
+                self.temp_dir = None
+            except OSError as e:
+                logger.warning(f"Failed to cleanup temp directory {self.temp_dir}: {e}")
diff --git a/core/logging_config.py b/core/logging_config.py
index 87e8220..e291ee2 100644
--- a/core/logging_config.py
+++ b/core/logging_config.py
@@ -3,77 +3,65 @@
 Provides structured logging with different levels and contexts
 """
 
+import json
 import logging
 import sys
-import os
+import traceback
 from datetime import datetime
 from pathlib import Path
-import json
-import traceback
-from typing import Dict, Any, Optional
+from typing import Any
+
 
 class WhisperForgeLogger:
     """Enhanced logger with context and structured output"""
-    
+
     def __init__(self, name: str = "whisperforge"):
         self.name = name
         self.logger = logging.getLogger(name)
         self.setup_logging()
-    
+
     def setup_logging(self):
         """Configure logging with multiple handlers and formats"""
-        
+
         # Clear existing handlers
         self.logger.handlers.clear()
-        
+
         # Set base level
         self.logger.setLevel(logging.DEBUG)
-        
+
         # Create logs directory
         logs_dir = Path("logs")
         logs_dir.mkdir(exist_ok=True)
-        
+
         # Console handler with color coding
         console_handler = logging.StreamHandler(sys.stdout)
         console_handler.setLevel(logging.INFO)
-        console_formatter = ColoredFormatter(
-            '%(asctime)s | %(levelname)s | %(name)s | %(message)s',
-            datefmt='%H:%M:%S'
-        )
+        console_formatter = ColoredFormatter("%(asctime)s | %(levelname)s | %(name)s | %(message)s", datefmt="%H:%M:%S")
         console_handler.setFormatter(console_formatter)
-        
+
         # File handler for all logs
-        file_handler = logging.FileHandler(
-            logs_dir / f"whisperforge_{datetime.now().strftime('%Y%m%d')}.log"
-        )
+        file_handler = logging.FileHandler(logs_dir / f"whisperforge_{datetime.now().strftime('%Y%m%d')}.log")
         file_handler.setLevel(logging.DEBUG)
         file_formatter = logging.Formatter(
-            '%(asctime)s | %(levelname)s | %(name)s | %(funcName)s:%(lineno)d | %(message)s',
-            datefmt='%Y-%m-%d %H:%M:%S'
+            "%(asctime)s | %(levelname)s | %(name)s | %(funcName)s:%(lineno)d | %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
         )
         file_handler.setFormatter(file_formatter)
-        
+
         # Error handler for critical issues
-        error_handler = logging.FileHandler(
-            logs_dir / f"errors_{datetime.now().strftime('%Y%m%d')}.log"
-        )
+        error_handler = logging.FileHandler(logs_dir / f"errors_{datetime.now().strftime('%Y%m%d')}.log")
         error_handler.setLevel(logging.ERROR)
         error_handler.setFormatter(file_formatter)
-        
+
         # Add handlers
         self.logger.addHandler(console_handler)
         self.logger.addHandler(file_handler)
         self.logger.addHandler(error_handler)
-    
-    def log_pipeline_step(self, step: str, status: str, data: Optional[Dict] = None):
+
+    def log_pipeline_step(self, step: str, status: str, data: dict | None = None):
         """Log pipeline step with structured data"""
-        log_data = {
-            "step": step,
-            "status": status,
-            "timestamp": datetime.now().isoformat(),
-            "data": data or {}
-        }
-        
+        log_data = {"step": step, "status": status, "timestamp": datetime.now().isoformat(), "data": data or {}}
+
         if status == "started":
             self.logger.info(f"🔄 Pipeline step started: {step}")
         elif status == "completed":
@@ -82,89 +70,99 @@ def log_pipeline_step(self, step: str, status: str, data: Optional[Dict] = None)
             self.logger.error(f"❌ Pipeline step failed: {step}")
             if data and "error" in data:
                 self.logger.error(f"Error details: {data['error']}")
-        
+
         # Log structured data to file
         self._log_structured(log_data)
-    
+
     def log_file_upload(self, filename: str, size_mb: float, file_type: str):
         """Log file upload details"""
         self.logger.info(f"📁 File uploaded: {filename} ({size_mb:.1f}MB, {file_type})")
-        self._log_structured({
-            "event": "file_upload",
-            "filename": filename,
-            "size_mb": size_mb,
-            "file_type": file_type,
-            "timestamp": datetime.now().isoformat()
-        })
-    
-    def log_ai_request(self, provider: str, model: str, prompt_type: str, tokens: Optional[int] = None):
+        self._log_structured(
+            {
+                "event": "file_upload",
+                "filename": filename,
+                "size_mb": size_mb,
+                "file_type": file_type,
+                "timestamp": datetime.now().isoformat(),
+            }
+        )
+
+    def log_ai_request(self, provider: str, model: str, prompt_type: str, tokens: int | None = None):
         """Log AI API requests"""
         self.logger.info(f"🤖 AI request: {provider}/{model} for {prompt_type}")
-        self._log_structured({
-            "event": "ai_request",
-            "provider": provider,
-            "model": model,
-            "prompt_type": prompt_type,
-            "tokens": tokens,
-            "timestamp": datetime.now().isoformat()
-        })
-    
-    def log_database_operation(self, operation: str, table: str, success: bool, error: Optional[str] = None):
+        self._log_structured(
+            {
+                "event": "ai_request",
+                "provider": provider,
+                "model": model,
+                "prompt_type": prompt_type,
+                "tokens": tokens,
+                "timestamp": datetime.now().isoformat(),
+            }
+        )
+
+    def log_database_operation(self, operation: str, table: str, success: bool, error: str | None = None):
         """Log database operations"""
         status = "✅" if success else "❌"
         self.logger.info(f"{status} Database {operation}: {table}")
         if error:
             self.logger.error(f"Database error: {error}")
-        
-        self._log_structured({
-            "event": "database_operation",
-            "operation": operation,
-            "table": table,
-            "success": success,
-            "error": error,
-            "timestamp": datetime.now().isoformat()
-        })
-    
-    def log_user_action(self, action: str, user_id: Optional[str] = None, details: Optional[Dict] = None):
+
+        self._log_structured(
+            {
+                "event": "database_operation",
+                "operation": operation,
+                "table": table,
+                "success": success,
+                "error": error,
+                "timestamp": datetime.now().isoformat(),
+            }
+        )
+
+    def log_user_action(self, action: str, user_id: str | None = None, details: dict | None = None):
         """Log user actions"""
         self.logger.info(f"👤 User action: {action} (user: {user_id or 'anonymous'})")
-        self._log_structured({
-            "event": "user_action",
-            "action": action,
-            "user_id": user_id,
-            "details": details or {},
-            "timestamp": datetime.now().isoformat()
-        })
-    
-    def log_error(self, error: Exception, context: Optional[str] = None):
+        self._log_structured(
+            {
+                "event": "user_action",
+                "action": action,
+                "user_id": user_id,
+                "details": details or {},
+                "timestamp": datetime.now().isoformat(),
+            }
+        )
+
+    def log_error(self, error: Exception, context: str | None = None):
         """Log errors with full context"""
         error_msg = str(error)
         error_type = type(error).__name__
-        
+
         self.logger.error(f"💥 {error_type}: {error_msg}")
         if context:
             self.logger.error(f"Context: {context}")
-        
+
         # Log full traceback
         self.logger.error(f"Traceback:\n{traceback.format_exc()}")
-        
-        self._log_structured({
-            "event": "error",
-            "error_type": error_type,
-            "error_message": error_msg,
-            "context": context,
-            "traceback": traceback.format_exc(),
-            "timestamp": datetime.now().isoformat()
-        })
-    
-    def _log_structured(self, data: Dict[str, Any]):
+
+        self._log_structured(
+            {
+                "event": "error",
+                "error_type": error_type,
+                "error_message": error_msg,
+                "context": context,
+                "traceback": traceback.format_exc(),
+                "timestamp": datetime.now().isoformat(),
+            }
+        )
+
+    def _log_structured(self, data: dict[str, Any]):
         """Log structured data to JSON file"""
         json_log_file = Path("logs") / f"structured_{datetime.now().strftime('%Y%m%d')}.jsonl"
-        
+
         try:
             with open(json_log_file, "a") as f:
                 f.write(json.dumps(data) + "\n")
-        except Exception as e:
+        except OSError as e:
             self.logger.error(f"Failed to write structured log: {e}")
 
     # -------------------------------------------------------------------
@@ -180,41 +178,50 @@ def __getattr__(self, name):
         """
         return getattr(self.logger, name)
 
+
 class ColoredFormatter(logging.Formatter):
     """Colored console formatter"""
-    
+
     COLORS = {
-        'DEBUG': '\033[36m',    # Cyan
-        'INFO': '\033[32m',     # Green
-        'WARNING': '\033[33m',  # Yellow
-        'ERROR': '\033[31m',    # Red
-        'CRITICAL': '\033[35m', # Magenta
-        'RESET': '\033[0m'      # Reset
+        "DEBUG": "\033[36m",  # Cyan
+        "INFO": "\033[32m",  # Green
+        "WARNING": "\033[33m",  # Yellow
+        "ERROR": "\033[31m",  # Red
+        "CRITICAL": "\033[35m",  # Magenta
+        "RESET": "\033[0m",  # Reset
     }
-    
+
     def format(self, record):
-        log_color = self.COLORS.get(record.levelname, self.COLORS['RESET'])
+        log_color = self.COLORS.get(record.levelname, self.COLORS["RESET"])
         record.levelname = f"{log_color}{record.levelname}{self.COLORS['RESET']}"
         return super().format(record)
 
+
 # Global logger instance
 logger = WhisperForgeLogger()
 
 # Convenience functions
-def log_pipeline_step(step: str, status: str, data: Optional[Dict] = None):
+
+
+def log_pipeline_step(step: str, status: str, data: dict | None = None):
     logger.log_pipeline_step(step, status, data)
 
+
 def log_file_upload(filename: str, size_mb: float, file_type: str):
     logger.log_file_upload(filename, size_mb, file_type)
 
-def log_ai_request(provider: str, model: str, prompt_type: str, tokens: Optional[int] = None):
+
+def log_ai_request(provider: str, model: str, prompt_type: str, tokens: int | None = None):
     logger.log_ai_request(provider, model, prompt_type, tokens)
 
-def log_database_operation(operation: str, table: str, success: bool, error: Optional[str] = None):
+
+def log_database_operation(operation: str, table: str, success: bool, error: str | None = None):
     logger.log_database_operation(operation, table, success, error)
 
-def log_user_action(action: str, user_id: Optional[str] = None, details: Optional[Dict] = None):
+
+def log_user_action(action: str, user_id: str | None = None, details: dict | None = None):
     logger.log_user_action(action, user_id, details)
 
-def log_error(error: Exception, context: Optional[str] = None):
-    logger.log_error(error, context) 
\ No newline at end of file
+
+def log_error(error: Exception, context: str | None = None):
+    logger.log_error(error, context)
diff --git a/core/mcp_integration.py b/core/mcp_integration.py
new file mode 100644
index 0000000..45a0a32
--- /dev/null
+++ b/core/mcp_integration.py
@@ -0,0 +1,111 @@
+"""
+MCP (Model Context Protocol) Integration Module
+================================================
+
+Provides AI models with context about user data and preferences
+via Supabase. Extracted from core.supabase_integration to separate
+MCP concerns from the base database client.
+"""
+
+import logging
+from typing import Any
+
+from postgrest.exceptions import APIError
+
+from .exceptions import DatabaseError
+
+logger = logging.getLogger(__name__)
+
+
+class MCPSupabaseIntegration:
+    """
+    Model Context Protocol integration for Supabase
+    Provides AI models with context about user data and preferences
+    """
+
+    def __init__(self, supabase_client):
+        self.db = supabase_client
+
+    def get_user_context(self, user_id: int) -> dict[str, Any]:
+        """Get comprehensive user context for AI models"""
+        try:
+            # Get user profile
+            user = self.db.get_user(user_id)
+            if not user:
+                return {}
+
+            # Get user's knowledge base
+            knowledge_base = self.db.get_user_knowledge_base(user_id)
+
+            # Get user's custom prompts
+            custom_prompts = self.db.get_user_prompts(user_id)
+
+            # Get recent content history (for style learning)
+            recent_content = self.db.get_user_content(user_id, limit=10)
+
+            # Get user analytics
+            analytics = self.db.get_user_analytics(user_id, days=30)
+
+            context = {
+                "user_profile": {
+                    "subscription_tier": user.get("subscription_tier", "free"),
+                    "usage_quota": user.get("usage_quota", 60),
+                    "usage_current": user.get("usage_current", 0),
+                    "created_at": user.get("created_at"),
+                },
+                "knowledge_base": knowledge_base,
+                "custom_prompts": custom_prompts,
+                "content_history": recent_content,
+                "analytics": analytics,
+                "preferences": {
+                    "preferred_ai_provider": analytics.get("ai_providers_used", ["openai"])[0]
+                    if analytics.get("ai_providers_used")
+                    else "openai",
+                    "most_used_model": analytics.get("most_used_model", "gpt-3.5-turbo"),
+                },
+            }
+
+            return context
+        except APIError as e:
+            logger.error(f"Error getting user context for MCP: {e}")
+            return {}
+        except DatabaseError as e:
+            logger.error(f"Unexpected error getting user context for MCP: {e}")
+            return {}
+
+    def update_context_from_interaction(self, user_id: int, interaction_data: dict[str, Any]) -> bool:
+        """Update user context based on AI interaction results"""
+        try:
+            # Log the interaction
+            self.db.log_pipeline_execution(user_id, interaction_data)
+
+            # Update usage if provided
+            if "duration_seconds" in interaction_data:
+                self.db.update_user_usage(user_id, interaction_data["duration_seconds"])
+
+            # Save generated content if provided
+            if "content" in interaction_data:
+                self.db.save_content(user_id, interaction_data["content"])
+
+            return True
+        except APIError as e:
+            logger.error(f"Error updating context from interaction: {e}")
+            return False
+        except DatabaseError as e:
+            logger.error(f"Unexpected error updating context from interaction: {e}")
+            return False
+
+
+# Global instance
+_mcp_integration = None
+
+
+def get_mcp_integration() -> MCPSupabaseIntegration:
+    """Get or create MCP integration instance"""
+    global _mcp_integration
+    if _mcp_integration is None:
+        # Lazy import to avoid circular imports
+        from .supabase_integration import get_supabase_client
+
+        _mcp_integration = MCPSupabaseIntegration(get_supabase_client())
+    return _mcp_integration
diff --git a/core/metrics_exporter.py b/core/metrics_exporter.py
index 60928fd..88b9997 100644
--- a/core/metrics_exporter.py
+++ b/core/metrics_exporter.py
@@ -8,15 +8,17 @@
 from __future__ import annotations
 
 import json
-from typing import Any, Dict, List
+from collections import deque
+from typing import Any
 
+MAX_ENTRIES = 1000
 
-metrics_exporter: Dict[str, Any] = {
+metrics_exporter: dict[str, Any] = {
     "counters": {},
     "gauges": {},
     "histograms": {},
-    "requests": [],
-    "pipelines": [],
+    "requests": deque(maxlen=MAX_ENTRIES),
+    "pipelines": deque(maxlen=MAX_ENTRIES),
 }
 
 
@@ -29,10 +31,10 @@ def track_request(duration: float, status_code: int, method: str, path: str) ->
             "path": path,
         }
     )
-    metrics_exporter["counters"]["http_requests_total"] = (
-        metrics_exporter["counters"].get("http_requests_total", 0) + 1
+    metrics_exporter["counters"]["http_requests_total"] = metrics_exporter["counters"].get("http_requests_total", 0) + 1
+    metrics_exporter["histograms"].setdefault("http_request_duration_seconds", deque(maxlen=MAX_ENTRIES)).append(
+        duration
     )
-    metrics_exporter["histograms"].setdefault("http_request_duration_seconds", []).append(duration)
 
 
 def track_pipeline(name: str, duration: float, success: bool) -> None:
@@ -45,7 +47,16 @@ def track_pipeline(name: str, duration: float, success: bool) -> None:
     )
     key = "pipeline_success_total" if success else "pipeline_failure_total"
     metrics_exporter["counters"][key] = metrics_exporter["counters"].get(key, 0) + 1
-    metrics_exporter["histograms"].setdefault(f"pipeline_{name}_duration", []).append(duration)
+    metrics_exporter["histograms"].setdefault(f"pipeline_{name}_duration", deque(maxlen=MAX_ENTRIES)).append(duration)
+
+
+def reset_metrics() -> None:
+    """Reset all metrics to initial state"""
+    metrics_exporter["counters"].clear()
+    metrics_exporter["gauges"].clear()
+    metrics_exporter["histograms"].clear()
+    metrics_exporter["requests"].clear()
+    metrics_exporter["pipelines"].clear()
 
 
 def export_prometheus_metrics() -> str:
@@ -65,14 +76,15 @@ def export_prometheus_metrics() -> str:
     return "\n".join(lines)
 
 
-def export_json_metrics() -> Dict[str, Any]:
+def export_json_metrics() -> dict[str, Any]:
     """Return the metrics as a JSON-serialisable object."""
 
-    return json.loads(json.dumps(
-        {
-            "counters": metrics_exporter["counters"],
-            "gauges": metrics_exporter["gauges"],
-            "histograms": metrics_exporter["histograms"],
-        }
-    ))
-
+    return json.loads(
+        json.dumps(
+            {
+                "counters": metrics_exporter["counters"],
+                "gauges": metrics_exporter["gauges"],
+                "histograms": {k: list(v) for k, v in metrics_exporter["histograms"].items()},
+            }
+        )
+    )
diff --git a/core/monitoring.py b/core/monitoring.py
index 98d2a99..8649108 100644
--- a/core/monitoring.py
+++ b/core/monitoring.py
@@ -12,8 +12,7 @@
 import time
 import uuid
 from contextlib import contextmanager
-from typing import Any, Dict, Optional
-
+from typing import Any
 
 logger = logging.getLogger(__name__)
 
@@ -22,10 +21,11 @@
 # Structured logging
 # ---------------------------------------------------------------------------
 
+
 class StructuredLogger(logging.LoggerAdapter):
     """Minimal structured logger used in tests."""
 
-    def process(self, msg: str, kwargs: Dict[str, Any]) -> tuple[str, Dict[str, Any]]:
+    def process(self, msg: str, kwargs: dict[str, Any]) -> tuple[str, dict[str, Any]]:
         extra = kwargs.pop("extra", {})
         # treat any additional keyword arguments as structured fields
         extra.update({k: kwargs.pop(k) for k in list(kwargs)})
@@ -35,7 +35,7 @@ def process(self, msg: str, kwargs: Dict[str, Any]) -> tuple[str, Dict[str, Any]
         kwargs["extra"] = extra
         return msg, kwargs
 
-    def pipeline_start(self, name: str, user_id: Optional[str] = None) -> None:
+    def pipeline_start(self, name: str, user_id: str | None = None) -> None:
         self.info("pipeline start", extra={"pipeline": name, "user_id": user_id})
 
     def pipeline_complete(self, name: str, duration: float, success: bool = True) -> None:
@@ -48,7 +48,7 @@ def pipeline_complete(self, name: str, duration: float, success: bool = True) ->
 structured_logger = StructuredLogger(logger, {})
 
 
-def set_trace_context(user_id: Optional[str] = None, operation: Optional[str] = None) -> str:
+def set_trace_context(user_id: str | None = None, operation: str | None = None) -> str:
     """Create a trace context and log it."""
 
     trace_id = str(uuid.uuid4())
@@ -60,7 +60,7 @@ def set_trace_context(user_id: Optional[str] = None, operation: Optional[str] =
 
 
 @contextmanager
-def trace_operation(operation: str, user_id: Optional[str] = None):
+def trace_operation(operation: str, user_id: str | None = None):
     """Context manager that logs a trace when the block executes."""
 
     trace_id = set_trace_context(user_id=user_id, operation=operation)
@@ -77,13 +77,12 @@ def trace_operation(operation: str, user_id: Optional[str] = None):
 # Error tracking
 # ---------------------------------------------------------------------------
 
+
 class ErrorTracker:
-    def capture_exception(self, exc: Exception, context: Optional[Dict[str, Any]] = None) -> None:
-        structured_logger.error(
-            f"captured exception: {exc}", extra={"context": context}
-        )
+    def capture_exception(self, exc: Exception, context: dict[str, Any] | None = None) -> None:
+        structured_logger.error(f"captured exception: {exc}", extra={"context": context})
 
-    def capture_message(self, message: str, level: str = "info", context: Optional[Dict[str, Any]] = None) -> None:
+    def capture_message(self, message: str, level: str = "info", context: dict[str, Any] | None = None) -> None:
         getattr(structured_logger, level)(message, extra={"context": context})
 
 
@@ -94,6 +93,7 @@ def capture_message(self, message: str, level: str = "info", context: Optional[D
 # Performance tracking
 # ---------------------------------------------------------------------------
 
+
 class PerformanceTracker:
     @contextmanager
     def track_operation(self, name: str):
@@ -108,7 +108,7 @@ def track_operation(self, name: str):
             )
 
     def track_pipeline_performance(
-        self, name: str, duration: float, success: bool, file_size_mb: Optional[int] = None
+        self, name: str, duration: float, success: bool, file_size_mb: int | None = None
     ) -> None:
         structured_logger.info(
             "pipeline metrics",
@@ -141,6 +141,7 @@ def wrapper(*args, **kwargs):
 # Compatibility helpers used by other modules
 # ---------------------------------------------------------------------------
 
+
 def init_monitoring() -> bool:
     structured_logger.info("Monitoring initialised")
     return True
@@ -154,15 +155,15 @@ def track_performance(operation: str, duration: float) -> None:
     performance_tracker.track_pipeline_performance(operation, duration, True)
 
 
-def track_user_action(action: str, user_id: Optional[str] = None) -> None:
+def track_user_action(action: str, user_id: str | None = None) -> None:
     structured_logger.info("user action", extra={"action": action, "user_id": user_id})
 
 
-def track_page(page: str, user_id: Optional[str] = None) -> None:
+def track_page(page: str, user_id: str | None = None) -> None:
     structured_logger.info("page view", extra={"page": page, "user_id": user_id})
 
 
-def get_health_status() -> Dict[str, Any]:
+def get_health_status() -> dict[str, Any]:
     return {"status": "healthy", "monitoring": "basic", "timestamp": "now"}
 
 
@@ -178,10 +179,9 @@ def track_error(self, error: Exception, context: str = "") -> None:
     def track_performance(self, operation: str, duration: float) -> None:
         track_performance(operation, duration)
 
-    def track_user_action(self, action: str, user_id: Optional[str] = None) -> None:
+    def track_user_action(self, action: str, user_id: str | None = None) -> None:
         track_user_action(action, user_id)
 
 
 def get_monitoring_manager() -> MonitoringManager:
     return MonitoringManager()
-
diff --git a/core/notifications.py b/core/notifications.py
index 7157ef9..5b4d26b 100644
--- a/core/notifications.py
+++ b/core/notifications.py
@@ -3,75 +3,61 @@
 Animated toast notifications and status updates
 """
 
-import streamlit as st
 import time
-from typing import Optional, Literal
-from datetime import datetime
+from html import escape as html_escape
+from typing import Literal
+
+import streamlit as st
+
 
 class NotificationManager:
     """Manages beautiful notifications and status updates"""
-    
+
     def __init__(self):
         self.notifications = []
-        
-    def show_toast(self, 
-                   message: str, 
-                   type: Literal["success", "error", "warning", "info"] = "info",
-                   duration: float = 3.0,
-                   icon: Optional[str] = None):
+
+    def show_toast(
+        self,
+        message: str,
+        notification_type: Literal["success", "error", "warning", "info"] = "info",
+        duration: float = 3.0,
+        icon: str | None = None,
+    ):
         """Show a beautiful toast notification"""
-        
+
         # Auto-select icon based on type if not provided
         if not icon:
-            icons = {
-                "success": "✅",
-                "error": "❌", 
-                "warning": "⚠️",
-                "info": "ℹ️"
-            }
-            icon = icons.get(type, "ℹ️")
-        
+            icons = {"success": "✅", "error": "❌", "warning": "⚠️", "info": "ℹ️"}
+            icon = icons.get(notification_type, "ℹ️")
+
+        # Sanitize user-controlled content
+        safe_message = html_escape(message)
+
         # Color scheme for different types
         colors = {
-            "success": {
-                "bg": "rgba(54, 211, 153, 0.1)",
-                "border": "rgba(54, 211, 153, 0.4)",
-                "text": "#36D399"
-            },
-            "error": {
-                "bg": "rgba(248, 114, 114, 0.1)",
-                "border": "rgba(248, 114, 114, 0.4)", 
-                "text": "#F87272"
-            },
-            "warning": {
-                "bg": "rgba(251, 189, 35, 0.1)",
-                "border": "rgba(251, 189, 35, 0.4)",
-                "text": "#FBBD23"
-            },
-            "info": {
-                "bg": "rgba(58, 191, 248, 0.1)",
-                "border": "rgba(58, 191, 248, 0.4)",
-                "text": "#3ABFF8"
-            }
+            "success": {"bg": "rgba(54, 211, 153, 0.1)", "border": "rgba(54, 211, 153, 0.4)", "text": "#36D399"},
+            "error": {"bg": "rgba(248, 114, 114, 0.1)", "border": "rgba(248, 114, 114, 0.4)", "text": "#F87272"},
+            "warning": {"bg": "rgba(251, 189, 35, 0.1)", "border": "rgba(251, 189, 35, 0.4)", "text": "#FBBD23"},
+            "info": {"bg": "rgba(58, 191, 248, 0.1)", "border": "rgba(58, 191, 248, 0.4)", "text": "#3ABFF8"},
         }
-        
-        color = colors[type]
-        
+
+        color = colors[notification_type]
+
         toast_html = f"""
-        <div class="toast-notification {type}" id="toast-{int(time.time())}">
+        <div class="toast-notification {notification_type}" id="toast-{int(time.time())}">
             <div class="toast-content">
                 <span class="toast-icon">{icon}</span>
-                <span class="toast-message">{message}</span>
+                <span class="toast-message">{safe_message}</span>
             </div>
             <div class="toast-progress">
                 <div class="toast-progress-bar"></div>
             </div>
         </div>
-        
+
         <style>
         .toast-notification {{
-            background: {color['bg']};
-            border: 1px solid {color['border']};
+            background: {color["bg"]};
+            border: 1px solid {color["border"]};
             border-radius: 8px;
             padding: 12px 16px 4px 16px;
             margin: 8px 0;
@@ -80,23 +66,23 @@ def show_toast(self,
             animation: toast-slide-in 0.3s cubic-bezier(0.4, 0, 0.2, 1);
             box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
         }}
-        
+
         .toast-content {{
             display: flex;
             align-items: center;
             gap: 8px;
         }}
-        
+
         .toast-icon {{
             font-size: 16px;
         }}
-        
+
         .toast-message {{
             color: var(--text-primary);
             font-weight: 500;
             font-size: 0.9rem;
         }}
-        
+
         .toast-progress {{
             position: absolute;
             bottom: 0;
@@ -105,13 +91,13 @@ def show_toast(self,
             height: 3px;
             background: rgba(255, 255, 255, 0.1);
         }}
-        
+
         .toast-progress-bar {{
             height: 100%;
-            background: {color['text']};
+            background: {color["text"]};
             animation: toast-progress {duration}s linear forwards;
         }}
-        
+
         @keyframes toast-slide-in {{
             from {{
                 transform: translateX(100%);
@@ -122,13 +108,13 @@ def show_toast(self,
                 opacity: 1;
             }}
         }}
-        
+
         @keyframes toast-progress {{
             from {{ width: 100%; }}
             to {{ width: 0%; }}
         }}
         </style>
-        
+
         <script>
         setTimeout(function() {{
             var toast = document.getElementById('toast-{int(time.time())}');
@@ -141,133 +127,140 @@ def show_toast(self,
         }}, {duration * 1000});
         </script>
         """
-        
+
         return st.markdown(toast_html, unsafe_allow_html=True)
-    
-    def show_status_indicator(self, 
-                             status: str, 
-                             details: str = "",
-                             animated: bool = True):
+
+    def show_status_indicator(self, status: str, details: str = "", animated: bool = True):
         """Show a status indicator with optional animation"""
-        
+
         # Status configurations
         status_configs = {
             "processing": {
                 "icon": "🔄",
                 "color": "#3ABFF8",
                 "bg": "rgba(58, 191, 248, 0.1)",
-                "border": "rgba(58, 191, 248, 0.3)"
+                "border": "rgba(58, 191, 248, 0.3)",
             },
             "complete": {
-                "icon": "✅", 
+                "icon": "✅",
                 "color": "#36D399",
                 "bg": "rgba(54, 211, 153, 0.1)",
-                "border": "rgba(54, 211, 153, 0.3)"
+                "border": "rgba(54, 211, 153, 0.3)",
             },
             "error": {
                 "icon": "❌",
-                "color": "#F87272", 
+                "color": "#F87272",
                 "bg": "rgba(248, 114, 114, 0.1)",
-                "border": "rgba(248, 114, 114, 0.3)"
+                "border": "rgba(248, 114, 114, 0.3)",
             },
             "waiting": {
                 "icon": "⏳",
                 "color": "#FBBD23",
-                "bg": "rgba(251, 189, 35, 0.1)", 
-                "border": "rgba(251, 189, 35, 0.3)"
-            }
+                "bg": "rgba(251, 189, 35, 0.1)",
+                "border": "rgba(251, 189, 35, 0.3)",
+            },
         }
-        
+
         config = status_configs.get(status, status_configs["waiting"])
         animation_class = "status-animated" if animated else ""
-        
+
         status_html = f"""
         <div class="status-indicator {animation_class}">
             <div class="status-main">
-                <span class="status-icon">{config['icon']}</span>
+                <span class="status-icon">{config["icon"]}</span>
                 <span class="status-text">{status.title()}</span>
             </div>
-            {f'<div class="status-details">{details}</div>' if details else ''}
+            {f'<div class="status-details">{details}</div>' if details else ""}
         </div>
-        
+
         <style>
         .status-indicator {{
-            background: {config['bg']};
-            border: 1px solid {config['border']};
+            background: {config["bg"]};
+            border: 1px solid {config["border"]};
             border-radius: 8px;
             padding: 12px;
             margin: 8px 0;
             transition: all 0.3s ease;
         }}
-        
+
         .status-main {{
             display: flex;
             align-items: center;
             gap: 8px;
         }}
-        
+
         .status-icon {{
             font-size: 18px;
         }}
-        
+
         .status-text {{
-            color: {config['color']};
+            color: {config["color"]};
             font-weight: 600;
             font-size: 1rem;
         }}
-        
+
         .status-details {{
             color: var(--text-secondary);
             font-size: 0.85rem;
             margin-top: 4px;
             margin-left: 26px;
         }}
-        
+
         .status-animated .status-icon {{
             animation: status-pulse 1.5s ease-in-out infinite;
         }}
-        
+
         @keyframes status-pulse {{
             0%, 100% {{ transform: scale(1); }}
             50% {{ transform: scale(1.1); }}
         }}
         </style>
         """
-        
+
         return st.markdown(status_html, unsafe_allow_html=True)
 
+
 # Global notification manager instance
 notification_manager = NotificationManager()
 
 # Convenience functions
+
+
 def show_success(message: str, duration: float = 3.0):
     """Show success notification"""
     return notification_manager.show_toast(message, "success", duration)
 
+
 def show_error(message: str, duration: float = 5.0):
     """Show error notification"""
     return notification_manager.show_toast(message, "error", duration)
 
+
 def show_warning(message: str, duration: float = 4.0):
     """Show warning notification"""
     return notification_manager.show_toast(message, "warning", duration)
 
+
 def show_info(message: str, duration: float = 3.0):
     """Show info notification"""
     return notification_manager.show_toast(message, "info", duration)
 
+
 def show_processing_status(details: str = ""):
     """Show processing status indicator"""
     return notification_manager.show_status_indicator("processing", details, True)
 
+
 def show_complete_status(details: str = ""):
     """Show completion status indicator"""
     return notification_manager.show_status_indicator("complete", details, False)
 
+
 def show_error_status(details: str = ""):
     """Show error status indicator"""
     return notification_manager.show_status_indicator("error", details, False)
 
+
 def create_step_completion_animation():
     """Create a beautiful step completion animation"""
     animation_html = """
@@ -277,7 +270,7 @@ def create_step_completion_animation():
         </div>
         <div class="completion-text">Step Complete!</div>
     </div>
-    
+
     <style>
     .step-completion-animation {
         display: flex;
@@ -287,7 +280,7 @@ def create_step_completion_animation():
         padding: 16px;
         animation: completion-appear 0.5s cubic-bezier(0.4, 0, 0.2, 1);
     }
-    
+
     .completion-circle {
         width: 32px;
         height: 32px;
@@ -299,20 +292,20 @@ def create_step_completion_animation():
         animation: completion-scale 0.6s cubic-bezier(0.4, 0, 0.2, 1);
         box-shadow: 0 4px 12px rgba(54, 211, 153, 0.3);
     }
-    
+
     .checkmark {
         color: white;
         font-weight: bold;
         font-size: 16px;
         animation: checkmark-appear 0.3s ease 0.3s both;
     }
-    
+
     .completion-text {
         color: #36D399;
         font-weight: 600;
         font-size: 0.9rem;
     }
-    
+
     @keyframes completion-appear {
         from {
             opacity: 0;
@@ -323,13 +316,13 @@ def create_step_completion_animation():
             transform: translateY(0);
         }
     }
-    
+
     @keyframes completion-scale {
         0% { transform: scale(0); }
         50% { transform: scale(1.2); }
         100% { transform: scale(1); }
     }
-    
+
     @keyframes checkmark-appear {
         from {
             opacity: 0;
@@ -342,9 +335,10 @@ def create_step_completion_animation():
     }
     </style>
     """
-    
+
     return st.markdown(animation_html, unsafe_allow_html=True)
 
+
 def create_loading_spinner(text: str = "Processing..."):
     """Create a beautiful loading spinner"""
     spinner_html = f"""
@@ -356,7 +350,7 @@ def create_loading_spinner(text: str = "Processing..."):
         </div>
         <div class="loading-text">{text}</div>
     </div>
-    
+
     <style>
     .loading-spinner-container {{
         display: flex;
@@ -365,13 +359,13 @@ def create_loading_spinner(text: str = "Processing..."):
         gap: 16px;
         padding: 20px;
     }}
-    
+
     .loading-spinner {{
         position: relative;
         width: 40px;
         height: 40px;
     }}
-    
+
     .spinner-ring {{
         position: absolute;
         top: 0;
@@ -383,30 +377,30 @@ def create_loading_spinner(text: str = "Processing..."):
         border-radius: 50%;
         animation: spinner-rotate 1.2s linear infinite;
     }}
-    
+
     .spinner-ring:nth-child(2) {{
         border-top-color: #FF0080;
         animation-delay: -0.4s;
         animation-duration: 1.8s;
     }}
-    
+
     .spinner-ring:nth-child(3) {{
         border-top-color: #3ABFF8;
         animation-delay: -0.8s;
         animation-duration: 2.4s;
     }}
-    
+
     .loading-text {{
         color: var(--text-secondary);
         font-size: 0.9rem;
         font-weight: 500;
     }}
-    
+
     @keyframes spinner-rotate {{
         0% {{ transform: rotate(0deg); }}
         100% {{ transform: rotate(360deg); }}
     }}
     </style>
     """
-    
-    return st.markdown(spinner_html, unsafe_allow_html=True) 
\ No newline at end of file
+
+    return st.markdown(spinner_html, unsafe_allow_html=True)
diff --git a/core/notion_integration.py b/core/notion_integration.py
new file mode 100644
index 0000000..f90332a
--- /dev/null
+++ b/core/notion_integration.py
@@ -0,0 +1,270 @@
+"""
+Notion Integration
+==================
+
+Create formatted Notion pages from WhisperForge content.
+"""
+
+import logging
+import os
+from datetime import datetime
+
+from .constants import (
+    MAX_NOTION_BLOCKS,
+    NOTION_CHUNK_SIZE,
+    NOTION_TITLE_EXCERPT_LENGTH,
+    NOTION_TITLE_MAX_LENGTH,
+    NOTION_TITLE_MAX_TOKENS,
+)
+from .content_generation import ContentGenerationError, _chat_completion
+from .exceptions import APIClientError
+
+logger = logging.getLogger(__name__)
+
+
+def generate_ai_title(transcript: str) -> str:
+    """Generate a concise AI-powered title from a transcript excerpt."""
+    try:
+        system_prompt = (
+            "Generate a concise, descriptive title (max 60 characters) for an audio transcript. "
+            "The title should be clear, specific, professional, and capture the main topic. "
+            "No quotes or special characters. Respond with only the title."
+        )
+        user_content = f"Here is the transcript excerpt:\n\n{transcript[:NOTION_TITLE_EXCERPT_LENGTH]}..."
+
+        title = _chat_completion(system_prompt, user_content, max_tokens=NOTION_TITLE_MAX_TOKENS)
+        return title.strip().replace('"', "").replace("'", "")[:NOTION_TITLE_MAX_LENGTH]
+    except ContentGenerationError:
+        return f"WhisperForge Content - {datetime.now().strftime('%Y-%m-%d %H:%M')}"
+    except APIClientError:
+        return f"WhisperForge Content - {datetime.now().strftime('%Y-%m-%d %H:%M')}"
+
+
+def create_notion_page(title: str, content_data: dict[str, str]) -> str | None:
+    """Create a Notion page with WhisperForge content.
+
+    Returns the page URL on success, or None on failure.
+    """
+    try:
+        from notion_client import Client
+    except ImportError:
+        logger.warning("Install notion-client to enable Notion publishing: pip install notion-client")
+        return None
+
+    try:
+        api_key = os.getenv("NOTION_API_KEY")
+        database_id = os.getenv("NOTION_DATABASE_ID")
+
+        if not api_key or not database_id:
+            logger.warning("Notion not configured. Set NOTION_API_KEY and NOTION_DATABASE_ID to auto-publish.")
+            return None
+
+        client = Client(auth=api_key)
+        children = _build_notion_children(title, content_data)
+
+        response = client.pages.create(
+            parent={"database_id": database_id},
+            icon={"type": "emoji", "emoji": "\U0001f30c"},
+            properties={"Name": {"title": [{"text": {"content": title[:100]}}]}},
+            children=children[:MAX_NOTION_BLOCKS],
+        )
+
+        if response and "id" in response:
+            page_id = response["id"]
+            return f"https://notion.so/{page_id.replace('-', '')}"
+
+        return None
+
+    except (ContentGenerationError, OSError) as e:
+        logger.error(f"Notion publishing failed: {e!s}")
+        return None
+    except APIClientError as e:
+        logger.error(f"Unexpected error in Notion publishing: {e!s}")
+        return None
+
+
+def _build_notion_children(title: str, content_data: dict) -> list[dict]:
+    """Build the list of Notion block children for a page."""
+    children: list[dict] = []
+
+    # Header
+    children.append(
+        {
+            "type": "heading_1",
+            "heading_1": {
+                "rich_text": [
+                    {"type": "text", "text": {"content": "\U0001f30c "}, "annotations": {"color": "blue"}},
+                    {"type": "text", "text": {"content": title}, "annotations": {"bold": True}},
+                ]
+            },
+        }
+    )
+
+    # Creation info
+    children.append(
+        {
+            "type": "paragraph",
+            "paragraph": {
+                "rich_text": [
+                    {"type": "text", "text": {"content": "Generated with "}},
+                    {
+                        "type": "text",
+                        "text": {"content": "WhisperForge Aurora"},
+                        "annotations": {"bold": True, "color": "blue"},
+                    },
+                    {
+                        "type": "text",
+                        "text": {"content": f" \u2022 {datetime.now().strftime('%B %d, %Y at %I:%M %p')}"},
+                    },
+                ]
+            },
+        }
+    )
+
+    children.append({"type": "divider", "divider": {}})
+
+    # Wisdom callout
+    if content_data.get("wisdom"):
+        children.append(
+            {
+                "type": "callout",
+                "callout": {
+                    "rich_text": [
+                        {"type": "text", "text": {"content": "Key Insights & Wisdom"}},
+                        {"type": "text", "text": {"content": f"\n\n{content_data['wisdom'][:NOTION_CHUNK_SIZE]}"}},
+                    ],
+                    "color": "purple_background",
+                    "icon": {"type": "emoji", "emoji": "\U0001f4a1"},
+                },
+            }
+        )
+
+    # Content sections as toggles
+    sections = [
+        ("\U0001f4dd Transcript", content_data.get("transcript")),
+        ("\U0001f4a1 Wisdom", content_data.get("wisdom")),
+        ("\U0001f50d Research Links", content_data.get("research")),
+        ("\U0001f4cb Outline", content_data.get("outline")),
+        ("\U0001f4f0 Article", content_data.get("article")),
+        ("\U0001f4f1 Social Content", content_data.get("social_content")),
+    ]
+
+    for section_title, section_content in sections:
+        if not section_content:
+            continue
+
+        if section_title == "\U0001f50d Research Links" and isinstance(section_content, dict):
+            children.append(_build_research_toggle(section_title, section_content))
+        elif isinstance(section_content, str):
+            chunks = [
+                section_content[i : i + NOTION_CHUNK_SIZE] for i in range(0, len(section_content), NOTION_CHUNK_SIZE)
+            ]
+            children.append(
+                {
+                    "type": "toggle",
+                    "toggle": {
+                        "rich_text": [{"type": "text", "text": {"content": section_title}}],
+                        "children": [
+                            {
+                                "type": "paragraph",
+                                "paragraph": {"rich_text": [{"type": "text", "text": {"content": chunk}}]},
+                            }
+                            for chunk in chunks[:5]
+                        ],
+                    },
+                }
+            )
+
+    # Footer
+    children.extend(
+        [
+            {"type": "divider", "divider": {}},
+            {
+                "type": "callout",
+                "callout": {
+                    "rich_text": [
+                        {
+                            "type": "text",
+                            "text": {"content": "Content Generation Complete"},
+                            "annotations": {"bold": True},
+                        },
+                        {"type": "text", "text": {"content": "\n\nAI Pipeline: completed successfully"}},
+                        {
+                            "type": "text",
+                            "text": {"content": f"\nGenerated: {datetime.now().strftime('%B %d, %Y at %I:%M %p')}"},
+                        },
+                        {"type": "text", "text": {"content": "\nPowered by WhisperForge Aurora"}},
+                    ],
+                    "color": "green_background",
+                    "icon": {"type": "emoji", "emoji": "\u2705"},
+                },
+            },
+        ]
+    )
+
+    return children
+
+
+def _build_research_toggle(section_title: str, section_content: dict) -> dict:
+    """Build a Notion toggle block for research data."""
+    research_children: list[dict] = []
+    entities = section_content.get("entities", [])
+
+    if entities:
+        for entity in entities[:5]:
+            entity_name = entity.get("name", "Unknown Entity")
+            why_matters = entity.get("why_matters", "No description available")
+            links = entity.get("links", [])
+
+            research_children.append(
+                {
+                    "type": "callout",
+                    "callout": {
+                        "rich_text": [
+                            {"type": "text", "text": {"content": entity_name}, "annotations": {"bold": True}},
+                            {"type": "text", "text": {"content": f"\n{why_matters}"}},
+                        ],
+                        "color": "blue_background",
+                        "icon": {"type": "emoji", "emoji": "\U0001f52c"},
+                    },
+                }
+            )
+
+            for link in links[:3]:
+                link_title = link.get("title", "Link")
+                link_desc = link.get("description", "")
+                is_gem = link.get("is_gem", False)
+                gem_icon = "\U0001f48e" if is_gem else "\U0001f517"
+                color = "orange" if is_gem else "default"
+
+                research_children.append(
+                    {
+                        "type": "bulleted_list_item",
+                        "bulleted_list_item": {
+                            "rich_text": [
+                                {"type": "text", "text": {"content": f"{gem_icon} "}, "annotations": {"color": color}},
+                                {"type": "text", "text": {"content": link_title}, "annotations": {"bold": True}},
+                                {
+                                    "type": "text",
+                                    "text": {"content": f" - {link_desc}"},
+                                    "annotations": {"italic": True},
+                                },
+                            ]
+                        },
+                    }
+                )
+    else:
+        research_children.append(
+            {
+                "type": "paragraph",
+                "paragraph": {"rich_text": [{"type": "text", "text": {"content": "No research entities found."}}]},
+            }
+        )
+
+    return {
+        "type": "toggle",
+        "toggle": {
+            "rich_text": [{"type": "text", "text": {"content": section_title}}],
+            "children": research_children,
+        },
+    }
diff --git a/core/pages.py b/core/pages.py
new file mode 100644
index 0000000..aef222a
--- /dev/null
+++ b/core/pages.py
@@ -0,0 +1,393 @@
+"""
+Application Pages
+=================
+
+Secondary page functions for settings, knowledge base, and prompt management.
+"""
+
+import os
+
+import streamlit as st
+from postgrest.exceptions import APIError
+
+from .auth_wrapper import get_auth
+from .content_display import create_enhanced_aurora_content_card
+from .exceptions import APIClientError, DatabaseError, FileProcessingError
+from .supabase_integration import get_supabase_client
+from .utils import safe_path
+
+
+def _get_api_key(env_var: str) -> str:
+    """Get API key from auth session cache (if authenticated) or environment."""
+    auth = get_auth()
+    if auth.is_authenticated():
+        stored = auth.get_api_keys()
+        if env_var in stored:
+            return stored[env_var]
+    return os.getenv(env_var, "")
+
+
+def _set_api_key(env_var: str, value: str) -> None:
+    """Persist API key via auth wrapper and set in environment for SDK clients."""
+    os.environ[env_var] = value
+    auth = get_auth()
+    if auth.is_authenticated():
+        auth.update_api_key(env_var, value)
+
+
+def show_settings_page():
+    """Settings and configuration page."""
+    st.markdown("### Settings & Configuration")
+
+    st.markdown("#### AI Provider")
+    with st.expander("AI Provider Selection", expanded=True):
+        provider_options = {"OpenAI": "openai", "Anthropic": "anthropic", "Grok (xAI)": "grok"}
+        current_provider = st.session_state.get("ai_provider", "openai")
+        # Find display name for current provider
+        current_label = next((label for label, val in provider_options.items() if val == current_provider), "OpenAI")
+        selected_label = st.selectbox(
+            "Active AI Provider",
+            list(provider_options.keys()),
+            index=list(provider_options.keys()).index(current_label),
+            help="Select the AI provider for content generation (wisdom, outline, article, social posts).",
+        )
+        st.session_state.ai_provider = provider_options[selected_label]
+
+        st.info(
+            "Whisper transcription always requires an OpenAI API key, regardless of which "
+            "AI provider is selected for content generation."
+        )
+
+    st.markdown("#### API Keys & Models")
+    with st.expander("API Configuration", expanded=True):
+        col1, col2, col3 = st.columns(3)
+
+        with col1:
+            st.markdown("**OpenAI**")
+            openai_key = st.text_input(
+                "OpenAI API Key", type="password", value=_get_api_key("OPENAI_API_KEY"), help="Your OpenAI API key"
+            )
+            if openai_key:
+                _set_api_key("OPENAI_API_KEY", openai_key)
+                st.success("OpenAI key configured")
+
+            openai_model = st.selectbox(
+                "OpenAI Model",
+                ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo"],
+                help="Model used when OpenAI is the active provider.",
+            )
+            st.session_state.openai_model = openai_model
+
+        with col2:
+            st.markdown("**Anthropic**")
+            anthropic_key = st.text_input(
+                "Anthropic API Key",
+                type="password",
+                value=_get_api_key("ANTHROPIC_API_KEY"),
+                help="Your Anthropic API key",
+            )
+            if anthropic_key:
+                _set_api_key("ANTHROPIC_API_KEY", anthropic_key)
+                st.success("Anthropic key configured")
+
+            anthropic_model = st.selectbox(
+                "Anthropic Model",
+                ["claude-sonnet-4-20250514", "claude-haiku-4-20250514", "claude-3-5-sonnet-20241022"],
+                help="Model used when Anthropic is the active provider.",
+            )
+            st.session_state.anthropic_model = anthropic_model
+
+        with col3:
+            st.markdown("**Grok (xAI)**")
+            grok_key = st.text_input(
+                "Grok API Key",
+                type="password",
+                value=_get_api_key("GROK_API_KEY"),
+                help="Your xAI / Grok API key",
+            )
+            if grok_key:
+                _set_api_key("GROK_API_KEY", grok_key)
+                st.success("Grok key configured")
+
+            grok_model = st.selectbox(
+                "Grok Model",
+                ["grok-3", "grok-3-mini", "grok-2"],
+                help="Model used when Grok is the active provider.",
+            )
+            st.session_state.grok_model = grok_model
+
+    st.markdown("#### Notion Integration")
+    with st.expander("Notion Configuration", expanded=False):
+        notion_key = st.text_input(
+            "Notion API Key",
+            type="password",
+            value=_get_api_key("NOTION_API_KEY"),
+            help="Your Notion integration token",
+        )
+        if notion_key:
+            _set_api_key("NOTION_API_KEY", notion_key)
+
+        notion_db = st.text_input("Notion Database ID", value=_get_api_key("NOTION_DATABASE_ID"))
+        if notion_db:
+            _set_api_key("NOTION_DATABASE_ID", notion_db)
+
+        if notion_key and notion_db:
+            st.success("Notion configured")
+
+    st.markdown("#### Pipeline Configuration")
+    with st.expander("Processing Pipeline", expanded=True):
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.markdown("**Core Features**")
+            st.session_state.auto_notion = st.checkbox(
+                "Auto-publish to Notion", value=st.session_state.get("auto_notion", True)
+            )
+            st.session_state.live_stream = st.checkbox(
+                "Live Streaming", value=st.session_state.get("live_stream", False)
+            )
+            st.session_state.large_file_mode = st.checkbox(
+                "Enhanced Large File Processing", value=st.session_state.get("large_file_mode", True)
+            )
+
+        with col2:
+            st.markdown("**Quality Settings**")
+            st.session_state.content_length = st.selectbox(
+                "Article Length", ["Short (500-800 words)", "Medium (800-1200 words)", "Long (1200+ words)"]
+            )
+            st.session_state.tone_style = st.selectbox(
+                "Content Tone", ["Professional", "Conversational", "Academic", "Creative"]
+            )
+
+            templates = [f.replace(".md", "") for f in os.listdir("templates")] if os.path.exists("templates") else []
+            if templates:
+                st.session_state.article_template = st.selectbox("Article Template", templates)
+            else:
+                st.session_state.article_template = None
+
+    st.markdown("#### System Status")
+    with st.expander("Connection Status", expanded=False):
+        if st.button("Test All Connections"):
+            with st.spinner("Testing all connections..."):
+                # AI provider keys
+                ai_providers = {
+                    "OpenAI": "OPENAI_API_KEY",
+                    "Anthropic": "ANTHROPIC_API_KEY",
+                    "Grok": "GROK_API_KEY",
+                }
+                for name, env_var in ai_providers.items():
+                    if os.getenv(env_var):
+                        st.success(f"{name} API key configured")
+                    else:
+                        st.warning(f"{name} API key not set")
+
+                try:
+                    db = get_supabase_client()
+                    if db and db.test_connection():
+                        st.success("Supabase connected")
+                    else:
+                        st.error("Supabase connection failed")
+                except APIError as e:
+                    st.error(f"Supabase error: {e}")
+                except DatabaseError as e:
+                    st.error(f"Unexpected Supabase error: {e}")
+
+                try:
+                    if os.getenv("NOTION_API_KEY") and os.getenv("NOTION_DATABASE_ID"):
+                        from notion_client import Client
+
+                        client = Client(auth=os.getenv("NOTION_API_KEY"))
+                        client.databases.retrieve(database_id=os.getenv("NOTION_DATABASE_ID"))
+                        st.success("Notion connected")
+                    else:
+                        st.warning("Notion not configured")
+                except OSError as e:
+                    st.error(f"Notion error: {e}")
+                except APIClientError as e:
+                    st.error(f"Unexpected Notion error: {e}")
+
+
+def show_knowledge_base():
+    """Knowledge base management page."""
+    st.markdown("### Knowledge Base")
+
+    kb_path = "prompts/default/knowledge_base"
+
+    st.markdown(
+        "The knowledge base provides context and expertise to enhance content generation. "
+        "Add domain-specific information, style guides, and reference materials here."
+    )
+
+    tabs = st.tabs(["View Knowledge", "Add Knowledge", "Manage Files"])
+
+    with tabs[0]:
+        st.markdown("#### Current Knowledge Base")
+        try:
+            if os.path.exists(kb_path):
+                files = [f for f in os.listdir(kb_path) if f.endswith(".md")]
+                if files:
+                    selected_file = st.selectbox("Select knowledge file:", files)
+                    if selected_file:
+                        file_path = safe_path(kb_path, selected_file)
+                        with open(file_path) as f:
+                            content = f.read()
+                        st.markdown(f"**File:** `{selected_file}`")
+                        create_enhanced_aurora_content_card("Knowledge Content", content, "text", "\U0001f4d6")
+                else:
+                    st.info("No knowledge files found")
+            else:
+                st.info("Knowledge base directory not found")
+        except OSError as e:
+            st.error(f"Error reading knowledge base: {e}")
+        except FileProcessingError as e:
+            st.error(f"Unexpected error reading knowledge base: {e}")
+
+    with tabs[1]:
+        st.markdown("#### Add New Knowledge")
+
+        col1, col2 = st.columns([2, 1])
+        with col1:
+            kb_title = st.text_input("Knowledge Title", placeholder="e.g., 'Marketing Guidelines'")
+        with col2:
+            kb_category = st.selectbox("Category", ["General", "Style Guide", "Domain Expertise", "Templates"])
+
+        kb_content = st.text_area("Knowledge Content", placeholder="Enter your knowledge content here...", height=300)
+
+        if st.button("Save Knowledge", type="primary"):
+            if kb_title and kb_content:
+                if len(kb_title) > 100:
+                    st.error("Title must be 100 characters or fewer.")
+                else:
+                    try:
+                        os.makedirs(kb_path, exist_ok=True)
+                        filename = f"{kb_title.lower().replace(' ', '_')}.md"
+                        file_path = safe_path(kb_path, filename)
+
+                        with open(file_path, "w") as f:
+                            f.write(f"# {kb_title}\n\n")
+                            f.write(f"**Category:** {kb_category}\n\n")
+                            f.write(kb_content)
+
+                        st.success(f"Knowledge saved as `{filename}`")
+                    except ValueError as e:
+                        st.error(f"Invalid filename: {e}")
+                    except OSError as e:
+                        st.error(f"Error saving knowledge: {e}")
+                    except FileProcessingError as e:
+                        st.error(f"Unexpected error saving knowledge: {e}")
+            else:
+                st.error("Please provide both title and content")
+
+    with tabs[2]:
+        st.markdown("#### Manage Knowledge Files")
+        try:
+            if os.path.exists(kb_path):
+                files = [f for f in os.listdir(kb_path) if f.endswith(".md")]
+                if files:
+                    for file in files:
+                        col1, col2 = st.columns([3, 1])
+                        with col1:
+                            st.markdown(f"`{file}`")
+                        with col2:
+                            if st.button("Delete", key=f"delete_{file}"):
+                                try:
+                                    validated_path = safe_path(kb_path, file)
+                                    os.remove(validated_path)
+                                    st.success(f"Deleted `{file}`")
+                                    st.rerun()
+                                except (ValueError, OSError) as e:
+                                    st.error(f"Error deleting file: {e}")
+                                except FileProcessingError as e:
+                                    st.error(f"Unexpected error deleting file: {e}")
+                else:
+                    st.info("No knowledge files found")
+            else:
+                st.info("Knowledge base directory not found")
+        except OSError as e:
+            st.error(f"Error managing files: {e}")
+        except FileProcessingError as e:
+            st.error(f"Unexpected error managing files: {e}")
+
+
+def show_prompts_page():
+    """Prompts management page."""
+    st.markdown("### Prompt Customization")
+    st.markdown("Customize the AI prompts used in each step of the content generation pipeline.")
+
+    prompt_types = {
+        "wisdom": "Wisdom Extraction",
+        "outline": "Content Outline",
+        "article": "Article Generation",
+        "social": "Social Media Posts",
+    }
+
+    file_mapping = {
+        "wisdom": "wisdom_extraction.md",
+        "outline": "outline_creation.md",
+        "article": "article_generation.md",
+        "social": "social_media.md",
+    }
+
+    prompt_tabs = st.tabs(list(prompt_types.values()) + ["Advanced"])
+
+    for i, (prompt_key, prompt_name) in enumerate(prompt_types.items()):
+        with prompt_tabs[i]:
+            st.markdown(f"#### {prompt_name}")
+
+            prompt_file = f"prompts/default/{file_mapping[prompt_key]}"
+            current_prompt = ""
+
+            try:
+                if os.path.exists(prompt_file):
+                    with open(prompt_file) as f:
+                        current_prompt = f.read()
+                else:
+                    current_prompt = f"# {prompt_name} Prompt\n\nDefault prompt for {prompt_key} generation."
+            except OSError as e:
+                st.error(f"Error loading prompt: {e}")
+            except FileProcessingError as e:
+                st.error(f"Unexpected error loading prompt: {e}")
+
+            new_prompt = st.text_area(
+                f"Edit {prompt_name} Prompt",
+                value=current_prompt,
+                height=400,
+                help=f"Customize the prompt used for {prompt_key} generation",
+            )
+
+            col1, col2, col3 = st.columns([1, 1, 2])
+            with col1:
+                if st.button("Save", key=f"save_{prompt_key}"):
+                    try:
+                        os.makedirs("prompts/default", exist_ok=True)
+                        with open(prompt_file, "w") as f:
+                            f.write(new_prompt)
+                        st.success(f"{prompt_name} prompt saved!")
+                    except OSError as e:
+                        st.error(f"Error saving prompt: {e}")
+                    except FileProcessingError as e:
+                        st.error(f"Unexpected error saving prompt: {e}")
+
+            with col2:
+                if st.button("Reset", key=f"reset_{prompt_key}"):
+                    st.info("Reset to default functionality coming soon!")
+
+            with col3:
+                st.markdown(f"**File:** `{prompt_file}`")
+
+    with prompt_tabs[-1]:
+        st.markdown("#### Advanced Prompt Settings")
+        col1, col2 = st.columns(2)
+        with col1:
+            st.markdown("**Global Settings**")
+            temperature = st.slider("Temperature (Creativity)", 0.0, 1.0, 0.7, 0.1)
+            max_tokens = st.number_input("Max Tokens", 100, 4000, 2000)
+        with col2:
+            st.markdown("**Prompt Templates**")
+            if st.button("Import Prompt Set"):
+                st.info("Import functionality coming soon!")
+            if st.button("Export Prompt Set"):
+                st.info("Export functionality coming soon!")
+
+        st.session_state.temperature = temperature
+        st.session_state.max_tokens = max_tokens
diff --git a/core/path_safety.py b/core/path_safety.py
new file mode 100644
index 0000000..b034ba6
--- /dev/null
+++ b/core/path_safety.py
@@ -0,0 +1,33 @@
+"""Filesystem path validation utilities for WhisperForge.
+
+Ensures that user-supplied filenames cannot escape a designated root
+directory, preventing path-traversal attacks.
+"""
+
+import re
+from pathlib import Path
+
+# Only allow alphanumeric, hyphens, underscores, and single dots (no ..)
+_SAFE_FILENAME_RE = re.compile(r"^[a-zA-Z0-9][a-zA-Z0-9._-]*$")
+
+
+def safe_path(root: str, untrusted_name: str) -> Path:
+    """Build a file path that is guaranteed to stay within *root*.
+
+    Raises ValueError if the resulting path escapes the root directory
+    or if the filename contains disallowed characters.
+    """
+    if not _SAFE_FILENAME_RE.match(untrusted_name):
+        raise ValueError(
+            f"Invalid filename: {untrusted_name!r}. "
+            "Only alphanumeric characters, hyphens, underscores, and dots are allowed."
+        )
+    if ".." in untrusted_name:
+        raise ValueError("Path traversal sequences are not allowed.")
+
+    root_resolved = Path(root).resolve()
+    target = (root_resolved / untrusted_name).resolve()
+
+    if not target.is_relative_to(root_resolved):
+        raise ValueError(f"Path escapes allowed directory: {target} is not under {root_resolved}")
+    return target
diff --git a/core/pipeline.py b/core/pipeline.py
new file mode 100644
index 0000000..78988b1
--- /dev/null
+++ b/core/pipeline.py
@@ -0,0 +1,190 @@
+"""
+Content Processing Pipeline
+=============================
+
+Thin Streamlit UI wrapper around :mod:`pipeline_engine`.
+Provides Aurora-themed progress visualisation while the pure-logic engine
+does the actual work.
+"""
+
+import logging
+import time
+
+import streamlit as st
+
+from . import pipeline_engine
+from .exceptions import DatabaseError
+from .pipeline_engine import NullListener, PipelineConfig
+from .prompt_loader import load_custom_prompts
+from .supabase_integration import get_supabase_client
+
+logger = logging.getLogger(__name__)
+
+# Pipeline step definitions
+PIPELINE_STEP_NAMES = pipeline_engine.PIPELINE_STEP_NAMES
+
+
+# ---------------------------------------------------------------------------
+# Streamlit listener -- maps engine callbacks to Aurora UI
+# ---------------------------------------------------------------------------
+
+
+class StreamlitPipelineListener(NullListener):
+    """Bridges :class:`pipeline_engine.PipelineListener` events to Streamlit widgets."""
+
+    def __init__(self, pipeline_placeholder, start_time, containers):
+        self.placeholder = pipeline_placeholder
+        self.start_time = start_time
+        self.containers = containers
+
+    def on_step_progress(self, step_index, step_progress, total_progress, message):
+        _update_pipeline(self.placeholder, step_index, step_progress, total_progress, message, self.start_time)
+
+    def on_step_complete(self, step_index, step_name, result):
+        container = self.containers.get(step_name)
+        if container and result and isinstance(result, str):
+            with container:
+                st.markdown(f"**{step_name} Complete**")
+                st.markdown(result)
+
+    def on_error(self, step_index, error):
+        st.error(f"Pipeline failed: {error!s}")
+
+    def on_pipeline_complete(self, results):
+        st.markdown(
+            '<div class="aurora-celebration">'
+            '<h1 class="aurora-celebration-title">Pipeline Complete!</h1>'
+            '<p class="aurora-celebration-subtitle">Your content has been transformed with AI magic</p>'
+            "</div>",
+            unsafe_allow_html=True,
+        )
+        time.sleep(2)
+        self.placeholder.empty()
+
+
+# ---------------------------------------------------------------------------
+# Public entry point -- signature unchanged for app_simple.py callers
+# ---------------------------------------------------------------------------
+
+
+def process_pipeline(audio_file=None, transcript: str | None = None) -> dict | None:
+    """Unified content pipeline.
+
+    Supply *audio_file* to transcribe first, or *transcript* to skip transcription.
+    Returns the results dict on success, or ``None`` on failure.
+    """
+    start_time = time.time()
+
+    # Load any user-defined custom prompts
+    custom_prompts = load_custom_prompts()
+    if custom_prompts:
+        st.info(f"Using {len(custom_prompts)} custom prompts")
+
+    # --- Streamlit UI scaffolding ---
+    pipeline_placeholder = st.empty()
+    st.markdown("### Live Content Generation")
+
+    containers = {
+        "Transcription": st.expander("Transcription", expanded=False),
+        "Wisdom Extraction": st.expander("Wisdom Extraction", expanded=False),
+        "Outline Creation": st.expander("Outline Creation", expanded=False),
+        "Article Generation": st.expander("Article Generation", expanded=False),
+        "Social Content": st.expander("Social Content", expanded=False),
+        "Publishing": st.expander("Notion Publishing", expanded=False),
+    }
+
+    # --- Build engine config from session state ---
+    config = PipelineConfig(
+        custom_prompts=custom_prompts or {},
+        article_template=st.session_state.get("article_template"),
+        knowledge_base=st.session_state.get("knowledge_base", {}),
+        user_id=st.session_state.get("user_id"),
+        publish_to_notion=True,
+    )
+
+    # --- Obtain content store (Supabase) ---
+    try:
+        content_store = get_supabase_client()
+    except (DatabaseError, ImportError):
+        logger.warning("Supabase client unavailable -- content will not be persisted")
+        content_store = None
+
+    # --- Create listener and run engine ---
+    listener = StreamlitPipelineListener(pipeline_placeholder, start_time, containers)
+
+    return pipeline_engine.run_pipeline(
+        audio_file=audio_file,
+        transcript=transcript,
+        config=config,
+        content_store=content_store,
+        listener=listener,
+    )
+
+
+def _update_pipeline(placeholder, step, step_progress, total_progress, message, start_time):
+    """Update the pipeline visualization placeholder."""
+    with placeholder.container():
+        show_processing_pipeline(
+            current_step=step,
+            step_progress=step_progress,
+            total_progress=total_progress,
+            status_message=message,
+            processing_time=f"{time.time() - start_time:.1f}s",
+        )
+
+
+def show_processing_pipeline(
+    current_step=0,
+    step_progress=0,
+    total_progress=0,
+    status_message="",
+    processing_time="",
+):
+    """Display Aurora-styled processing pipeline visualization."""
+    steps = PIPELINE_STEP_NAMES
+    step_icons = ["\U0001f3a4", "\U0001f4a1", "\U0001f4cb", "\U0001f4dd", "\U0001f4f1", "\U0001f30c"]
+
+    step_items = []
+    for i, (name, icon) in enumerate(zip(steps, step_icons, strict=False)):
+        if i < current_step:
+            state = "completed"
+        elif i == current_step:
+            state = "active"
+        else:
+            state = "pending"
+
+        step_items.append(f"""
+        <div class="aurora-pipeline-step {state}">
+            <div class="aurora-step-icon">{icon}</div>
+            <div class="aurora-step-name">{name}</div>
+            {'<div class="aurora-step-progress"><div class="aurora-step-progress-bar" style="width: ' + str(step_progress) + '%"></div></div>' if state == "active" else ""}
+        </div>
+        """)
+
+    pipeline_html = f"""
+    <div class="aurora-processing-pipeline">
+        <div class="aurora-pipeline-header">
+            <h3 class="aurora-pipeline-title">Content Pipeline</h3>
+            <div class="aurora-pipeline-progress">
+                <div class="aurora-pipeline-progress-bar" style="width: {total_progress}%"></div>
+                <span class="aurora-pipeline-progress-text">{total_progress}%</span>
+            </div>
+        </div>
+        <div class="aurora-pipeline-steps">
+            {"".join(step_items)}
+        </div>
+        {
+        f'''
+        <div class="aurora-processing-status active">
+            <span class="aurora-status-icon">&#x26A1;</span>
+            <span class="aurora-status-text">{status_message}</span>
+            <span class="aurora-status-time">{processing_time}</span>
+        </div>
+        '''
+        if status_message
+        else ""
+    }
+    </div>
+    """
+
+    st.markdown(pipeline_html, unsafe_allow_html=True)
diff --git a/core/pipeline_engine.py b/core/pipeline_engine.py
new file mode 100644
index 0000000..f7823a3
--- /dev/null
+++ b/core/pipeline_engine.py
@@ -0,0 +1,308 @@
+"""
+Pipeline Engine
+================
+
+Pure business-logic pipeline orchestration with ZERO Streamlit dependencies.
+Extracts the content-generation workflow from pipeline.py so it can be driven
+by any front-end (Streamlit, CLI, API server, tests).
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import tempfile
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Any, Protocol
+
+from .content_generation import (
+    generate_article,
+    generate_outline,
+    generate_social_content,
+    generate_wisdom,
+    transcribe_audio,
+)
+from .exceptions import DatabaseError
+from .notion_integration import create_notion_page, generate_ai_title
+from .prompt_loader import get_prompt_for_step, load_custom_prompts, load_template
+
+logger = logging.getLogger(__name__)
+
+# Pipeline step definitions (mirrors pipeline.py)
+PIPELINE_STEP_NAMES = [
+    "Transcription",
+    "Wisdom Extraction",
+    "Outline Creation",
+    "Article Generation",
+    "Social Content",
+    "Publishing",
+]
+
+
+# ---------------------------------------------------------------------------
+# Listener protocol -- callers implement this to receive progress updates
+# ---------------------------------------------------------------------------
+
+
+class PipelineListener(Protocol):
+    """Observer interface for pipeline progress events."""
+
+    def on_step_start(self, step_index: int, step_name: str, message: str) -> None: ...
+
+    def on_step_progress(self, step_index: int, step_progress: int, total_progress: int, message: str) -> None: ...
+
+    def on_step_complete(self, step_index: int, step_name: str, result: Any) -> None: ...
+
+    def on_error(self, step_index: int, error: Exception) -> None: ...
+
+    def on_pipeline_complete(self, results: dict) -> None: ...
+
+
+class NullListener:
+    """No-op listener -- silently ignores every event."""
+
+    def on_step_start(self, step_index: int, step_name: str, message: str) -> None:
+        pass
+
+    def on_step_progress(self, step_index: int, step_progress: int, total_progress: int, message: str) -> None:
+        pass
+
+    def on_step_complete(self, step_index: int, step_name: str, result: Any) -> None:
+        pass
+
+    def on_error(self, step_index: int, error: Exception) -> None:
+        pass
+
+    def on_pipeline_complete(self, results: dict) -> None:
+        pass
+
+
+# ---------------------------------------------------------------------------
+# Pipeline configuration
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class PipelineConfig:
+    """Configuration knobs for a single pipeline run."""
+
+    custom_prompts: dict[str, str] = field(default_factory=dict)
+    article_template: str | None = None
+    knowledge_base: dict[str, str] = field(default_factory=dict)
+    user_id: int | str | None = None
+    publish_to_notion: bool = True
+
+
+# ---------------------------------------------------------------------------
+# Content store protocol -- abstracts away the persistence layer
+# ---------------------------------------------------------------------------
+
+
+class ContentStore(Protocol):
+    """Persistence interface for generated content."""
+
+    def save_content(self, user_id: int | str, content_data: dict) -> str | None: ...
+
+
+class NullContentStore:
+    """No-op content store -- discards everything."""
+
+    def save_content(self, user_id: int | str, content_data: dict) -> str | None:
+        return None
+
+
+# ---------------------------------------------------------------------------
+# Main orchestration
+# ---------------------------------------------------------------------------
+
+
+def run_pipeline(
+    *,
+    audio_file: Any = None,
+    transcript: str | None = None,
+    config: PipelineConfig | None = None,
+    content_store: ContentStore | None = None,
+    listener: PipelineListener | None = None,
+) -> dict | None:
+    """Execute the full content-generation pipeline.
+
+    Supply *audio_file* (a file-like object with ``.name`` and ``.getvalue()``)
+    to transcribe first, or *transcript* to skip transcription.
+
+    Returns the results dict on success, or ``None`` on failure.
+    """
+    if config is None:
+        config = PipelineConfig()
+    if content_store is None:
+        content_store = NullContentStore()
+    if listener is None:
+        listener = NullListener()
+
+    # Load custom prompts from disk when none were injected
+    custom_prompts = config.custom_prompts
+    if not custom_prompts:
+        custom_prompts = load_custom_prompts()
+        if custom_prompts:
+            logger.info("Loaded %d custom prompts from disk", len(custom_prompts))
+
+    results: dict = {}
+
+    try:
+        # ------------------------------------------------------------------
+        # Step 0 -- Transcription
+        # ------------------------------------------------------------------
+        if audio_file is not None:
+            listener.on_step_start(0, "Transcription", "Starting transcription...")
+            listener.on_step_progress(0, 0, 0, "Starting transcription...")
+
+            suffix = os.path.splitext(audio_file.name)[1]
+            with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+                tmp.write(audio_file.getvalue())
+                tmp_path = tmp.name
+
+            try:
+                listener.on_step_progress(0, 50, 8, "Transcribing audio with Whisper AI...")
+                transcript = transcribe_audio(tmp_path)
+                if not transcript:
+                    error = RuntimeError("Transcription failed: empty result")
+                    listener.on_error(0, error)
+                    logger.error("Transcription returned empty result")
+                    return None
+            finally:
+                if os.path.exists(tmp_path):
+                    os.unlink(tmp_path)
+
+            results["transcript"] = transcript
+            listener.on_step_progress(0, 100, 17, "Transcription complete!")
+            listener.on_step_complete(0, "Transcription", transcript)
+        else:
+            if not transcript:
+                error = ValueError("No audio file or transcript provided.")
+                listener.on_error(0, error)
+                logger.error("No audio file or transcript provided")
+                return None
+            results["transcript"] = transcript
+            listener.on_step_progress(1, 0, 17, f"Using pre-transcribed content ({len(transcript)} characters)")
+
+        # ------------------------------------------------------------------
+        # Step 1 -- Wisdom Extraction
+        # ------------------------------------------------------------------
+        listener.on_step_start(1, "Wisdom Extraction", "Extracting wisdom and insights...")
+        listener.on_step_progress(1, 0, 17, "Extracting wisdom and insights...")
+
+        wisdom_prompt = get_prompt_for_step("wisdom", custom_prompts)
+        listener.on_step_progress(1, 50, 25, "Analyzing content for key insights...")
+
+        wisdom = generate_wisdom(transcript, custom_prompt=wisdom_prompt, knowledge_base=config.knowledge_base)
+        results["wisdom"] = wisdom
+
+        listener.on_step_progress(1, 100, 33, "Wisdom extraction complete!")
+        listener.on_step_complete(1, "Wisdom Extraction", wisdom)
+
+        # ------------------------------------------------------------------
+        # Step 2 -- Outline Creation
+        # ------------------------------------------------------------------
+        listener.on_step_start(2, "Outline Creation", "Creating structured outline...")
+        listener.on_step_progress(2, 0, 33, "Creating structured outline...")
+
+        outline_prompt = get_prompt_for_step("outline", custom_prompts)
+        listener.on_step_progress(2, 50, 42, "Structuring content hierarchy...")
+
+        outline = generate_outline(
+            transcript, wisdom, custom_prompt=outline_prompt, knowledge_base=config.knowledge_base
+        )
+        results["outline"] = outline
+
+        listener.on_step_progress(2, 100, 50, "Outline creation complete!")
+        listener.on_step_complete(2, "Outline Creation", outline)
+
+        # ------------------------------------------------------------------
+        # Step 3 -- Article Generation
+        # ------------------------------------------------------------------
+        listener.on_step_start(3, "Article Generation", "Generating comprehensive article...")
+        listener.on_step_progress(3, 0, 50, "Generating comprehensive article...")
+
+        article_prompt = get_prompt_for_step("article", custom_prompts)
+        if config.article_template:
+            template_text = load_template(config.article_template)
+            if template_text and article_prompt:
+                article_prompt = template_text + "\n" + article_prompt
+
+        listener.on_step_progress(3, 50, 58, "Writing detailed article content...")
+
+        article = generate_article(
+            transcript,
+            wisdom,
+            outline,
+            custom_prompt=article_prompt,
+            knowledge_base=config.knowledge_base,
+        )
+        results["article"] = article
+
+        listener.on_step_progress(3, 100, 67, "Article generation complete!")
+        listener.on_step_complete(3, "Article Generation", article)
+
+        # ------------------------------------------------------------------
+        # Step 4 -- Social Content
+        # ------------------------------------------------------------------
+        listener.on_step_start(4, "Social Content", "Creating social media content...")
+        listener.on_step_progress(4, 0, 67, "Creating social media content...")
+
+        social_prompt = get_prompt_for_step("social", custom_prompts)
+        listener.on_step_progress(4, 50, 75, "Generating social media posts...")
+
+        social = generate_social_content(
+            wisdom, outline, article, custom_prompt=social_prompt, knowledge_base=config.knowledge_base
+        )
+        results["social_content"] = social
+
+        listener.on_step_progress(4, 100, 83, "Social content creation complete!")
+        listener.on_step_complete(4, "Social Content", social)
+
+        # ------------------------------------------------------------------
+        # Step 5 -- Publishing & Persistence
+        # ------------------------------------------------------------------
+        listener.on_step_start(5, "Publishing", "Publishing to Notion workspace...")
+        listener.on_step_progress(5, 0, 83, "Publishing to Notion workspace...")
+
+        if config.publish_to_notion and os.getenv("NOTION_API_KEY") and os.getenv("NOTION_DATABASE_ID"):
+            ai_title = generate_ai_title(transcript)
+            listener.on_step_progress(5, 50, 90, "Uploading content to Notion...")
+
+            notion_url = create_notion_page(ai_title, results)
+            if notion_url:
+                results["notion_url"] = notion_url
+            else:
+                logger.warning("Notion page creation returned None")
+        else:
+            logger.info("Notion publishing skipped (disabled or not configured)")
+
+        # --- Save to DB ---
+        listener.on_step_progress(5, 90, 96, "Saving to database...")
+        try:
+            if config.user_id is not None:
+                content_data = {
+                    "title": results.get("title", "Untitled"),
+                    "transcript": results.get("transcript", ""),
+                    "wisdom": results.get("wisdom", ""),
+                    "outline": results.get("outline", ""),
+                    "article": results.get("article", ""),
+                    "social_content": results.get("social_content", ""),
+                    "notion_url": results.get("notion_url", ""),
+                    "created_at": datetime.now().isoformat(),
+                }
+                content_store.save_content(config.user_id, content_data)
+        except DatabaseError as exc:
+            logger.warning("Content saved locally but database save failed: %s", exc)
+
+        listener.on_step_progress(5, 100, 100, "Pipeline complete! All content generated successfully.")
+        listener.on_step_complete(5, "Publishing", results.get("notion_url"))
+        listener.on_pipeline_complete(results)
+
+        return results
+
+    except Exception as exc:
+        listener.on_error(0, exc)
+        logger.exception("Pipeline failed: %s", exc)
+        return None
diff --git a/core/prompt_loader.py b/core/prompt_loader.py
new file mode 100644
index 0000000..50e47af
--- /dev/null
+++ b/core/prompt_loader.py
@@ -0,0 +1,59 @@
+"""
+Prompt Loading Utilities
+========================
+
+Load and manage AI prompts from the filesystem.
+"""
+
+import logging
+import os
+
+logger = logging.getLogger(__name__)
+
+
+def load_custom_prompts() -> dict[str, str]:
+    """Load custom prompts from the prompts/default directory."""
+    prompts = {}
+    prompt_dir = "prompts/default"
+
+    if os.path.exists(prompt_dir):
+        for filename in os.listdir(prompt_dir):
+            if filename.endswith(".md"):
+                prompt_name = filename.replace(".md", "")
+                try:
+                    with open(os.path.join(prompt_dir, filename), encoding="utf-8") as f:
+                        prompts[prompt_name] = f.read()
+                except OSError as e:
+                    logger.warning(f"Failed to load prompt {filename}: {e}")
+
+    return prompts
+
+
+def load_template(template_name: str) -> str | None:
+    """Load an article template by name from the templates folder."""
+    template_path = os.path.join("templates", f"{template_name}.md")
+    if os.path.exists(template_path):
+        with open(template_path, encoding="utf-8") as f:
+            return f.read()
+    return None
+
+
+# Maps pipeline step names to prompt file basenames
+_STEP_PROMPT_MAP = {
+    "wisdom": "wisdom_extraction",
+    "outline": "outline_creation",
+    "social": "social_media",
+    "article": "article_generation",
+}
+
+
+def get_prompt_for_step(step_name: str, custom_prompts: dict[str, str] | None = None) -> str | None:
+    """Get the appropriate prompt for a pipeline step."""
+    if not custom_prompts:
+        custom_prompts = load_custom_prompts()
+
+    prompt_key = _STEP_PROMPT_MAP.get(step_name)
+    if prompt_key and prompt_key in custom_prompts:
+        return custom_prompts[prompt_key]
+
+    return None
diff --git a/core/prompts.py b/core/prompts.py
new file mode 100644
index 0000000..eeb73b4
--- /dev/null
+++ b/core/prompts.py
@@ -0,0 +1,88 @@
+"""Prompt management for WhisperForge content generation.
+
+Handles loading, formatting, and enhancing prompts from markdown files
+with support for per-user overrides and automatic knowledge-base
+concatenation.
+"""
+
+import logging
+
+from .exceptions import FileProcessingError
+from .path_safety import safe_path
+
+logger = logging.getLogger(__name__)
+
+# Default prompts for content generation (DEPRECATED - use load_prompt_from_file)
+DEFAULT_PROMPTS = {
+    "wisdom_extraction": """Extract key insights, lessons, and wisdom from the transcript. Focus on actionable takeaways and profound realizations.""",
+    "summary": """## Summary
+Create a concise summary of the main points and key messages in the transcript.
+Capture the essence of the content in a few paragraphs.""",
+    "outline_creation": """Create a detailed outline for an article or blog post based on the transcript and extracted wisdom. Include major sections and subsections.""",
+    "social_media": """Generate engaging social media posts for different platforms (Twitter, LinkedIn, Instagram) based on the key insights.""",
+    "image_prompts": """Create detailed image generation prompts that visualize the key concepts and metaphors from the content.""",
+    "article_writing": """Write a comprehensive article based on the provided outline and wisdom. Maintain a clear narrative flow and engaging style.""",
+    "seo_analysis": """Analyze the content from an SEO perspective and provide optimization recommendations for better search visibility while maintaining content quality.""",
+    "editor_persona": """You are a professional content editor. Provide constructive feedback to improve the content quality.""",
+}
+
+
+def load_prompt_from_file(prompt_type: str, user_id: str = None) -> str:
+    """Load prompt from markdown file with user override support"""
+    try:
+        # Check for user-specific prompt first (for paid tiers)
+        if user_id:
+            user_dir = str(safe_path("prompts/users", user_id))
+            user_prompt_path = safe_path(user_dir, f"{prompt_type}.md")
+            if user_prompt_path.exists():
+                return user_prompt_path.read_text(encoding="utf-8").strip()
+
+        # Load default prompt
+        default_prompt_path = safe_path("prompts/default", f"{prompt_type}.md")
+        if default_prompt_path.exists():
+            return default_prompt_path.read_text(encoding="utf-8").strip()
+
+        # Fallback to hardcoded prompts
+        fallback = DEFAULT_PROMPTS.get(prompt_type, "")
+        if fallback:
+            logger.warning(f"Using fallback prompt for {prompt_type} - consider creating markdown file")
+            return fallback
+
+        logger.error(f"No prompt found for type: {prompt_type}")
+        return f"Please provide content for {prompt_type.replace('_', ' ')}."
+
+    except (FileProcessingError, OSError) as e:
+        logger.error(f"Error loading prompt {prompt_type}: {e}")
+        return DEFAULT_PROMPTS.get(prompt_type, f"Error loading {prompt_type} prompt.")
+
+
+def format_knowledge_base_context(knowledge_base: dict[str, str]) -> str:
+    """Format knowledge base content for auto-concatenation to prompts"""
+    if not knowledge_base:
+        return ""
+
+    context_parts = ["## Knowledge Base Context\n"]
+    context_parts.append(
+        "Use the following knowledge base to inform your analysis and maintain consistency with established perspectives:\n"
+    )
+
+    for name, content in knowledge_base.items():
+        context_parts.append(f"### {name}")
+        context_parts.append(content)
+        context_parts.append("")  # Empty line for separation
+
+    context_parts.append("---\n")
+    context_parts.append("## Your Task\n")
+
+    return "\n".join(context_parts)
+
+
+def get_enhanced_prompt(prompt_type: str, knowledge_base: dict[str, str] = None, user_id: str = None) -> str:
+    """Get prompt with automatic knowledge base concatenation"""
+    base_prompt = load_prompt_from_file(prompt_type, user_id)
+
+    if knowledge_base:
+        kb_context = format_knowledge_base_context(knowledge_base)
+        return f"{kb_context}{base_prompt}"
+
+    return base_prompt
diff --git a/core/security.py b/core/security.py
new file mode 100644
index 0000000..0ad5dff
--- /dev/null
+++ b/core/security.py
@@ -0,0 +1,51 @@
+"""Password hashing and verification utilities for WhisperForge.
+
+Provides bcrypt-based password hashing for current use and a deprecated
+SHA-256 helper retained solely for migrating legacy credential stores.
+"""
+
+import hashlib
+import logging
+import warnings
+
+import bcrypt
+
+logger = logging.getLogger(__name__)
+
+
+def hash_password(password: str) -> str:
+    """Hash a password using bcrypt with salt"""
+    # Generate salt and hash password
+    salt = bcrypt.gensalt()
+    hashed = bcrypt.hashpw(password.encode("utf-8"), salt)
+    return hashed.decode("utf-8")
+
+
+def verify_password(password: str, hashed: str) -> bool:
+    """Verify a password against its hash.
+
+    Raises on unexpected errors (e.g. DB corruption) instead of
+    silently returning False which would be indistinguishable from
+    a wrong password.
+    """
+    try:
+        return bcrypt.checkpw(password.encode("utf-8"), hashed.encode("utf-8"))
+    except ValueError as e:
+        # Malformed hash string (wrong prefix, bad encoding, etc.)
+        logger.error("Password verification failed – malformed hash: %s", e)
+        return False
+
+
+def legacy_hash_password(password: str) -> str:
+    """Legacy SHA-256 hash - DEPRECATED, use for migration only.
+
+    .. deprecated::
+        Use :func:`hash_password` (bcrypt) for all new credential storage.
+    """
+    warnings.warn(
+        "legacy_hash_password() is deprecated and will be removed in a future release. "
+        "Use hash_password() (bcrypt) instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return hashlib.sha256(password.encode()).hexdigest()
diff --git a/core/services.py b/core/services.py
new file mode 100644
index 0000000..00fbba4
--- /dev/null
+++ b/core/services.py
@@ -0,0 +1,97 @@
+"""
+Service Locator / DI Container
+================================
+
+Lightweight dependency-injection container for WhisperForge.
+Each field defaults to ``None`` and falls back to the existing
+module-level singleton on first access, so existing code keeps
+working unchanged while tests and alternative front-ends can
+inject their own implementations.
+
+Usage::
+
+    from core.services import get_services
+
+    svc = get_services()
+    cfg = svc.get_config()       # lazily loads the global Config
+    db  = svc.get_db()           # lazily loads the global SupabaseClient
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from .auth_wrapper import AuthWrapper
+    from .config import Config
+    from .session_manager import SessionManager
+    from .supabase_integration import SupabaseClient
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Services:
+    """Central service registry with lazy fallbacks to existing singletons."""
+
+    config: Config | None = None
+    db: SupabaseClient | None = None
+    auth: AuthWrapper | None = None
+    session: SessionManager | None = None
+
+    # -- lazy getters -------------------------------------------------------
+
+    def get_config(self) -> Config:
+        """Return the injected Config or fall back to the global singleton."""
+        if self.config is not None:
+            return self.config
+        from .config import get_config
+
+        return get_config()
+
+    def get_db(self) -> SupabaseClient:
+        """Return the injected DB client or fall back to the global singleton."""
+        if self.db is not None:
+            return self.db
+        from .supabase_integration import get_supabase_client
+
+        return get_supabase_client()
+
+    def get_auth(self) -> AuthWrapper:
+        """Return the injected AuthWrapper or fall back to the global singleton."""
+        if self.auth is not None:
+            return self.auth
+        from .auth_wrapper import get_auth
+
+        return get_auth()
+
+    def get_session(self) -> SessionManager:
+        """Return the injected SessionManager or fall back to the global singleton."""
+        if self.session is not None:
+            return self.session
+        from .session_manager import get_session_manager
+
+        return get_session_manager()
+
+
+# ---------------------------------------------------------------------------
+# Module-level singleton
+# ---------------------------------------------------------------------------
+
+_services: Services | None = None
+
+
+def get_services() -> Services:
+    """Return the current global ``Services`` instance (created on first call)."""
+    global _services
+    if _services is None:
+        _services = Services()
+    return _services
+
+
+def set_services(services: Services) -> None:
+    """Replace the global ``Services`` instance (useful in tests)."""
+    global _services
+    _services = services
diff --git a/core/session_manager.py b/core/session_manager.py
index 7a5607e..74963eb 100644
--- a/core/session_manager.py
+++ b/core/session_manager.py
@@ -1,15 +1,19 @@
 import json
 import logging
-from datetime import datetime, timedelta
+from datetime import UTC, datetime, timedelta
 from pathlib import Path
+
 import streamlit as st
 
+from .constants import DEFAULT_PAGE, SESSION_EXPIRY_DAYS
+
 logger = logging.getLogger(__name__)
 
+
 class SessionManager:
     """Simple file-based session persistence"""
 
-    def __init__(self, app_name: str = "whisperforge", expiry_days: int = 7):
+    def __init__(self, app_name: str = "whisperforge", expiry_days: int = SESSION_EXPIRY_DAYS):
         self.session_dir = Path.home() / f".{app_name}_sessions"
         self.session_file = self.session_dir / "session.json"
         self.expiry_days = expiry_days
@@ -18,7 +22,7 @@ def __init__(self, app_name: str = "whisperforge", expiry_days: int = 7):
             "user_id": None,
             "user_email": None,
             "preferences": {},
-            "current_page": "Transform",
+            "current_page": DEFAULT_PAGE,
             "pipeline_active": False,
             "created_at": None,
             "last_activity": None,
@@ -28,34 +32,40 @@ def __init__(self, app_name: str = "whisperforge", expiry_days: int = 7):
     def _load(self):
         if self.session_file.exists():
             try:
-                with open(self.session_file, "r", encoding="utf-8") as f:
+                with open(self.session_file, encoding="utf-8") as f:
                     loaded = json.load(f)
                 if loaded.get("created_at"):
                     created = datetime.fromisoformat(loaded["created_at"])
-                    if datetime.utcnow() - created > timedelta(days=self.expiry_days):
+                    if datetime.now(UTC) - created > timedelta(days=self.expiry_days):
                         self.session_file.unlink()
                         return
                 self.data.update(loaded)
                 st.session_state.update(self.data)
-            except Exception as e:
+            except (json.JSONDecodeError, OSError, KeyError) as e:
                 logger.error(f"Session load failed: {e}")
+            except Exception as e:
+                logger.warning(f"Unexpected error type ({type(e).__name__}) in session load: {e}")
 
     def _save(self):
         try:
             self.session_dir.mkdir(parents=True, exist_ok=True)
             with open(self.session_file, "w", encoding="utf-8") as f:
                 json.dump(self.data, f)
-        except Exception as e:
+        except OSError as e:
             logger.error(f"Session save failed: {e}")
+        except Exception as e:
+            logger.warning(f"Unexpected error type ({type(e).__name__}) in session save: {e}")
 
     def authenticate_user(self, user_id: str, email: str) -> bool:
-        self.data.update({
-            "authenticated": True,
-            "user_id": str(user_id),
-            "user_email": email,
-            "created_at": datetime.utcnow().isoformat(),
-            "last_activity": datetime.utcnow().isoformat(),
-        })
+        self.data.update(
+            {
+                "authenticated": True,
+                "user_id": str(user_id),
+                "user_email": email,
+                "created_at": datetime.now(UTC).isoformat(),
+                "last_activity": datetime.now(UTC).isoformat(),
+            }
+        )
         st.session_state.update(self.data)
         self._save()
         return True
@@ -91,7 +101,7 @@ def set_current_page(self, page: str):
         self._save()
 
     def get_current_page(self) -> str:
-        return self.data.get("current_page", "Transform")
+        return self.data.get("current_page", DEFAULT_PAGE)
 
     def set_pipeline_active(self, active: bool):
         self.data["pipeline_active"] = active
@@ -109,6 +119,7 @@ def get_session_info(self):
             "last_activity": self.data.get("last_activity"),
         }
 
+
 def get_session_manager() -> SessionManager:
     if "_session_manager" not in st.session_state:
         st.session_state._session_manager = SessionManager()
diff --git a/core/streaming_pipeline.py b/core/streaming_pipeline.py
index 7b3f526..878aad8 100644
--- a/core/streaming_pipeline.py
+++ b/core/streaming_pipeline.py
@@ -3,32 +3,42 @@
 Enables real-time progress updates and content streaming during processing
 """
 
-import streamlit as st
 import time
-from typing import Dict, Optional, Any
-from datetime import datetime
+from typing import Any
+
+import openai
+import streamlit as st
+from postgrest.exceptions import APIError
+
+from .constants import LARGE_FILE_THRESHOLD_MB
 from .content_generation import (
-    transcribe_audio, generate_wisdom, generate_outline, generate_article,
-    generate_social_content, generate_image_prompts, editor_critique
+    ContentGenerationError,
+    generate_article,
+    generate_outline,
+    generate_social_content,
+    generate_wisdom,
+    transcribe_audio,
 )
-from .research_enrichment import generate_research_enrichment
-from .visible_thinking import thinking_step_start, thinking_step_complete, thinking_error, render_thinking_stream
-# Removed old complex progress tracker - using simple progress bars now
+from .exceptions import DatabaseError, PipelineError
+from .visible_thinking import thinking_error, thinking_step_complete, thinking_step_start
 
 
 class StreamingPipelineController:
     """Controls step-by-step pipeline execution with real-time UI updates"""
-    
-    # Define pipeline steps as class constant
+
     PIPELINE_STEPS = [
-        "upload_validation", "transcription", "wisdom_extraction", 
-        "research_enrichment", "outline_creation", "article_creation", 
-        "social_content", "image_prompts", "database_storage"
+        "upload_validation",
+        "transcription",
+        "wisdom_extraction",
+        "outline_creation",
+        "article_creation",
+        "social_content",
+        "database_storage",
     ]
-    
+
     def __init__(self):
         self.reset_pipeline()
-    
+
     def reset_pipeline(self):
         """Reset pipeline state for new processing"""
         st.session_state.pipeline_active = False
@@ -36,78 +46,66 @@ def reset_pipeline(self):
         st.session_state.pipeline_results = {}
         st.session_state.pipeline_errors = {}
         st.session_state.pipeline_audio_file = None
-        
+
     def start_pipeline(self, audio_file):
         """Initialize pipeline for processing with large file support"""
         self.reset_pipeline()
         st.session_state.pipeline_active = True
         st.session_state.pipeline_audio_file = audio_file
-        
-        # Store file info for later use
+
         file_size_mb = len(audio_file.getvalue()) / (1024 * 1024)
         st.session_state.pipeline_file_info = {
             "name": audio_file.name,
             "size": len(audio_file.getvalue()),
             "size_mb": file_size_mb,
-            "is_large_file": file_size_mb > 20  # Flag for large file processing
+            "is_large_file": file_size_mb > LARGE_FILE_THRESHOLD_MB,
         }
-        
-        # Initialize required session state if missing
-        if not hasattr(st.session_state, 'prompts'):
+
+        if not hasattr(st.session_state, "prompts"):
             st.session_state.prompts = {}
-        if not hasattr(st.session_state, 'knowledge_base'):
+        if not hasattr(st.session_state, "knowledge_base"):
             st.session_state.knowledge_base = {}
-        if not hasattr(st.session_state, 'ai_provider'):
-            st.session_state.ai_provider = "OpenAI"
-        if not hasattr(st.session_state, 'ai_model'):
-            st.session_state.ai_model = "gpt-4o"
-    
+
     def process_next_step(self):
         """Process the next step in the pipeline"""
         if not st.session_state.pipeline_active:
             return False
-            
+
         step_index = st.session_state.pipeline_step_index
-        
+
         if step_index >= len(self.PIPELINE_STEPS):
-            # Pipeline complete
             st.session_state.pipeline_active = False
             return False
-        
+
         step_id = self.PIPELINE_STEPS[step_index]
-        
+
         try:
-            # Show immediate status update
             with st.status(f"Processing {step_id.replace('_', ' ').title()}...", expanded=True):
                 st.write(f"Step {step_index + 1} of {len(self.PIPELINE_STEPS)}: {step_id.replace('_', ' ')}")
-                
-                # Process the step
                 result = self._execute_step(step_id, step_index)
-                
-                # Store result
                 st.session_state.pipeline_results[step_id] = result
-                
-                st.write("✅ Complete!")
-            
-            # Move to next step
+                st.write("Complete!")
+
             st.session_state.pipeline_step_index += 1
-            
             return True
-            
-        except Exception as e:
-            # Handle step error
+
+        except (ContentGenerationError, openai.APIError, ValueError) as e:
             error_msg = str(e)
             st.session_state.pipeline_errors[step_id] = error_msg
             st.session_state.pipeline_active = False
-            st.error(f"❌ Error in {step_id}: {error_msg}")
+            st.error(f"Error in {step_id}: {error_msg}")
             return False
-    
+        except PipelineError as e:
+            error_msg = str(e)
+            st.session_state.pipeline_errors[step_id] = error_msg
+            st.session_state.pipeline_active = False
+            st.error(f"Unexpected error in {step_id}: {error_msg}")
+            return False
+
     def _execute_step(self, step_id: str, step_index: int) -> Any:
         """Execute a specific pipeline step"""
-        
-        # Add visible thinking at step start
         thinking_step_start(step_id)
-        
+
         try:
             if step_id == "upload_validation":
                 result = self._step_upload_validation()
@@ -115,426 +113,222 @@ def _execute_step(self, step_id: str, step_index: int) -> Any:
                 result = self._step_transcription()
             elif step_id == "wisdom_extraction":
                 result = self._step_wisdom_extraction()
-            elif step_id == "research_enrichment":
-                result = self._step_research_enrichment()
             elif step_id == "outline_creation":
                 result = self._step_outline_creation()
             elif step_id == "article_creation":
                 result = self._step_article_creation()
             elif step_id == "social_content":
                 result = self._step_social_content()
-            elif step_id == "image_prompts":
-                result = self._step_image_prompts()
             elif step_id == "database_storage":
                 result = self._step_database_storage()
             else:
-                raise Exception(f"Unknown step: {step_id}")
-            
-            # Add success thinking
+                raise ValueError(f"Unknown step: {step_id}")
+
             thinking_step_complete(step_id)
             return result
-            
-        except Exception as e:
-            # Add error thinking
+
+        except (ContentGenerationError, openai.APIError, ValueError) as e:
             thinking_error(step_id, str(e))
             raise
-    
-    def _step_upload_validation(self) -> Dict[str, Any]:
+        except PipelineError as e:
+            thinking_error(step_id, f"Unexpected: {e!s}")
+            raise
+
+    def _step_upload_validation(self) -> dict[str, Any]:
         """Step 1: Validate uploaded file"""
         file_info = st.session_state.pipeline_file_info
-        
-        if file_info["size_mb"] > 25:
-            raise Exception(f"File too large: {file_info['size_mb']:.1f}MB (max 25MB)")
-        
-        # Simulate validation time
+
+        from .config import get_config
+
+        max_file_size_mb = get_config().audio_chunk_size_mb
+        if file_info["size_mb"] > max_file_size_mb:
+            raise ValueError(f"File too large: {file_info['size_mb']:.1f}MB (max {max_file_size_mb}MB)")
+
         time.sleep(0.5)
-        
-        return {
-            "status": "validated",
-            "file_name": file_info["name"],
-            "file_size_mb": file_info["size_mb"]
-        }
-    
+
+        return {"status": "validated", "file_name": file_info["name"], "file_size_mb": file_info["size_mb"]}
+
     def _step_transcription(self) -> str:
         """Step 2: Transcribe audio with large file support"""
         audio_file = st.session_state.pipeline_audio_file
         file_info = st.session_state.pipeline_file_info
-        
-        # Check if this is a large file requiring chunked processing
+
         if file_info.get("is_large_file", False):
             return self._transcribe_large_file(audio_file)
         else:
             return self._transcribe_small_file(audio_file)
-    
+
     def _transcribe_small_file(self, audio_file) -> str:
         """Transcribe small files directly"""
-        from .content_generation import transcribe_audio
-        
-        st.write("🎵 **Processing small file directly...**")
-        
+        st.write("Processing small file directly...")
+
         transcript = transcribe_audio(audio_file)
         if not transcript:
-            raise Exception("Failed to transcribe audio - transcript is empty")
-        
-        if "Error" in transcript:
-            raise Exception(f"Transcription failed: {transcript}")
-        
-        # Store in session for access by later steps
+            raise ValueError("Failed to transcribe audio - transcript is empty")
+
         st.session_state.pipeline_transcript = transcript
         return transcript
-    
+
     def _transcribe_large_file(self, audio_file) -> str:
-        """🚀 Transcribe large files using chunked processing"""
-        from .file_upload import LargeFileUploadManager
-        
-        st.write("🚀 **Processing large file with chunked transcription...**")
-        
-        # Create upload manager for large file processing
-        upload_manager = LargeFileUploadManager()
-        
-        # Process the large file with chunking
-        result = upload_manager.process_large_file(audio_file)
-        
+        """Transcribe large files using chunked processing"""
+        from .file_upload import EnhancedLargeFileProcessor
+
+        st.write("Processing large file with chunked transcription...")
+
+        processor = EnhancedLargeFileProcessor()
+        result = processor.process_large_file(audio_file)
+
         if not result["success"]:
-            raise Exception(f"Large file transcription failed: {result['error']}")
-        
+            raise ValueError(f"Large file transcription failed: {result['error']}")
+
         transcript = result["transcript"]
-        
         if not transcript:
-            raise Exception("Large file transcription produced empty result")
-        
-        # Show processing summary
+            raise ValueError("Large file transcription produced empty result")
+
         st.success(f"""
-        ✅ **Large File Transcription Complete!**
-        - **Chunks processed:** {result.get('chunks', 'N/A')}
-        - **Processing time:** {result.get('processing_time', 'N/A')}
-        - **Transcript length:** {len(transcript)} characters
+        Large File Transcription Complete!
+        - Chunks processed: {result.get("chunks", "N/A")}
+        - Processing time: {result.get("processing_time", "N/A")}
+        - Transcript length: {len(transcript)} characters
         """)
-        
-        # Store in session for access by later steps
+
         st.session_state.pipeline_transcript = transcript
         return transcript
-    
+
     def _step_wisdom_extraction(self) -> str:
         """Step 3: Extract wisdom"""
         transcript = st.session_state.pipeline_transcript
-        
-        # Get custom prompt if available
-        custom_prompt = st.session_state.prompts.get("wisdom_extraction") if hasattr(st.session_state, 'prompts') else None
-        
+
+        custom_prompt = (
+            st.session_state.prompts.get("wisdom_extraction") if hasattr(st.session_state, "prompts") else None
+        )
+
         wisdom = generate_wisdom(
-            transcript, 
-            st.session_state.ai_provider, 
-            st.session_state.ai_model, 
-            custom_prompt, 
-            st.session_state.knowledge_base
+            transcript, custom_prompt=custom_prompt, knowledge_base=st.session_state.knowledge_base
         )
-        
-        # Handle editor mode
-        if st.session_state.get("editor_enabled", False):
-            critique = editor_critique(
-                wisdom, "wisdom_extraction", 
-                st.session_state.ai_provider, 
-                st.session_state.ai_model,
-                st.session_state.knowledge_base
-            )
-            
-            # Store critique for display
-            st.session_state.pipeline_results["wisdom_critique"] = critique
-            
-            # Generate revision based on critique
-            revision_prompt = f"""Based on this editorial feedback, please revise the wisdom extraction:
-
-EDITORIAL FEEDBACK:
-{critique}
-
-ORIGINAL WISDOM:
-{wisdom}
-
-Please provide an improved version that addresses the feedback."""
-            
-            wisdom = generate_wisdom(
-                transcript, 
-                st.session_state.ai_provider, 
-                st.session_state.ai_model, 
-                revision_prompt, 
-                st.session_state.knowledge_base
-            )
-        
-        # Store in session for later steps AND results for display
+
         st.session_state.pipeline_wisdom = wisdom
         return wisdom
-    
-    def _step_research_enrichment(self) -> Dict[str, Any]:
-        """Step 3.5: Research Enrichment - NEW STEP"""
-        wisdom = st.session_state.pipeline_wisdom
-        transcript = st.session_state.pipeline_transcript
-        
-        # Check if research enrichment is enabled (default True for paid users)
-        research_enabled = st.session_state.get("research_enabled", True)
-        
-        # Generate research enrichment
-        research_data = generate_research_enrichment(
-            wisdom=wisdom,
-            transcript=transcript,
-            ai_provider=st.session_state.ai_provider,
-            ai_model=st.session_state.ai_model,
-            enabled=research_enabled
-        )
-        
-        # Store in session for access by later steps
-        st.session_state.pipeline_research = research_data
-        return research_data
-    
+
     def _step_outline_creation(self) -> str:
         """Step 4: Create outline"""
         transcript = st.session_state.pipeline_transcript
         wisdom = st.session_state.pipeline_wisdom
-        research = st.session_state.pipeline_results.get("research_enrichment", {})
-        
-        # Get custom prompt if available
-        custom_prompt = st.session_state.prompts.get("outline_creation") if hasattr(st.session_state, 'prompts') else None
-        
+
+        custom_prompt = (
+            st.session_state.prompts.get("outline_creation") if hasattr(st.session_state, "prompts") else None
+        )
+
         outline = generate_outline(
-            transcript,
-            wisdom,
-            research, 
-            st.session_state.ai_provider, 
-            st.session_state.ai_model, 
-            custom_prompt, 
-            st.session_state.knowledge_base
+            transcript, wisdom, custom_prompt=custom_prompt, knowledge_base=st.session_state.knowledge_base
         )
-        
-        # Handle editor mode
-        if st.session_state.get("editor_enabled", False):
-            critique = editor_critique(
-                outline, "outline_creation", 
-                st.session_state.ai_provider, 
-                st.session_state.ai_model,
-                st.session_state.knowledge_base
-            )
-            
-            st.session_state.pipeline_results["outline_critique"] = critique
-            
-            revision_prompt = f"""Based on this editorial feedback, please revise the outline:
-
-EDITORIAL FEEDBACK:
-{critique}
-
-ORIGINAL OUTLINE:
-{outline}
-
-Please provide an improved version that addresses the feedback."""
-            
-            outline = generate_outline(
-                transcript,
-                wisdom,
-                research, 
-                st.session_state.ai_provider, 
-                st.session_state.ai_model, 
-                revision_prompt, 
-                st.session_state.knowledge_base
-            )
-        
-        # Store in session for later steps AND results for display
+
         st.session_state.pipeline_outline = outline
         return outline
-    
+
     def _step_article_creation(self) -> str:
         """Step 5: Create article"""
         transcript = st.session_state.pipeline_transcript
         wisdom = st.session_state.pipeline_wisdom
         outline = st.session_state.pipeline_outline
-        
-        # Get custom prompt if available  
-        custom_prompt = st.session_state.prompts.get("article_creation") if hasattr(st.session_state, 'prompts') else None
-        
+
+        custom_prompt = (
+            st.session_state.prompts.get("article_creation") if hasattr(st.session_state, "prompts") else None
+        )
+
         article = generate_article(
-            transcript,
-            wisdom, 
-            outline, 
-            st.session_state.ai_provider, 
-            st.session_state.ai_model, 
-            custom_prompt, 
-            st.session_state.knowledge_base
+            transcript, wisdom, outline, custom_prompt=custom_prompt, knowledge_base=st.session_state.knowledge_base
         )
-        
-        # Handle editor mode
-        if st.session_state.get("editor_enabled", False):
-            critique = editor_critique(
-                article, "article_writing", 
-                st.session_state.ai_provider, 
-                st.session_state.ai_model,
-                st.session_state.knowledge_base
-            )
-            
-            st.session_state.pipeline_results["article_critique"] = critique
-            
-            revision_prompt = f"""Based on this editorial feedback, please revise the article:
-
-EDITORIAL FEEDBACK:
-{critique}
-
-ORIGINAL ARTICLE:
-{article}
-
-Please provide an improved version that addresses the feedback."""
-            
-            article = generate_article(
-                transcript,
-                wisdom, 
-                outline, 
-                st.session_state.ai_provider, 
-                st.session_state.ai_model, 
-                revision_prompt, 
-                st.session_state.knowledge_base
-            )
-        
+
         st.session_state.pipeline_article = article
         return article
-    
+
     def _step_social_content(self) -> str:
         """Step 6: Generate social media content"""
         wisdom = st.session_state.pipeline_wisdom
         outline = st.session_state.pipeline_outline
         article = st.session_state.pipeline_article
-        
-        # Get custom prompt if available
-        custom_prompt = st.session_state.prompts.get("social_media") if hasattr(st.session_state, 'prompts') else None
-        
+
+        custom_prompt = st.session_state.prompts.get("social_media") if hasattr(st.session_state, "prompts") else None
+
         social = generate_social_content(
-            wisdom, 
-            outline,
-            article, 
-            st.session_state.ai_provider, 
-            st.session_state.ai_model, 
-            custom_prompt, 
-            st.session_state.knowledge_base
+            wisdom, outline, article, custom_prompt=custom_prompt, knowledge_base=st.session_state.knowledge_base
         )
-        
-        # Handle editor mode
-        if st.session_state.get("editor_enabled", False):
-            critique = editor_critique(
-                social, "social_media", 
-                st.session_state.ai_provider, 
-                st.session_state.ai_model,
-                st.session_state.knowledge_base
-            )
-            
-            st.session_state.pipeline_results["social_critique"] = critique
-            
-            revision_prompt = f"""Based on this editorial feedback, please revise the social media content:
-
-EDITORIAL FEEDBACK:
-{critique}
-
-ORIGINAL SOCIAL CONTENT:
-{social}
-
-Please provide improved versions that address the feedback."""
-            
-            social = generate_social_content(
-                wisdom, 
-                outline,
-                article, 
-                st.session_state.ai_provider, 
-                st.session_state.ai_model, 
-                revision_prompt, 
-                st.session_state.knowledge_base
-            )
-        
+
         st.session_state.pipeline_social = social
         return social
-    
-    def _step_image_prompts(self) -> str:
-        """Step 7: Generate image prompts"""
-        wisdom = st.session_state.pipeline_wisdom
-        outline = st.session_state.pipeline_outline
-        
-        # Get custom prompt if available
-        custom_prompt = st.session_state.prompts.get("image_prompts") if hasattr(st.session_state, 'prompts') else None
-        
-        images = generate_image_prompts(
-            wisdom, 
-            outline, 
-            st.session_state.ai_provider, 
-            st.session_state.ai_model, 
-            custom_prompt, 
-            st.session_state.knowledge_base
-        )
-        
-        st.session_state.pipeline_images = images
-        return images
-    
+
     def _step_database_storage(self) -> str:
-        """Step 8: Store content in database"""
+        """Step 7: Store content in database"""
         try:
-            # Direct Supabase access to avoid circular imports
             from .supabase_integration import get_supabase_client
-            
+
             db = get_supabase_client()
             if not db:
                 return "Database connection failed"
-            
-            # Get results with correct step names
+
             results = st.session_state.pipeline_results
-            
-            # Direct database insert with CORRECT field names
-            result = db.client.table("content").insert({
-                "user_id": st.session_state.user_id,
-                "title": f"Content from {st.session_state.pipeline_file_info['name']}",
-                "transcript": results.get("transcription", ""),
-                "wisdom": results.get("wisdom_extraction", ""), 
-                "outline": results.get("outline_creation", ""),
-                "article": results.get("article_creation", ""),
-                "social_content": results.get("social_content", ""),
-                "created_at": "now()"
-            }).execute()
-            
+
+            result = (
+                db.client.table("content")
+                .insert(
+                    {
+                        "user_id": st.session_state.user_id,
+                        "title": f"Content from {st.session_state.pipeline_file_info['name']}",
+                        "transcript": results.get("transcription", ""),
+                        "wisdom": results.get("wisdom_extraction", ""),
+                        "outline": results.get("outline_creation", ""),
+                        "article": results.get("article_creation", ""),
+                        "social_content": results.get("social_content", ""),
+                        "created_at": "now()",
+                    }
+                )
+                .execute()
+            )
+
             content_id = result.data[0]["id"] if result.data else ""
             if not content_id:
                 return "Failed to save content to database"
-            
-            time.sleep(0.3)  # Simulate save time
+
+            time.sleep(0.3)
             return f"Content saved with ID: {content_id}"
-            
-        except Exception as e:
-            # Don't fail the pipeline for database errors
+
+        except APIError as e:
             return f"Database save failed: {str(e)}"
-    
+        except DatabaseError as e:
+            return f"Unexpected database error: {str(e)}"
+
     @property
     def is_active(self) -> bool:
         """Check if pipeline is currently active"""
         return st.session_state.get("pipeline_active", False)
-    
+
     @property
     def is_complete(self) -> bool:
         """Check if pipeline has completed"""
-        return (not self.is_active and 
-                st.session_state.get("pipeline_step_index", 0) >= len(self.PIPELINE_STEPS))
-    
+        return not self.is_active and st.session_state.get("pipeline_step_index", 0) >= len(self.PIPELINE_STEPS)
+
     @property
     def current_step_index(self) -> int:
         """Get current step index"""
         return st.session_state.get("pipeline_step_index", 0)
-    
+
     @property
     def progress_percentage(self) -> float:
         """Get overall progress percentage"""
         return (self.current_step_index / len(self.PIPELINE_STEPS)) * 100
-    
-    def get_results(self) -> Dict[str, Any]:
+
+    def get_results(self) -> dict[str, Any]:
         """Get all pipeline results"""
         return st.session_state.get("pipeline_results", {})
-    
-    def get_errors(self) -> Dict[str, str]:
+
+    def get_errors(self) -> dict[str, str]:
         """Get any pipeline errors"""
         return st.session_state.get("pipeline_errors", {})
 
 
-# Global pipeline controller instance
 def get_pipeline_controller() -> StreamingPipelineController:
     """Get or create the global pipeline controller"""
-    if 'pipeline_controller' not in st.session_state:
+    if "pipeline_controller" not in st.session_state:
         st.session_state.pipeline_controller = StreamingPipelineController()
-    return st.session_state.pipeline_controller 
\ No newline at end of file
+    return st.session_state.pipeline_controller
diff --git a/core/streaming_results.py b/core/streaming_results.py
index fcd71b0..a8775b2 100644
--- a/core/streaming_results.py
+++ b/core/streaming_results.py
@@ -1,465 +1,247 @@
 """
-Streaming Results Display for WhisperForge
-Shows content as it's generated with beautiful Aurora styling
+Streaming Results Display
+=========================
+
+Shows content as it's generated with Aurora styling.
+CSS loaded from static/css/streaming.css.
 """
 
-import streamlit as st
-import html
+import os
 import time
-from typing import Dict, Any, Optional
-from .streaming_pipeline import get_pipeline_controller
 import uuid
-from .visible_thinking import render_thinking_stream
-
-# CSS for streaming results
-STREAMING_RESULTS_CSS = """
-<style>
-/* Aurora Progress Animation */
-@keyframes aurora-flow {
-    0%, 100% { left: -100%; opacity: 0; }
-    25% { opacity: 1; }
-    75% { opacity: 1; }
-    100% { left: 100%; opacity: 0; }
-}
-
-@keyframes aurora-pulse {
-    0%, 100% { opacity: 1; transform: scale(1); }
-    50% { opacity: 0.7; transform: scale(1.1); }
-}
-
-@keyframes completion-glow {
-    0%, 100% { left: -100%; opacity: 0; }
-    20% { opacity: 1; }
-    80% { opacity: 1; }
-    100% { left: 100%; opacity: 0; }
-}
-
-/* Enhanced Button Styling */
-.stButton > button {
-    background: linear-gradient(135deg, rgba(0, 255, 255, 0.1), rgba(64, 224, 208, 0.15)) !important;
-    border: 1px solid rgba(0, 255, 255, 0.2) !important;
-    color: rgba(255, 255, 255, 0.9) !important;
-    border-radius: 8px !important;
-    font-weight: 500 !important;
-    transition: all 0.3s ease !important;
-}
-
-.stButton > button:hover {
-    background: linear-gradient(135deg, rgba(0, 255, 255, 0.15), rgba(64, 224, 208, 0.2)) !important;
-    border-color: rgba(0, 255, 255, 0.3) !important;
-    color: white !important;
-    transform: translateY(-1px);
-    box-shadow: 0 4px 15px rgba(0, 255, 255, 0.15);
-}
-
-/* Expander Styling */
-.streamlit-expanderHeader {
-    background: rgba(0, 255, 255, 0.03) !important;
-    border: 1px solid rgba(0, 255, 255, 0.1) !important;
-    border-radius: 8px !important;
-}
-
-.streamlit-expanderContent {
-    background: rgba(0, 255, 255, 0.02) !important;
-    border: 1px solid rgba(0, 255, 255, 0.1) !important;
-    border-top: none !important;
-    border-radius: 0 0 8px 8px !important;
-}
-</style>
-"""
 
-# Enhanced UI Functions for streaming results
+import streamlit as st
+
+from .export import create_json_download, create_markdown_download, create_text_download
+from .streaming_pipeline import get_pipeline_controller
+from .streaming_status import (  # noqa: F401 - re-exports
+    show_2025_content_display,
+    show_enhanced_streaming_status,
+    show_processing_status,
+)
+
+_STREAMING_CSS_CACHE = None
+
+
+def _load_streaming_css():
+    """Load streaming CSS from static file (cached)."""
+    global _STREAMING_CSS_CACHE  # noqa: PLW0603
+    if _STREAMING_CSS_CACHE is None:
+        css_path = os.path.join("static", "css", "streaming.css")
+        if os.path.exists(css_path):
+            with open(css_path, encoding="utf-8") as f:
+                _STREAMING_CSS_CACHE = f"<style>{f.read()}</style>"
+        else:
+            _STREAMING_CSS_CACHE = ""
+    return _STREAMING_CSS_CACHE
+
+
 def apply_streaming_css():
-    """Apply Aurora theme CSS for streaming results"""
+    """Apply Aurora theme CSS for streaming results."""
+    st.markdown(_load_streaming_css(), unsafe_allow_html=True)
+
 
-# Generate truly unique keys for Streamlit widgets
 def generate_unique_key(base_name: str) -> str:
-    """Generate truly unique key for Streamlit widgets to prevent DuplicateWidgetID errors"""
+    """Generate truly unique key for Streamlit widgets to prevent DuplicateWidgetID errors."""
     return f"{base_name}_{uuid.uuid4().hex[:8]}_{int(time.time() * 1000000) % 1000000}"
 
+
 def show_streaming_results():
-    """Display content as it streams - REAL-TIME STREAMING IMPLEMENTATION"""
+    """Display content as it streams - real-time streaming implementation."""
     controller = get_pipeline_controller()
     results = controller.get_results()
-    
+
     if not results:
-        # Show placeholder while waiting for first results
-        st.markdown("### 🌊 Live Content Stream")
-        st.info("🔄 Waiting for processing to begin...")
+        st.markdown("### \U0001f30a Live Content Stream")
+        st.info("\U0001f504 Waiting for processing to begin...")
         return
-    
-    # Show real-time streaming content with smooth reveals
+
     show_real_time_content_stream(results, controller)
 
 
-def show_real_time_content_stream(results: Dict[str, Any], controller):
-    """🚀 ENHANCED: Real-time content streaming with step-by-step reveals"""
-    st.markdown("### ✨ Content Generation Stream")
-    
-    # Define content sections with order and styling
-    content_sections = [
-        ("transcription", "🎙️", "Audio Transcription", "Converting speech to text..."),
-        ("wisdom_extraction", "💎", "Key Insights & Wisdom", "Extracting valuable insights..."),
-        ("research_enrichment", "🔍", "Research Links", "Finding supporting resources..."),
-        ("outline_creation", "📋", "Content Outline", "Structuring content flow..."),
-        ("article_creation", "📰", "Full Article", "Writing comprehensive article..."),
-        ("social_content", "📱", "Social Media Posts", "Creating social content..."),
-        ("image_prompts", "🖼️", "Image Prompts", "Generating visual concepts..."),
-        ("database_storage", "💾", "Content Saved", "Storing to your library...")
-    ]
-    
-    # Show each section as it becomes available
-    for i, (step_key, icon, title, processing_msg) in enumerate(content_sections):
-        
-        if step_key in results and results[step_key]:
-            # Content is ready - show it with beautiful styling
-            show_completed_content_section(step_key, icon, title, results[step_key])
-            
-        elif controller.current_step_index == i and controller.is_active:
-            # Currently processing this step - show loading state
-            show_processing_content_section(icon, title, processing_msg)
-            
-        elif controller.current_step_index > i:
-            # This step should be done but no content - show error state
-            show_error_content_section(icon, title, "Content generation failed")
-            
-        # Don't show future steps to avoid spoilers
-
-
-def show_completed_content_section(step_key: str, icon: str, title: str, content: Any):
-    """Display completed content with beautiful Aurora styling"""
-    
-    # Convert content to string safely
-    content_str = str(content) if content else "No content generated"
-    
-    # Beautiful content reveal with animation
-    st.markdown(f"""
-    <div style="
-        background: linear-gradient(135deg, rgba(0, 255, 255, 0.05), rgba(64, 224, 208, 0.08));
-        border: 1px solid rgba(0, 255, 255, 0.2);
-        border-radius: 12px;
-        padding: 20px;
-        margin: 16px 0;
-        animation: contentReveal 0.6s ease-out;
-        position: relative;
-        overflow: hidden;
-    ">
-        <div style="
-            position: absolute;
-            top: 0;
-            left: -100%;
-            width: 100%;
-            height: 100%;
-            background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.1), transparent);
-            animation: shimmer 2s ease-in-out;
-        "></div>
-        <div style="position: relative; z-index: 2;">
-            <div style="display: flex; align-items: center; margin-bottom: 12px;">
-                <span style="font-size: 1.4rem; margin-right: 10px;">{icon}</span>
-                <h4 style="color: #00FFFF; margin: 0; font-weight: 500;">{title}</h4>
-                <span style="margin-left: auto; color: #00FF7F; font-size: 0.9rem;">✅ Complete</span>
-            </div>
+def show_real_time_content_stream(results, controller):
+    """Show real-time streaming content with smooth reveals."""
+    apply_streaming_css()
+
+    step_map = {
+        "transcription": ("\U0001f399\ufe0f", "Audio Transcription"),
+        "wisdom_extraction": ("\U0001f48e", "Wisdom & Key Insights"),
+        "research_enrichment": ("\U0001f50d", "Research Enrichment"),
+        "outline_creation": ("\U0001f4cb", "Content Outline"),
+        "article_creation": ("\U0001f4f0", "Full Article"),
+        "social_content": ("\U0001f4f1", "Social Media Content"),
+        "image_prompts": ("\U0001f5bc\ufe0f", "Image Prompts"),
+    }
+
+    for step_key, (icon, title) in step_map.items():
+        content = results.get(step_key)
+        errors = controller.get_errors() if hasattr(controller, "get_errors") else {}
+
+        if content:
+            show_completed_content_section(step_key, icon, title, content)
+        elif step_key in errors:
+            show_error_content_section(icon, title, errors[step_key])
+        elif controller.is_active and step_key == list(step_map.keys())[controller.current_step_index]:
+            show_processing_content_section(icon, title, f"Generating {title.lower()}...")
+
+    if controller.is_complete:
+        _show_download_options(results)
+
+
+def show_completed_content_section(step_key: str, icon: str, title: str, content):
+    """Display a completed content section with Aurora styling."""
+    content_str = str(content)
+    word_count = len(content_str.split())
+    preview_length = 200
+
+    st.markdown(
+        f"""
+    <div class="aurora-content-card">
+        <div class="aurora-content-header">
+            <span class="aurora-content-title">{icon} {title}</span>
+            <span class="aurora-status-badge completed">\u2705 Complete \u2022 {word_count} words</span>
         </div>
     </div>
-    
-    <style>
-    @keyframes contentReveal {{
-        0% {{ opacity: 0; transform: translateY(20px); }}
-        100% {{ opacity: 1; transform: translateY(0); }}
-    }}
-    
-    @keyframes shimmer {{
-        0% {{ left: -100%; }}
-        100% {{ left: 100%; }}
-    }}
-    </style>
-    """, unsafe_allow_html=True)
-    
-    # Show content with smart preview/expand
-    if len(content_str) > 600:
-        # Long content - show preview with expand
-        st.markdown("**Preview:**")
-        preview_text = content_str[:300] + "..." if len(content_str) > 300 else content_str
-        st.markdown(preview_text)
-        
-        # Expandable full content
-        expand_key = generate_unique_key(f"expand_{step_key}")
-        with st.expander("📖 Show Full Content", expanded=False):
-            st.markdown(content_str)
-            
-            # Copy button
-            copy_key = generate_unique_key(f"copy_{step_key}")
-            if st.button(f"📋 Copy {title}", key=copy_key, use_container_width=True):
-                st.code(content_str, language="markdown")
-                st.success("✅ Copied to clipboard area!")
-    else:
-        # Short content - show directly
-        st.markdown(content_str)
-        
-        # Inline copy button
-        copy_key = generate_unique_key(f"copy_inline_{step_key}")
-        if st.button(f"📋 Copy {title}", key=copy_key):
-            st.code(content_str, language="markdown")
-            st.success("✅ Copied!")
-    
-    st.markdown("---")
+    """,
+        unsafe_allow_html=True,
+    )
+
+    with st.container():
+        if len(content_str) > preview_length:
+            with st.expander(f"View {title}", expanded=False):
+                st.markdown(content_str)
+
+                col1, col2 = st.columns([1, 4])
+                with col1:
+                    copy_key = generate_unique_key(f"copy_{step_key}")
+                    if st.button("\U0001f4cb Copy", key=copy_key, help=f"Copy {title}"):
+                        st.code(content_str, language="markdown")
+                        st.success("Content displayed - copy with Ctrl+A, Ctrl+C")
+
+                # Editor section for article
+                if step_key == "article_creation":
+                    st.markdown(
+                        """
+                    <div class="aurora-editor-section">
+                        <div class="aurora-editor-header">
+                            <span class="aurora-editor-title">\u270f\ufe0f Content Editor</span>
+                            <span class="aurora-editor-badge">EDIT MODE</span>
+                        </div>
+                    </div>
+                    """,
+                        unsafe_allow_html=True,
+                    )
+
+                    edit_key = generate_unique_key("edit_article")
+                    edited_content = st.text_area("Edit Article Content", value=content_str, height=400, key=edit_key)
+
+                    if edited_content != content_str:
+                        save_key = generate_unique_key("save_article")
+                        if st.button("\U0001f4be Save Changes", key=save_key):
+                            controller = get_pipeline_controller()
+                            controller.update_result(step_key, edited_content)
+                            st.success("\u2705 Article updated!")
+                            st.rerun()
+        else:
+            st.markdown(
+                f"""
+            <div class="aurora-content-body">
+                {content_str}
+            </div>
+            """,
+                unsafe_allow_html=True,
+            )
 
 
 def show_processing_content_section(icon: str, title: str, message: str):
-    """Show animated processing state for current step"""
-    
-    st.markdown(f"""
-    <div style="
-        background: linear-gradient(135deg, rgba(255, 165, 0, 0.05), rgba(255, 215, 0, 0.08));
-        border: 1px solid rgba(255, 165, 0, 0.3);
-        border-radius: 12px;
-        padding: 20px;
-        margin: 16px 0;
-        position: relative;
-        overflow: hidden;
-    ">
-        <div style="
-            position: absolute;
-            top: 0;
-            left: -100%;
-            width: 100%;
-            height: 100%;
-            background: linear-gradient(90deg, transparent, rgba(255, 165, 0, 0.2), transparent);
-            animation: processing 2s linear infinite;
-        "></div>
-        <div style="position: relative; z-index: 2;">
-            <div style="display: flex; align-items: center;">
-                <span style="font-size: 1.4rem; margin-right: 10px; animation: spin 2s linear infinite;">{icon}</span>
-                <div>
-                    <h4 style="color: #FFA500; margin: 0; font-weight: 500;">{title}</h4>
-                    <p style="color: rgba(255, 255, 255, 0.7); margin: 4px 0 0 0; font-size: 0.9rem;">{message}</p>
-                </div>
-                <div style="margin-left: auto;">
-                    <div style="
-                        width: 20px;
-                        height: 20px;
-                        border: 2px solid rgba(255, 165, 0, 0.3);
-                        border-top: 2px solid #FFA500;
-                        border-radius: 50%;
-                        animation: spin 1s linear infinite;
-                    "></div>
-                </div>
+    """Display a processing content section with animation."""
+    st.markdown(
+        f"""
+    <div class="aurora-content-card" style="border-color: rgba(0, 255, 255, 0.3);">
+        <div class="aurora-content-header">
+            <span class="aurora-content-title">{icon} {title}</span>
+            <span class="aurora-status-badge"
+                  style="background: rgba(0, 255, 255, 0.15); color: #00FFFF;
+                         border: 1px solid rgba(0, 255, 255, 0.3);
+                         animation: aurora-pulse 2s ease-in-out infinite;">
+                \U0001f504 Processing
+            </span>
+        </div>
+        <div class="aurora-content-body">
+            <div style="display: flex; align-items: center; gap: 12px; padding: 20px;">
+                <div style="
+                    width: 24px; height: 24px;
+                    border: 2px solid rgba(0, 255, 255, 0.3);
+                    border-top: 2px solid #00FFFF;
+                    border-radius: 50%;
+                    animation: spin 1s linear infinite;
+                "></div>
+                <span style="color: rgba(255, 255, 255, 0.7); font-style: italic;">
+                    {message}
+                </span>
             </div>
         </div>
     </div>
-    
+
     <style>
-    @keyframes processing {{
-        0% {{ left: -100%; }}
-        100% {{ left: 100%; }}
-    }}
-    
     @keyframes spin {{
         0% {{ transform: rotate(0deg); }}
         100% {{ transform: rotate(360deg); }}
     }}
     </style>
-    """, unsafe_allow_html=True)
+    """,
+        unsafe_allow_html=True,
+    )
 
 
 def show_error_content_section(icon: str, title: str, error_msg: str):
-    """Show error state for failed step"""
-    
-    st.markdown(f"""
-    <div style="
-        background: linear-gradient(135deg, rgba(255, 69, 58, 0.05), rgba(255, 105, 97, 0.08));
-        border: 1px solid rgba(255, 69, 58, 0.3);
-        border-radius: 12px;
-        padding: 20px;
-        margin: 16px 0;
-    ">
-        <div style="display: flex; align-items: center;">
-            <span style="font-size: 1.4rem; margin-right: 10px;">{icon}</span>
-            <div>
-                <h4 style="color: #FF6B6B; margin: 0; font-weight: 500;">{title}</h4>
-                <p style="color: rgba(255, 255, 255, 0.7); margin: 4px 0 0 0; font-size: 0.9rem;">{error_msg}</p>
-            </div>
-            <span style="margin-left: auto; color: #FF6B6B; font-size: 0.9rem;">❌ Failed</span>
+    """Display an error content section."""
+    st.markdown(
+        f"""
+    <div class="aurora-content-card" style="border-color: rgba(255, 100, 100, 0.3);">
+        <div class="aurora-content-header">
+            <span class="aurora-content-title">{icon} {title}</span>
+            <span class="aurora-status-badge"
+                  style="background: rgba(255, 100, 100, 0.15); color: #FF6B6B;
+                         border: 1px solid rgba(255, 100, 100, 0.3);">
+                \u274c Error
+            </span>
         </div>
-    </div>
-    """, unsafe_allow_html=True)
-
-
-def show_2025_content_display():
-    """🚀 Ultra-modern 2025 Aurora content display for completed results"""
-    controller = get_pipeline_controller()
-    results = controller.get_results()
-    
-    if not results:
-        return
-    
-    # Beautiful completion header
-    st.markdown("""
-    <div class="completion-showcase">
-        <div class="showcase-bg"></div>
-        <div class="showcase-content">
-            <h2>✨ Transformation Complete</h2>
-            <p>Your audio has been transformed into structured, actionable content</p>
-        </div>
-        <div class="showcase-orbs">
-            <div class="orb orb-1"></div>
-            <div class="orb orb-2"></div>
-            <div class="orb orb-3"></div>
+        <div class="aurora-content-body" style="color: rgba(255, 100, 100, 0.8);">
+            {error_msg}
         </div>
     </div>
-    
-    <style>
-    .completion-showcase {
-        position: relative;
-        background: linear-gradient(135deg, rgba(0, 255, 255, 0.08), rgba(64, 224, 208, 0.12));
-        border: 1px solid rgba(0, 255, 255, 0.2);
-        border-radius: 20px;
-        padding: 48px;
-        margin: 32px 0;
-        text-align: center;
-        overflow: hidden;
-    }
-    
-    .showcase-bg {
-        position: absolute;
-        top: 0;
-        left: 0;
-        right: 0;
-        bottom: 0;
-        background: radial-gradient(circle at 30% 70%, rgba(0, 255, 255, 0.1) 0%, transparent 50%),
-                    radial-gradient(circle at 70% 30%, rgba(64, 224, 208, 0.08) 0%, transparent 50%);
-        animation: bg-float 8s ease-in-out infinite;
-    }
-    
-    .showcase-content {
-        position: relative;
-        z-index: 2;
-    }
-    
-    .showcase-content h2 {
-        margin: 0 0 12px 0;
-        color: #00FFFF;
-        font-size: 2.2rem;
-        font-weight: 300;
-        letter-spacing: 0.05em;
-        text-shadow: 0 0 20px rgba(0, 255, 255, 0.3);
-    }
-    
-    .showcase-content p {
-        margin: 0;
-        color: rgba(255, 255, 255, 0.7);
-        font-size: 1.1rem;
-        letter-spacing: 0.02em;
-    }
-    
-    .showcase-orbs {
-        position: absolute;
-        top: 50%;
-        left: 50%;
-        transform: translate(-50%, -50%);
-        width: 200px;
-        height: 200px;
-        pointer-events: none;
-    }
-    
-    .orb {
-        position: absolute;
-        border-radius: 50%;
-        filter: blur(1px);
-    }
-    
-    .orb-1 {
-        width: 60px;
-        height: 60px;
-        background: radial-gradient(circle, rgba(0, 255, 255, 0.2), transparent);
-        top: 20%;
-        left: 20%;
-        animation: orb-float-1 6s ease-in-out infinite;
-    }
-    
-    .orb-2 {
-        width: 40px;
-        height: 40px;
-        background: radial-gradient(circle, rgba(64, 224, 208, 0.15), transparent);
-        top: 60%;
-        right: 25%;
-        animation: orb-float-2 8s ease-in-out infinite reverse;
-    }
-    
-    .orb-3 {
-        width: 30px;
-        height: 30px;
-        background: radial-gradient(circle, rgba(0, 255, 200, 0.1), transparent);
-        bottom: 30%;
-        left: 30%;
-        animation: orb-float-3 7s ease-in-out infinite;
-    }
-    
-    @keyframes bg-float {
-        0%, 100% { transform: translateY(0px) rotate(0deg); }
-        50% { transform: translateY(-10px) rotate(2deg); }
-    }
-    
-    @keyframes orb-float-1 {
-        0%, 100% { transform: translate(0, 0) scale(1); }
-        33% { transform: translate(10px, -15px) scale(1.1); }
-        66% { transform: translate(-8px, 12px) scale(0.9); }
-    }
-    
-    @keyframes orb-float-2 {
-        0%, 100% { transform: translate(0, 0) scale(1); }
-        50% { transform: translate(-12px, -10px) scale(1.2); }
-    }
-    
-    @keyframes orb-float-3 {
-        0%, 100% { transform: translate(0, 0) scale(1); }
-        25% { transform: translate(8px, -8px) scale(0.8); }
-        75% { transform: translate(-6px, 10px) scale(1.1); }
-    }
-    </style>
-    """, unsafe_allow_html=True)
-    
-    # Display all results with modern cards
-    content_map = {
-        'transcription': ('🎙️', 'Audio Transcription', 'Complete speech-to-text conversion'),
-        'wisdom_extraction': ('💎', 'Key Insights & Wisdom', 'Extracted insights and actionable takeaways'),
-        'research_enrichment': ('🔍', 'Research Enrichment', 'Supporting links and contextual information'),
-        'outline_creation': ('📋', 'Content Outline', 'Structured organization and flow'),
-        'article_creation': ('📰', 'Full Article', 'Complete written content ready for publication'),
-        'social_content': ('📱', 'Social Media Content', 'Platform-optimized posts and captions'),
-        'image_prompts': ('🖼️', 'Image Generation Prompts', 'AI-generated visual concept descriptions')
-    }
-    
-    for key, (icon, title, desc) in content_map.items():
-        if key in results and results[key]:
-            show_streaming_content_card(icon, title, desc, results[key], is_live=False)
+    """,
+        unsafe_allow_html=True,
+    )
 
 
 def show_streaming_content_card(icon: str, title: str, description: str, content: str, is_live: bool = False):
-    """🎨 Beautiful streaming content card with Aurora effects"""
-    
-    # Create unique key for this card
-    card_key = generate_unique_key(f"stream_card_{title.lower()}")
-    
-    # Live vs complete styling
+    """Beautiful streaming content card with Aurora effects."""
     border_color = "rgba(0, 255, 100, 0.2)" if is_live else "rgba(0, 255, 255, 0.15)"
-    bg_gradient = "rgba(0, 255, 100, 0.03), rgba(0, 255, 255, 0.05)" if is_live else "rgba(0, 255, 255, 0.03), rgba(64, 224, 208, 0.05)"
+    bg_gradient = (
+        "rgba(0, 255, 100, 0.03), rgba(0, 255, 255, 0.05)"
+        if is_live
+        else "rgba(0, 255, 255, 0.03), rgba(64, 224, 208, 0.05)"
+    )
     glow_color = "rgba(0, 255, 100, 0.4)" if is_live else "rgba(0, 255, 255, 0.3)"
-    
+
     with st.container():
-        st.markdown(f"""
-        <div class="aurora-stream-card {'live' if is_live else 'complete'}">
+        live_badge = '<div class="live-badge">LIVE</div>' if is_live else '<div class="complete-badge">\u2713</div>'
+        st.markdown(
+            f"""
+        <div class="aurora-stream-card {"live" if is_live else "complete"}">
             <div class="card-header">
                 <span class="card-icon">{icon}</span>
                 <div class="card-title-group">
                     <h4 class="card-title">{title}</h4>
                     <p class="card-description">{description}</p>
                 </div>
-                {'<div class="live-badge">LIVE</div>' if is_live else '<div class="complete-badge">✓</div>'}
+                {live_badge}
             </div>
             <div class="card-separator"></div>
         </div>
-        
+
         <style>
         .aurora-stream-card {{
             background: linear-gradient(135deg, {bg_gradient});
@@ -472,7 +254,7 @@ def show_streaming_content_card(icon: str, title: str, description: str, content
             transition: all 0.4s cubic-bezier(0.23, 1, 0.32, 1);
             backdrop-filter: blur(20px);
         }}
-        
+
         .aurora-stream-card::before {{
             content: "";
             position: absolute;
@@ -483,26 +265,26 @@ def show_streaming_content_card(icon: str, title: str, description: str, content
             background: linear-gradient(90deg, transparent, {glow_color}, transparent);
             animation: card-glow 4s ease-in-out infinite;
         }}
-        
+
         .aurora-stream-card.live::before {{
             animation: card-glow 2s ease-in-out infinite;
         }}
-        
+
         .card-header {{
             display: flex;
             align-items: center;
             gap: 16px;
         }}
-        
+
         .card-icon {{
             font-size: 1.8rem;
             filter: drop-shadow(0 0 8px {glow_color});
         }}
-        
+
         .card-title-group {{
             flex: 1;
         }}
-        
+
         .card-title {{
             margin: 0;
             color: #00FFFF;
@@ -510,13 +292,13 @@ def show_streaming_content_card(icon: str, title: str, description: str, content
             font-weight: 500;
             letter-spacing: 0.02em;
         }}
-        
+
         .card-description {{
             margin: 4px 0 0 0;
             color: rgba(255, 255, 255, 0.6);
             font-size: 0.85rem;
         }}
-        
+
         .live-badge {{
             background: linear-gradient(135deg, rgba(0, 255, 100, 0.2), rgba(0, 255, 100, 0.1));
             border: 1px solid rgba(0, 255, 100, 0.3);
@@ -528,7 +310,7 @@ def show_streaming_content_card(icon: str, title: str, description: str, content
             letter-spacing: 0.05em;
             animation: pulse-live 2s ease-in-out infinite;
         }}
-        
+
         .complete-badge {{
             background: linear-gradient(135deg, rgba(0, 255, 255, 0.2), rgba(0, 255, 255, 0.1));
             border: 1px solid rgba(0, 255, 255, 0.3);
@@ -541,455 +323,89 @@ def show_streaming_content_card(icon: str, title: str, description: str, content
             font-size: 0.8rem;
             color: #00FFFF;
         }}
-        
+
         .card-separator {{
             height: 1px;
             background: linear-gradient(90deg, transparent, {border_color}, transparent);
             margin: 16px 0 0 0;
         }}
-        
+
         @keyframes card-glow {{
             0%, 100% {{ left: -100%; opacity: 0; }}
             50% {{ left: 100%; opacity: 1; }}
         }}
-        
+
         @keyframes pulse-live {{
             0%, 100% {{ transform: scale(1); opacity: 1; }}
             50% {{ transform: scale(1.05); opacity: 0.8; }}
         }}
         </style>
-        """, unsafe_allow_html=True)
-        
-        # Content preview with smart truncation
+        """,
+            unsafe_allow_html=True,
+        )
+
         if len(content) > 300:
             preview = content[:300] + "..."
-            
-            # Expandable content
-            with st.expander(f"📖 Preview {title}", expanded=False):
+
+            with st.expander(f"\U0001f4d6 Preview {title}", expanded=False):
                 st.markdown(preview)
-                
-            with st.expander(f"📄 Full {title}", expanded=False):
+
+            with st.expander(f"\U0001f4c4 Full {title}", expanded=False):
                 st.markdown(content)
-                
-                # Copy button
+
                 copy_key = generate_unique_key(f"copy_{title}")
                 if st.button(f"Copy {title}", key=copy_key, help=f"Copy {title} to clipboard"):
                     st.code(content, language="markdown")
         else:
             st.markdown(content)
-            
-            # Copy button for short content
+
             copy_key = generate_unique_key(f"copy_short_{title}")
             if st.button(f"Copy {title}", key=copy_key, help=f"Copy {title} to clipboard"):
                 st.code(content, language="markdown")
 
 
-def _show_download_options(results: Dict[str, Any]):
-    """Show download options for generated content"""
-    
-    st.markdown("""
+def _show_download_options(results):
+    """Show download options for generated content."""
+    st.markdown(
+        """
     <div class="aurora-download-section">
         <div class="aurora-section-title">
-            <span>📥</span>
+            <span>\U0001f4e5</span>
             Download Options
         </div>
     </div>
-    """, unsafe_allow_html=True)
-    
-    # Create downloadable content formats
+    """,
+        unsafe_allow_html=True,
+    )
+
     formats = {
-        "JSON": _create_json_download(results),
-        "Markdown": _create_markdown_download(results),
-        "Text": _create_text_download(results)
+        "JSON": create_json_download(results),
+        "Markdown": create_markdown_download(results),
+        "Text": create_text_download(results),
     }
-    
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        if "JSON" in formats:
-            st.download_button(
-                "📄 JSON Format",
-                data=formats["JSON"],
-                file_name="whisperforge_results.json",
-                mime="application/json"
-            )
-    
-    with col2:
-        if "Markdown" in formats:
-            st.download_button(
-                "📝 Markdown Format", 
-                data=formats["Markdown"],
-                file_name="whisperforge_results.md",
-                mime="text/markdown"
-            )
-    
-    with col3:
-        if "Text" in formats:
-            st.download_button(
-                "📄 Text Format",
-                data=formats["Text"],
-                file_name="whisperforge_results.txt",
-                mime="text/plain"
-            )
-
-
-def _create_json_download(results: Dict[str, Any]) -> str:
-    """Create JSON format download"""
-    import json
-    return json.dumps(results, indent=2, ensure_ascii=False)
 
+    col1, col2, col3 = st.columns(3)
 
-def _create_markdown_download(results: Dict[str, Any]) -> str:
-    """Create Markdown format download"""
-    content = "# WhisperForge Content Generation Results\n\n"
-    
-    sections = {
-        "transcription": "## 📝 Audio Transcription\n\n",
-        "wisdom_extraction": "## 💎 Key Insights & Wisdom\n\n",
-        "outline_creation": "## 📋 Content Outline\n\n", 
-        "article_creation": "## 📰 Full Article\n\n",
-        "social_content": "## 📱 Social Media Content\n\n",
-        "image_prompts": "## 🖼️ Image Generation Prompts\n\n"
-    }
-    
-    for key, header in sections.items():
-        if key in results:
-            content += header + results[key] + "\n\n---\n\n"
-    
-    return content
-
-
-def _create_text_download(results: Dict[str, Any]) -> str:
-    """Create plain text format download"""
-    content = "WHISPERFORGE CONTENT GENERATION RESULTS\n"
-    content += "=" * 50 + "\n\n"
-    
-    sections = {
-        "transcription": "AUDIO TRANSCRIPTION\n" + "-" * 20 + "\n\n",
-        "wisdom_extraction": "KEY INSIGHTS & WISDOM\n" + "-" * 20 + "\n\n",
-        "outline_creation": "CONTENT OUTLINE\n" + "-" * 15 + "\n\n",
-        "article_creation": "FULL ARTICLE\n" + "-" * 12 + "\n\n", 
-        "social_content": "SOCIAL MEDIA CONTENT\n" + "-" * 20 + "\n\n",
-        "image_prompts": "IMAGE GENERATION PROMPTS\n" + "-" * 25 + "\n\n"
-    }
-    
-    for key, header in sections.items():
-        if key in results:
-            content += header + results[key] + "\n\n" + "=" * 50 + "\n\n"
-    
-    return content
-
-
-# Enhanced CSS for streaming results
-STREAMING_RESULTS_CSS = """
-<style>
-/* Aurora Streaming Results Styling */
-.aurora-content-card {
-    background: linear-gradient(135deg, rgba(0, 255, 255, 0.05), rgba(64, 224, 208, 0.08));
-    backdrop-filter: blur(24px) saturate(180%);
-    border: 1px solid rgba(0, 255, 255, 0.15);
-    border-radius: 16px;
-    padding: 24px;
-    margin: 16px 0;
-    position: relative;
-    overflow: hidden;
-    transition: all 0.4s cubic-bezier(0.4, 0.0, 0.2, 1);
-}
-
-.aurora-content-card::before {
-    content: "";
-    position: absolute;
-    top: 0;
-    left: -100%;
-    width: 100%;
-    height: 2px;
-    background: linear-gradient(90deg, transparent, #00FFFF, #40E0D0, transparent);
-    animation: aurora-scan 6s ease-in-out infinite;
-}
-
-.aurora-content-header {
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    margin-bottom: 16px;
-    padding-bottom: 12px;
-    border-bottom: 1px solid rgba(0, 255, 255, 0.1);
-}
-
-.aurora-content-title {
-    font-size: 1.1rem;
-    font-weight: 600;
-    color: rgba(255, 255, 255, 0.95);
-}
-
-.aurora-status-badge {
-    padding: 4px 12px;
-    border-radius: 12px;
-    font-size: 0.75rem;
-    font-weight: 500;
-    text-transform: uppercase;
-    letter-spacing: 0.05em;
-}
-
-.aurora-status-badge.completed {
-    background: rgba(0, 255, 127, 0.15);
-    color: #00FF7F;
-    border: 1px solid rgba(0, 255, 127, 0.3);
-}
-
-.aurora-content-body {
-    color: rgba(255, 255, 255, 0.85);
-    line-height: 1.6;
-    font-size: 0.95rem;
-}
-
-.aurora-editor-section {
-    background: rgba(255, 255, 127, 0.05);
-    border: 1px solid rgba(255, 255, 127, 0.15);
-    border-radius: 12px;
-    padding: 16px;
-    margin: 16px 0;
-}
-
-.aurora-editor-header {
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-}
-
-.aurora-editor-title {
-    font-weight: 600;
-    color: rgba(255, 255, 255, 0.95);
-}
-
-.aurora-editor-badge {
-    background: rgba(255, 255, 127, 0.15);
-    color: #FFFF7F;
-    padding: 4px 8px;
-    border-radius: 8px;
-    font-size: 0.7rem;
-    font-weight: 500;
-}
-
-.aurora-critique-card {
-    background: rgba(255, 255, 255, 0.02);
-    border-radius: 8px;
-    padding: 16px;
-    margin: 12px 0;
-}
-
-.aurora-critique-content {
-    color: rgba(255, 255, 255, 0.8);
-    line-height: 1.5;
-    font-size: 0.9rem;
-}
-
-@keyframes aurora-scan {
-    0%, 100% { left: -100%; }
-    50% { left: 100%; }
-}
-</style>
-"""
+    with col1:
+        st.download_button(
+            "\U0001f4c4 JSON Format",
+            data=formats["JSON"],
+            file_name="whisperforge_results.json",
+            mime="application/json",
+        )
 
-def show_enhanced_streaming_status():
-    """PHASE 3: ENHANCED STREAMING UX OVERHAUL - 2025 st.status() integration WITH VISIBLE THINKING"""
-    controller = get_pipeline_controller()
-    
-    if not controller.is_active and not controller.is_complete:
-        return
+    with col2:
+        st.download_button(
+            "\U0001f4dd Markdown Format",
+            data=formats["Markdown"],
+            file_name="whisperforge_results.md",
+            mime="text/markdown",
+        )
 
-    current_step = controller.current_step_index
-    pipeline_steps = [
-        ("Upload Validation", "File format & compatibility check", "upload_validation"),
-        ("Audio Transcription", "Speech-to-text conversion", "transcription"),
-        ("Wisdom Extraction", "Key insights extraction", "wisdom_extraction"),
-        ("Research Enrichment", "Supporting links & context", "research_enrichment"),  
-        ("Outline Generation", "Content structure creation", "outline_creation"),
-        ("Article Creation", "Full article generation", "article_creation"),
-        ("Social Media Posts", "Platform-optimized content", "social_content"),
-        ("Image Prompts", "Visual concept generation", "image_prompts"),
-        ("Database Storage", "Secure content storage", "database_storage")
-    ]
-    
-    results = controller.get_results()
-    errors = controller.get_errors() if hasattr(controller, 'get_errors') else {}
-    
-    # 🧠 VISIBLE THINKING INTEGRATION - Show AI thought bubbles during processing
-    if controller.is_active and st.session_state.get("thinking_enabled", True):
-        # Create dedicated container for thinking bubbles
-        thinking_container = st.container()
-        with thinking_container:
-            st.markdown("""
-            <div class="thinking-section">
-                <div class="thinking-header">
-                    <span class="thinking-icon">🧠</span>
-                    <span class="thinking-title">AI Thinking Process</span>
-                    <div class="thinking-pulse"></div>
-                </div>
-            </div>
-            
-            <style>
-            .thinking-section {
-                background: linear-gradient(135deg, rgba(255, 20, 147, 0.08), rgba(138, 43, 226, 0.05));
-                border: 1px solid rgba(255, 20, 147, 0.15);
-                border-radius: 12px;
-                padding: 16px;
-                margin: 16px 0;
-                position: relative;
-                overflow: hidden;
-            }
-            
-            .thinking-header {
-                display: flex;
-                align-items: center;
-                gap: 12px;
-            }
-            
-            .thinking-icon {
-                font-size: 1.2rem;
-                animation: think-pulse 2s ease-in-out infinite;
-            }
-            
-            .thinking-title {
-                color: rgba(255, 20, 147, 0.9);
-                font-weight: 500;
-                font-size: 0.9rem;
-                letter-spacing: 0.02em;
-            }
-            
-            .thinking-pulse {
-                width: 8px;
-                height: 8px;
-                background: #FF1493;
-                border-radius: 50%;
-                margin-left: auto;
-                animation: pulse-dot 1.5s ease-in-out infinite;
-            }
-            
-            @keyframes think-pulse {
-                0%, 100% { transform: scale(1); }
-                50% { transform: scale(1.1); }
-            }
-            
-            @keyframes pulse-dot {
-                0%, 100% { opacity: 1; transform: scale(1); }
-                50% { opacity: 0.4; transform: scale(0.8); }
-            }
-            </style>
-            """, unsafe_allow_html=True)
-            
-            # Render the actual thinking stream
-            try:
-                render_thinking_stream(thinking_container)
-            except Exception as e:
-                st.info(f"💭 AI is thinking... (thinking system loading)")
-    
-    # Main processing status container with st.status()
-    if controller.is_active:
-        current_title, current_desc, current_key = pipeline_steps[current_step]
-        
-        with st.status(f"🔄 {current_title}", expanded=True) as status:
-            st.write(f"📝 **{current_desc}**")
-            
-            # Progress bar
-            progress = (current_step / len(pipeline_steps)) * 100
-            st.progress(progress / 100, text=f"Progress: {progress:.0f}% ({current_step + 1}/{len(pipeline_steps)})")
-            
-            # Show previous completed steps with content preview
-            for i in range(current_step):
-                title, _, step_key = pipeline_steps[i]
-                if step_key in results:
-                    st.write(f"✅ {title} - Complete")
-                    # Show brief preview of generated content
-                    if step_key in results and results[step_key] and step_key not in ["upload_validation", "database_storage"]:
-                        preview = str(results[step_key])[:100] + "..." if len(str(results[step_key])) > 100 else str(results[step_key])
-                        st.caption(f"Preview: {preview}")
-                elif step_key in errors:
-                    st.write(f"❌ {title} - Error: {errors[step_key]}")
-                else:
-                    st.write(f"✅ {title} - Complete")
-            
-            # Current step with enhanced styling
-            st.markdown(f"""
-            <div style="
-                background: linear-gradient(135deg, rgba(0, 255, 255, 0.1), rgba(64, 224, 208, 0.05));
-                border-left: 3px solid #00FFFF;
-                padding: 12px 16px;
-                margin: 8px 0;
-                border-radius: 0 8px 8px 0;
-            ">
-                🔄 <strong>{current_title}</strong> - {current_desc}...
-            </div>
-            """, unsafe_allow_html=True)
-            
-            # Show preview of remaining steps
-            for i in range(current_step + 1, len(pipeline_steps)):
-                title, _, _ = pipeline_steps[i]
-                st.write(f"⭕ {title} - Pending")
-            
-            # Update status based on completion
-            if current_step >= len(pipeline_steps) - 1:
-                status.update(label="✅ Processing Complete!", state="complete", expanded=False)
-            else:
-                status.update(label=f"🔄 {current_title}", state="running")
-    
-    elif controller.is_complete:
-        # Completion status with beautiful summary
-        with st.status("✅ All processing complete!", state="complete", expanded=False):
-            st.success("Your audio has been transformed into comprehensive content!")
-        
-        # Enhanced completion summary
-        st.markdown("""
-        <div class="completion-summary">
-            <h4>🌟 Generation Summary</h4>
-        </div>
-        
-        <style>
-        .completion-summary {
-            background: linear-gradient(135deg, rgba(0, 255, 255, 0.08), rgba(64, 224, 208, 0.05));
-            border: 1px solid rgba(0, 255, 255, 0.15);
-            border-radius: 12px;
-            padding: 20px;
-            margin: 16px 0;
-            text-align: center;
-        }
-        
-        .completion-summary h4 {
-            margin: 0;
-            color: #00FFFF;
-            font-weight: 300;
-            letter-spacing: 0.02em;
-        }
-        </style>
-        """, unsafe_allow_html=True)
-        
-        col1, col2, col3 = st.columns(3)
-        
-        with col1:
-            completed_count = len([r for r in results.values() if r])
-            st.metric("Steps Completed", completed_count, len(pipeline_steps))
-        with col2:
-            error_count = len(errors)
-            st.metric("Errors", error_count, delta_color="inverse")
-        with col3:
-            success_rate = ((completed_count - error_count) / len(pipeline_steps)) * 100
-            st.metric("Success Rate", f"{success_rate:.1f}%")
-        
-        # Show content type breakdown
-        if results:
-            st.markdown("**Generated Content Types:**")
-            content_types = []
-            if results.get('transcription'): content_types.append("📝 Transcription")
-            if results.get('wisdom_extraction'): content_types.append("💎 Insights")
-            if results.get('research_enrichment'): content_types.append("🔍 Research")
-            if results.get('outline_creation'): content_types.append("📋 Outline")
-            if results.get('article_creation'): content_types.append("📰 Article")
-            if results.get('social_content'): content_types.append("📱 Social Posts")
-            if results.get('image_prompts'): content_types.append("🖼️ Image Prompts")
-            
-            if content_types:
-                st.write(" • ".join(content_types))
-
-def show_processing_status():
-    """Display ultra-modern Aurora pipeline with real-time visibility - WRAPPER"""
-    show_enhanced_streaming_status()  # Use the new enhanced version 
\ No newline at end of file
+    with col3:
+        st.download_button(
+            "\U0001f4c4 Text Format",
+            data=formats["Text"],
+            file_name="whisperforge_results.txt",
+            mime="text/plain",
+        )
diff --git a/core/streaming_status.py b/core/streaming_status.py
new file mode 100644
index 0000000..316e1a1
--- /dev/null
+++ b/core/streaming_status.py
@@ -0,0 +1,382 @@
+"""
+Streaming Status Display
+========================
+
+Enhanced streaming UX with st.status() integration and visible thinking.
+Includes the 2025 content display and pipeline status visualization.
+"""
+
+import streamlit as st
+
+from .exceptions import WhisperForgeError
+from .streaming_pipeline import get_pipeline_controller
+
+
+def show_2025_content_display():
+    """Ultra-modern 2025 Aurora content display for completed results."""
+    from .streaming_results import show_streaming_content_card
+
+    controller = get_pipeline_controller()
+    results = controller.get_results()
+
+    if not results:
+        return
+
+    st.markdown(
+        """
+    <div class="completion-showcase">
+        <div class="showcase-bg"></div>
+        <div class="showcase-content">
+            <h2>\u2728 Transformation Complete</h2>
+            <p>Your audio has been transformed into structured, actionable content</p>
+        </div>
+        <div class="showcase-orbs">
+            <div class="orb orb-1"></div>
+            <div class="orb orb-2"></div>
+            <div class="orb orb-3"></div>
+        </div>
+    </div>
+
+    <style>
+    .completion-showcase {
+        position: relative;
+        background: linear-gradient(135deg, rgba(0, 255, 255, 0.08), rgba(64, 224, 208, 0.12));
+        border: 1px solid rgba(0, 255, 255, 0.2);
+        border-radius: 20px;
+        padding: 48px;
+        margin: 32px 0;
+        text-align: center;
+        overflow: hidden;
+    }
+
+    .showcase-bg {
+        position: absolute;
+        top: 0;
+        left: 0;
+        right: 0;
+        bottom: 0;
+        background: radial-gradient(circle at 30% 70%, rgba(0, 255, 255, 0.1) 0%, transparent 50%),
+                    radial-gradient(circle at 70% 30%, rgba(64, 224, 208, 0.08) 0%, transparent 50%);
+        animation: bg-float 8s ease-in-out infinite;
+    }
+
+    .showcase-content {
+        position: relative;
+        z-index: 2;
+    }
+
+    .showcase-content h2 {
+        margin: 0 0 12px 0;
+        color: #00FFFF;
+        font-size: 2.2rem;
+        font-weight: 300;
+        letter-spacing: 0.05em;
+        text-shadow: 0 0 20px rgba(0, 255, 255, 0.3);
+    }
+
+    .showcase-content p {
+        margin: 0;
+        color: rgba(255, 255, 255, 0.7);
+        font-size: 1.1rem;
+        letter-spacing: 0.02em;
+    }
+
+    .showcase-orbs {
+        position: absolute;
+        top: 50%;
+        left: 50%;
+        transform: translate(-50%, -50%);
+        width: 200px;
+        height: 200px;
+        pointer-events: none;
+    }
+
+    .orb {
+        position: absolute;
+        border-radius: 50%;
+        filter: blur(1px);
+    }
+
+    .orb-1 {
+        width: 60px;
+        height: 60px;
+        background: radial-gradient(circle, rgba(0, 255, 255, 0.2), transparent);
+        top: 20%;
+        left: 20%;
+        animation: orb-float-1 6s ease-in-out infinite;
+    }
+
+    .orb-2 {
+        width: 40px;
+        height: 40px;
+        background: radial-gradient(circle, rgba(64, 224, 208, 0.15), transparent);
+        top: 60%;
+        right: 25%;
+        animation: orb-float-2 8s ease-in-out infinite reverse;
+    }
+
+    .orb-3 {
+        width: 30px;
+        height: 30px;
+        background: radial-gradient(circle, rgba(0, 255, 200, 0.1), transparent);
+        bottom: 30%;
+        left: 30%;
+        animation: orb-float-3 7s ease-in-out infinite;
+    }
+
+    @keyframes bg-float {
+        0%, 100% { transform: translateY(0px) rotate(0deg); }
+        50% { transform: translateY(-10px) rotate(2deg); }
+    }
+
+    @keyframes orb-float-1 {
+        0%, 100% { transform: translate(0, 0) scale(1); }
+        33% { transform: translate(10px, -15px) scale(1.1); }
+        66% { transform: translate(-8px, 12px) scale(0.9); }
+    }
+
+    @keyframes orb-float-2 {
+        0%, 100% { transform: translate(0, 0) scale(1); }
+        50% { transform: translate(-12px, -10px) scale(1.2); }
+    }
+
+    @keyframes orb-float-3 {
+        0%, 100% { transform: translate(0, 0) scale(1); }
+        25% { transform: translate(8px, -8px) scale(0.8); }
+        75% { transform: translate(-6px, 10px) scale(1.1); }
+    }
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
+
+    content_map = {
+        "transcription": ("\U0001f399\ufe0f", "Audio Transcription", "Complete speech-to-text conversion"),
+        "wisdom_extraction": ("\U0001f48e", "Key Insights & Wisdom", "Extracted insights and actionable takeaways"),
+        "research_enrichment": ("\U0001f50d", "Research Enrichment", "Supporting links and contextual information"),
+        "outline_creation": ("\U0001f4cb", "Content Outline", "Structured organization and flow"),
+        "article_creation": ("\U0001f4f0", "Full Article", "Complete written content ready for publication"),
+        "social_content": ("\U0001f4f1", "Social Media Content", "Platform-optimized posts and captions"),
+        "image_prompts": ("\U0001f5bc\ufe0f", "Image Generation Prompts", "AI-generated visual concept descriptions"),
+    }
+
+    for key, (icon, title, desc) in content_map.items():
+        if key in results and results[key]:
+            show_streaming_content_card(icon, title, desc, results[key], is_live=False)
+
+
+def show_enhanced_streaming_status():
+    """Enhanced streaming UX with st.status() integration and visible thinking."""
+    controller = get_pipeline_controller()
+
+    if not controller.is_active and not controller.is_complete:
+        return
+
+    current_step = controller.current_step_index
+    pipeline_steps = [
+        ("Upload Validation", "File format & compatibility check", "upload_validation"),
+        ("Audio Transcription", "Speech-to-text conversion", "transcription"),
+        ("Wisdom Extraction", "Key insights extraction", "wisdom_extraction"),
+        ("Research Enrichment", "Supporting links & context", "research_enrichment"),
+        ("Outline Generation", "Content structure creation", "outline_creation"),
+        ("Article Creation", "Full article generation", "article_creation"),
+        ("Social Media Posts", "Platform-optimized content", "social_content"),
+        ("Image Prompts", "Visual concept generation", "image_prompts"),
+        ("Database Storage", "Secure content storage", "database_storage"),
+    ]
+
+    results = controller.get_results()
+    errors = controller.get_errors() if hasattr(controller, "get_errors") else {}
+
+    # Visible thinking integration
+    if controller.is_active and st.session_state.get("thinking_enabled", True):
+        thinking_container = st.container()
+        with thinking_container:
+            st.markdown(
+                """
+            <div class="thinking-section">
+                <div class="thinking-header">
+                    <span class="thinking-icon">\U0001f9e0</span>
+                    <span class="thinking-title">AI Thinking Process</span>
+                    <div class="thinking-pulse"></div>
+                </div>
+            </div>
+
+            <style>
+            .thinking-section {
+                background: linear-gradient(135deg, rgba(255, 20, 147, 0.08), rgba(138, 43, 226, 0.05));
+                border: 1px solid rgba(255, 20, 147, 0.15);
+                border-radius: 12px;
+                padding: 16px;
+                margin: 16px 0;
+                position: relative;
+                overflow: hidden;
+            }
+
+            .thinking-header {
+                display: flex;
+                align-items: center;
+                gap: 12px;
+            }
+
+            .thinking-icon {
+                font-size: 1.2rem;
+                animation: think-pulse 2s ease-in-out infinite;
+            }
+
+            .thinking-title {
+                color: rgba(255, 20, 147, 0.9);
+                font-weight: 500;
+                font-size: 0.9rem;
+                letter-spacing: 0.02em;
+            }
+
+            .thinking-pulse {
+                width: 8px;
+                height: 8px;
+                background: #FF1493;
+                border-radius: 50%;
+                margin-left: auto;
+                animation: pulse-dot 1.5s ease-in-out infinite;
+            }
+
+            @keyframes think-pulse {
+                0%, 100% { transform: scale(1); }
+                50% { transform: scale(1.1); }
+            }
+
+            @keyframes pulse-dot {
+                0%, 100% { opacity: 1; transform: scale(1); }
+                50% { opacity: 0.4; transform: scale(0.8); }
+            }
+            </style>
+            """,
+                unsafe_allow_html=True,
+            )
+
+            try:
+                from .visible_thinking import render_thinking_stream
+
+                render_thinking_stream(thinking_container)
+            except (WhisperForgeError, ImportError):
+                st.info("\U0001f4ad AI is thinking... (thinking system loading)")
+
+    # Main processing status
+    if controller.is_active:
+        current_title, current_desc, _current_key = pipeline_steps[current_step]
+
+        with st.status(f"\U0001f504 {current_title}", expanded=True) as status:
+            st.write(f"\U0001f4dd **{current_desc}**")
+
+            progress = (current_step / len(pipeline_steps)) * 100
+            st.progress(progress / 100, text=f"Progress: {progress:.0f}% ({current_step + 1}/{len(pipeline_steps)})")
+
+            for i in range(current_step):
+                title, _, step_key = pipeline_steps[i]
+                if step_key in results:
+                    st.write(f"\u2705 {title} - Complete")
+                    if (
+                        step_key in results
+                        and results[step_key]
+                        and step_key not in ["upload_validation", "database_storage"]
+                    ):
+                        result_str = str(results[step_key])
+                        preview = result_str[:100] + "..." if len(result_str) > 100 else result_str
+                        st.caption(f"Preview: {preview}")
+                elif step_key in errors:
+                    st.write(f"\u274c {title} - Error: {errors[step_key]}")
+                else:
+                    st.write(f"\u2705 {title} - Complete")
+
+            st.markdown(
+                f"""
+            <div style="
+                background: linear-gradient(135deg, rgba(0, 255, 255, 0.1), rgba(64, 224, 208, 0.05));
+                border-left: 3px solid #00FFFF;
+                padding: 12px 16px;
+                margin: 8px 0;
+                border-radius: 0 8px 8px 0;
+            ">
+                \U0001f504 <strong>{current_title}</strong> - {current_desc}...
+            </div>
+            """,
+                unsafe_allow_html=True,
+            )
+
+            for i in range(current_step + 1, len(pipeline_steps)):
+                title, _, _ = pipeline_steps[i]
+                st.write(f"\u2b55 {title} - Pending")
+
+            if current_step >= len(pipeline_steps) - 1:
+                status.update(label="\u2705 Processing Complete!", state="complete", expanded=False)
+            else:
+                status.update(label=f"\U0001f504 {current_title}", state="running")
+
+    elif controller.is_complete:
+        with st.status("\u2705 All processing complete!", state="complete", expanded=False):
+            st.success("Your audio has been transformed into comprehensive content!")
+
+        st.markdown(
+            """
+        <div class="completion-summary">
+            <h4>\U0001f31f Generation Summary</h4>
+        </div>
+
+        <style>
+        .completion-summary {
+            background: linear-gradient(135deg, rgba(0, 255, 255, 0.08), rgba(64, 224, 208, 0.05));
+            border: 1px solid rgba(0, 255, 255, 0.15);
+            border-radius: 12px;
+            padding: 20px;
+            margin: 16px 0;
+            text-align: center;
+        }
+
+        .completion-summary h4 {
+            margin: 0;
+            color: #00FFFF;
+            font-weight: 300;
+            letter-spacing: 0.02em;
+        }
+        </style>
+        """,
+            unsafe_allow_html=True,
+        )
+
+        col1, col2, col3 = st.columns(3)
+
+        with col1:
+            completed_count = len([r for r in results.values() if r])
+            st.metric("Steps Completed", completed_count, len(pipeline_steps))
+        with col2:
+            error_count = len(errors)
+            st.metric("Errors", error_count, delta_color="inverse")
+        with col3:
+            success_rate = ((completed_count - error_count) / len(pipeline_steps)) * 100
+            st.metric("Success Rate", f"{success_rate:.1f}%")
+
+        if results:
+            st.markdown("**Generated Content Types:**")
+            content_types = []
+            if results.get("transcription"):
+                content_types.append("\U0001f4dd Transcription")
+            if results.get("wisdom_extraction"):
+                content_types.append("\U0001f48e Insights")
+            if results.get("research_enrichment"):
+                content_types.append("\U0001f50d Research")
+            if results.get("outline_creation"):
+                content_types.append("\U0001f4cb Outline")
+            if results.get("article_creation"):
+                content_types.append("\U0001f4f0 Article")
+            if results.get("social_content"):
+                content_types.append("\U0001f4f1 Social Posts")
+            if results.get("image_prompts"):
+                content_types.append("\U0001f5bc\ufe0f Image Prompts")
+
+            if content_types:
+                st.write(" \u2022 ".join(content_types))
+
+
+def show_processing_status():
+    """Display Aurora pipeline with real-time visibility - wrapper."""
+    show_enhanced_streaming_status()
diff --git a/core/streamlit_monitoring.py b/core/streamlit_monitoring.py
index 9500aeb..8d2f4ed 100644
--- a/core/streamlit_monitoring.py
+++ b/core/streamlit_monitoring.py
@@ -2,8 +2,8 @@
 
 from __future__ import annotations
 
+from collections.abc import Callable
 from functools import wraps
-from typing import Callable
 
 from .monitoring import structured_logger
 
@@ -43,4 +43,3 @@ def wrapper(*args, **kwargs):
         return wrapper
 
     return decorator
-
diff --git a/core/styling.py b/core/styling.py
index 91b1c4e..ab970ba 100644
--- a/core/styling.py
+++ b/core/styling.py
@@ -5,24 +5,29 @@
 
 import streamlit as st
 
+
 def apply_aurora_theme():
     """Apply the complete Aurora theme by loading our comprehensive CSS file"""
     # Load the comprehensive Aurora CSS file
     css_file_path = "static/css/main.css"
-    
+
     try:
-        with open(css_file_path, 'r', encoding='utf-8') as f:
+        with open(css_file_path, encoding="utf-8") as f:
             css_content = f.read()
-        
-        st.markdown(f"""
+
+        st.markdown(
+            f"""
         <style>
         {css_content}
         </style>
-        """, unsafe_allow_html=True)
-        
+        """,
+            unsafe_allow_html=True,
+        )
+
     except FileNotFoundError:
         # Fallback to basic Aurora styling if file not found
-        st.markdown("""
+        st.markdown(
+            """
         <style>
         :root {
             --aurora-primary: #40E0D0;
@@ -35,19 +40,23 @@ def apply_aurora_theme():
             --aurora-text: rgba(255, 255, 255, 0.95);
             --aurora-glow: 0 0 20px rgba(64, 224, 208, 0.3);
         }
-        
+
         .stApp {
             background: linear-gradient(135deg, var(--aurora-bg-dark) 0%, var(--aurora-bg-darker) 100%);
             color: var(--aurora-text);
         }
         </style>
-        """, unsafe_allow_html=True)
+        """,
+            unsafe_allow_html=True,
+        )
+
 
 def create_aurora_header():
     """Create a flagship Aurora header with integrated navigation and logout - REBUILT FOR 2025"""
-    
+
     # First, inject the CSS using st.markdown()
-    st.markdown("""
+    st.markdown(
+        """
     <style>
     .aurora-header {
         background: linear-gradient(135deg, rgba(0, 20, 40, 0.95), rgba(0, 40, 60, 0.98));
@@ -58,7 +67,7 @@ def create_aurora_header():
         overflow: hidden;
         backdrop-filter: blur(10px);
     }
-    
+
     .aurora-header::before {
         content: '';
         position: absolute;
@@ -69,7 +78,7 @@ def create_aurora_header():
         background: linear-gradient(90deg, transparent, #00FFFF, #40E0D0, transparent);
         animation: aurora-flow 8s ease-in-out infinite;
     }
-    
+
     .aurora-header-content {
         display: flex;
         justify-content: space-between;
@@ -78,13 +87,13 @@ def create_aurora_header():
         position: relative;
         z-index: 1;
     }
-    
+
     .aurora-brand {
         display: flex;
         align-items: center;
         gap: 12px;
     }
-    
+
     .aurora-title {
         font-size: 1.6rem;
         font-weight: 800;
@@ -96,7 +105,7 @@ def create_aurora_header():
         text-shadow: 0 0 20px rgba(0, 255, 255, 0.4);
         letter-spacing: -0.02em;
     }
-    
+
     .aurora-status {
         display: flex;
         align-items: center;
@@ -107,7 +116,7 @@ def create_aurora_header():
         border-radius: 20px;
         backdrop-filter: blur(5px);
     }
-    
+
     .aurora-status-dot {
         width: 6px;
         height: 6px;
@@ -116,7 +125,7 @@ def create_aurora_header():
         animation: status-pulse 2s ease-in-out infinite;
         box-shadow: 0 0 8px rgba(0, 255, 136, 0.6);
     }
-    
+
     .aurora-status-text {
         color: rgba(255, 255, 255, 0.8);
         font-size: 0.75rem;
@@ -128,39 +137,42 @@ def create_aurora_header():
         align-items: center;
         gap: 8px;
     }
-    
+
     @keyframes aurora-flow {
-        0%, 100% { 
-            left: -100%; 
-            opacity: 0; 
+        0%, 100% {
+            left: -100%;
+            opacity: 0;
         }
-        25% { 
-            opacity: 1; 
+        25% {
+            opacity: 1;
         }
-        75% { 
-            opacity: 1; 
+        75% {
+            opacity: 1;
         }
-        100% { 
-            left: 100%; 
-            opacity: 0; 
+        100% {
+            left: 100%;
+            opacity: 0;
         }
     }
-    
+
     @keyframes status-pulse {
-        0%, 100% { 
-            opacity: 1; 
-            transform: scale(1); 
+        0%, 100% {
+            opacity: 1;
+            transform: scale(1);
         }
-        50% { 
-            opacity: 0.6; 
-            transform: scale(1.3); 
+        50% {
+            opacity: 0.6;
+            transform: scale(1.3);
         }
     }
     </style>
-    """, unsafe_allow_html=True)
-    
+    """,
+        unsafe_allow_html=True,
+    )
+
     # Then render the HTML structure using st.markdown()
-    st.markdown("""
+    st.markdown(
+        """
     <header class="aurora-header">
         <div class="aurora-header-content">
             <div class="aurora-brand">
@@ -175,19 +187,23 @@ def create_aurora_header():
             </div>
         </div>
     </header>
-    """, unsafe_allow_html=True)
+    """,
+        unsafe_allow_html=True,
+    )
+
 
 def create_aurora_nav_buttons():
     """Create integrated navigation buttons for the Aurora header"""
-    
+
     # Enhanced styling for integrated nav buttons
-    st.markdown("""
+    st.markdown(
+        """
     <style>
     /* Aurora nav button styling */
     .aurora-nav-button {
         margin: 0 2px !important;
     }
-    
+
     .aurora-nav-button .stButton > button {
         background: rgba(0, 255, 255, 0.08) !important;
         border: 1px solid rgba(0, 255, 255, 0.15) !important;
@@ -200,7 +216,7 @@ def create_aurora_nav_buttons():
         min-width: 80px !important;
         height: 32px !important;
         }
-        
+
     .aurora-nav-button .stButton > button:hover {
         background: rgba(0, 255, 255, 0.15) !important;
         border-color: rgba(0, 255, 255, 0.25) !important;
@@ -208,7 +224,7 @@ def create_aurora_nav_buttons():
         transform: translateY(-1px);
         box-shadow: 0 4px 12px rgba(0, 255, 255, 0.1);
     }
-    
+
     /* Active/Primary button styling */
     .aurora-nav-button .stButton > button[kind="primary"] {
         background: rgba(0, 255, 255, 0.2) !important;
@@ -216,60 +232,64 @@ def create_aurora_nav_buttons():
         color: #00FFFF !important;
         box-shadow: 0 0 15px rgba(0, 255, 255, 0.15);
     }
-    
+
     /* Logout button special styling */
     .aurora-logout-button .stButton > button {
         background: rgba(255, 100, 100, 0.1) !important;
         border: 1px solid rgba(255, 100, 100, 0.2) !important;
         color: rgba(255, 180, 180, 0.9) !important;
     }
-    
+
     .aurora-logout-button .stButton > button:hover {
         background: rgba(255, 100, 100, 0.2) !important;
         border-color: rgba(255, 100, 100, 0.3) !important;
         color: #FF6B6B !important;
     }
     </style>
-    """, unsafe_allow_html=True)
+    """,
+        unsafe_allow_html=True,
+    )
 
     pages = [
         ("Processing", "Content Pipeline"),
-        ("History", "Content History"), 
+        ("History", "Content History"),
         ("Settings", "Settings"),
-        ("Status", "Health Check")
+        ("Status", "Health Check"),
     ]
-    
-    current_page = st.session_state.get('current_page', 'Content Pipeline')
-    
+
+    current_page = st.session_state.get("current_page", "Content Pipeline")
+
     # Create horizontal layout for nav buttons
     nav_cols = st.columns([1, 1, 1, 1, 0.8])  # Last column smaller for logout
-    
+
     for i, (page_name, page_key) in enumerate(pages):
         with nav_cols[i]:
             st.markdown('<div class="aurora-nav-button">', unsafe_allow_html=True)
             if st.button(
-                page_name, 
-                key=f"nav_{page_name}", 
+                page_name,
+                key=f"nav_{page_name}",
                 type="primary" if page_key == current_page else "secondary",
-                use_container_width=True
+                use_container_width=True,
             ):
                 st.session_state.current_page = page_key
                 st.rerun()
-            st.markdown('</div>', unsafe_allow_html=True)
-    
+            st.markdown("</div>", unsafe_allow_html=True)
+
     # Logout button in the last column
     with nav_cols[4]:
         st.markdown('<div class="aurora-logout-button">', unsafe_allow_html=True)
         if st.button("Sign Out", key="logout_btn", use_container_width=True):
             return True  # Signal logout
-        st.markdown('</div>', unsafe_allow_html=True)
-    
+        st.markdown("</div>", unsafe_allow_html=True)
+
     return False  # No logout
 
+
 def create_aurora_progress_card(title, current_step, total_steps, description=""):
     """Create a beautiful Aurora progress card"""
     progress = (current_step / total_steps) * 100
-    st.markdown(f"""
+    st.markdown(
+        f"""
     <div class="aurora-progress-container aurora-fade-in">
         <div class="aurora-progress-header">
             <h3 class="aurora-progress-title">{title}</h3>
@@ -277,18 +297,21 @@ def create_aurora_progress_card(title, current_step, total_steps, description=""
                 <span>{current_step}/{total_steps} steps • {progress:.0f}%</span>
             </div>
         </div>
-        
+
         <div class="aurora-progress-bar">
             <div class="aurora-progress-fill" style="width: {progress}%"></div>
         </div>
-        
-        {f'<p style="color: var(--aurora-text-muted); margin: 8px 0 0 0;">{description}</p>' if description else ''}
+
+        {f'<p style="color: var(--aurora-text-muted); margin: 8px 0 0 0;">{description}</p>' if description else ""}
     </div>
-    """, unsafe_allow_html=True)
+    """,
+        unsafe_allow_html=True,
+    )
+
 
 def create_aurora_step_card(title, description, status="pending", progress=0):
     """Create a beautiful step card with Aurora styling"""
-    
+
     # Determine icon and styling based on status
     if status == "completed":
         icon = "✅"
@@ -299,7 +322,7 @@ def create_aurora_step_card(title, description, status="pending", progress=0):
     else:
         icon = "⭕"
         container_class = "aurora-step-container"
-    
+
     progress_bar = ""
     if status == "processing" and progress > 0:
         progress_bar = f"""
@@ -325,8 +348,9 @@ def create_aurora_step_card(title, description, status="pending", progress=0):
             </div>
         </div>
         """
-    
-    st.markdown(f"""
+
+    st.markdown(
+        f"""
     <div class="{container_class} aurora-fade-in">
         <div class="aurora-step-header">
             <div class="aurora-step-info">
@@ -336,15 +360,18 @@ def create_aurora_step_card(title, description, status="pending", progress=0):
                     <p class="aurora-step-description">{description}</p>
                 </div>
             </div>
-            <div class="aurora-step-progress">{progress if status == 'processing' else (100 if status == 'completed' else 0)}%</div>
+            <div class="aurora-step-progress">{progress if status == "processing" else (100 if status == "completed" else 0)}%</div>
         </div>
         {progress_bar}
     </div>
-    """, unsafe_allow_html=True)
+    """,
+        unsafe_allow_html=True,
+    )
+
 
 def create_aurora_content_card(title, content, content_type="text"):
     """Create a beautiful content display card"""
-    
+
     # Truncate content if too long
     if len(content) > 500:
         preview_content = content[:500] + "..."
@@ -352,8 +379,9 @@ def create_aurora_content_card(title, content, content_type="text"):
     else:
         preview_content = content
         show_full = False
-    
-    st.markdown(f"""
+
+    st.markdown(
+        f"""
     <div class="aurora-card aurora-fade-in">
         <div class="aurora-card-header">
             <h4 class="aurora-card-title">{title}</h4>
@@ -364,20 +392,26 @@ def create_aurora_content_card(title, content, content_type="text"):
             white-space: pre-wrap;
         ">{preview_content}</div>
     </div>
-    """, unsafe_allow_html=True)
+    """,
+        unsafe_allow_html=True,
+    )
 
     if show_full:
         with st.expander("Show full content"):
             st.markdown(content)
 
+
 # Aurora Component Utilities
+
+
 class AuroraComponents:
     """Beautiful Aurora-themed UI components"""
-    
+
     @staticmethod
     def success_message(message):
         """Aurora success message"""
-        st.markdown(f"""
+        st.markdown(
+            f"""
         <div style="
             background: rgba(0, 255, 136, 0.1);
             border: 1px solid rgba(0, 255, 136, 0.3);
@@ -388,12 +422,15 @@ def success_message(message):
         ">
             ✅ {message}
         </div>
-        """, unsafe_allow_html=True)
-    
+        """,
+            unsafe_allow_html=True,
+        )
+
     @staticmethod
     def warning_message(message):
         """Aurora warning message"""
-        st.markdown(f"""
+        st.markdown(
+            f"""
         <div style="
             background: rgba(255, 184, 0, 0.1);
             border: 1px solid rgba(255, 184, 0, 0.3);
@@ -404,12 +441,15 @@ def warning_message(message):
         ">
             ⚠️ {message}
         </div>
-        """, unsafe_allow_html=True)
-    
+        """,
+            unsafe_allow_html=True,
+        )
+
     @staticmethod
     def error_message(message):
         """Aurora error message"""
-        st.markdown(f"""
+        st.markdown(
+            f"""
         <div style="
             background: rgba(255, 68, 68, 0.1);
             border: 1px solid rgba(255, 68, 68, 0.3);
@@ -420,4 +460,6 @@ def error_message(message):
         ">
             ❌ {message}
         </div>
-    """, unsafe_allow_html=True) 
\ No newline at end of file
+    """,
+            unsafe_allow_html=True,
+        )
diff --git a/core/supabase_integration.py b/core/supabase_integration.py
index 812d53f..514942e 100644
--- a/core/supabase_integration.py
+++ b/core/supabase_integration.py
@@ -6,18 +6,23 @@
 Designed to work with MCP (Model Context Protocol) for enhanced AI integration.
 """
 
-import os
 import logging
-from typing import Dict, List, Optional, Any
+import os
 from datetime import datetime, timedelta
-from supabase import create_client, Client
+from typing import Any
+
 from dotenv import load_dotenv
+from postgrest.exceptions import APIError
+from supabase import Client, create_client
+
+from .constants import DEFAULT_ANALYTICS_DAYS, DEFAULT_CONTENT_QUERY_LIMIT, DEFAULT_USAGE_QUOTA_MINUTES
+from .exceptions import DatabaseError
+from .utils import hash_password
 
 # Load environment variables
 load_dotenv()
 
 # Import hash_password function from utils instead of app
-from .utils import hash_password
 
 logger = logging.getLogger(__name__)
 
@@ -26,97 +31,112 @@ class SupabaseClient:
     """
     Supabase client wrapper with MCP integration capabilities
     """
-    
+
     def __init__(self):
         self.url = os.getenv("SUPABASE_URL")
         # Handle both SUPABASE_KEY and SUPABASE_ANON_KEY for backward compatibility
         self.key = os.getenv("SUPABASE_ANON_KEY") or os.getenv("SUPABASE_KEY")
         self.service_role_key = os.getenv("SUPABASE_SERVICE_ROLE_KEY")
-        
+
         if not self.url or not self.key:
-            raise ValueError("SUPABASE_URL and SUPABASE_ANON_KEY (or SUPABASE_KEY) must be set in environment variables")
-        
+            raise ValueError(
+                "SUPABASE_URL and SUPABASE_ANON_KEY (or SUPABASE_KEY) must be set in environment variables"
+            )
+
         self.client: Client = create_client(self.url, self.key)
-        self.admin_client: Optional[Client] = None
-        
+        self.admin_client: Client | None = None
+
         if self.service_role_key:
             self.admin_client = create_client(self.url, self.service_role_key)
-        
+
         logger.info("Supabase client initialized successfully")
-    
+
     def test_connection(self) -> bool:
         """Test the Supabase connection"""
         try:
             # Try a simple query to test connectivity
-            result = self.client.table("users").select("id").limit(1).execute()
+            self.client.table("users").select("id").limit(1).execute()
             logger.info("Supabase connection test successful")
             return True
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Supabase connection test failed: {e}")
             return False
-    
+        except DatabaseError as e:
+            logger.error(f"Unexpected error in Supabase connection test: {e}")
+            return False
+
     # User Management
-    def create_user(self, email: str, password: str, metadata: Dict[str, Any] = None) -> Dict[str, Any]:
+    def create_user(self, email: str, password: str, metadata: dict[str, Any] = None) -> dict[str, Any]:
         """Create a new user"""
         try:
             # Hash the password before storing
             hashed_password = hash_password(password)
-            
+
             user_data = {
                 "email": email,
                 "password": hashed_password,  # Store hashed password
                 "created_at": datetime.now().isoformat(),
-                "usage_quota": 60,  # Default 60 minutes per month
+                "usage_quota": DEFAULT_USAGE_QUOTA_MINUTES,
                 "usage_current": 0,
                 "is_admin": False,
-                "subscription_tier": "free"
+                "subscription_tier": "free",
             }
-            
+
             if metadata:
                 user_data.update(metadata)
-            
+
             result = self.client.table("users").insert(user_data).execute()
             logger.info(f"User created successfully: {email}")
             return result.data[0] if result.data else {}
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error creating user: {e}")
             raise
-    
-    def get_user(self, user_id: int) -> Optional[Dict[str, Any]]:
+        except DatabaseError as e:
+            logger.error(f"Unexpected error creating user: {e}")
+            raise
+
+    def get_user(self, user_id: int) -> dict[str, Any] | None:
         """Get user by ID"""
         try:
             result = self.client.table("users").select("*").eq("id", user_id).execute()
             return result.data[0] if result.data else None
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error fetching user: {e}")
             return None
-    
-    def get_user_by_email(self, email: str) -> Optional[Dict[str, Any]]:
+        except DatabaseError as e:
+            logger.error(f"Unexpected error fetching user: {e}")
+            return None
+
+    def get_user_by_email(self, email: str) -> dict[str, Any] | None:
         """Get user by email"""
         try:
             result = self.client.table("users").select("*").eq("email", email).execute()
             return result.data[0] if result.data else None
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error fetching user by email: {e}")
             return None
-    
+        except DatabaseError as e:
+            logger.error(f"Unexpected error fetching user by email: {e}")
+            return None
+
     def update_user_usage(self, user_id: int, usage_seconds: int) -> bool:
         """Update user's current usage"""
         try:
             # Convert seconds to minutes
             usage_minutes = usage_seconds / 60
-            
-            result = self.client.table("users").update({
-                "usage_current": usage_minutes
-            }).eq("id", user_id).execute()
-            
+
+            result = self.client.table("users").update({"usage_current": usage_minutes}).eq("id", user_id).execute()
+
             return bool(result.data)
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error updating user usage: {e}")
             return False
-    
+        except DatabaseError as e:
+            logger.error(f"Unexpected error updating user usage: {e}")
+            return False
+
     # Content Storage
-    def save_content(self, user_id: int, content_data: Dict[str, Any]) -> Optional[str]:
+    def save_content(self, user_id: int, content_data: dict[str, Any]) -> str | None:
         """Save generated content to database"""
         try:
             content_record = {
@@ -130,55 +150,76 @@ def save_content(self, user_id: int, content_data: Dict[str, Any]) -> Optional[s
                 "article": content_data.get("article", ""),
                 "metadata": content_data.get("metadata", {}),
                 "created_at": datetime.now().isoformat(),
-                "updated_at": datetime.now().isoformat()
+                "updated_at": datetime.now().isoformat(),
             }
-            
+
             result = self.client.table("content").insert(content_record).execute()
-            
+
             if result.data:
                 content_id = result.data[0]["id"]
                 logger.info(f"Content saved successfully with ID: {content_id}")
                 return content_id
             return None
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error saving content: {e}")
             return None
-    
-    def get_user_content(self, user_id: int, limit: int = 50) -> List[Dict[str, Any]]:
+        except DatabaseError as e:
+            logger.error(f"Unexpected error saving content: {e}")
+            return None
+
+    def get_user_content(self, user_id: int, limit: int = DEFAULT_CONTENT_QUERY_LIMIT) -> list[dict[str, Any]]:
         """Get user's content history"""
         try:
-            result = self.client.table("content").select("*").eq("user_id", user_id).order("created_at", desc=True).limit(limit).execute()
+            result = (
+                self.client.table("content")
+                .select("*")
+                .eq("user_id", user_id)
+                .order("created_at", desc=True)
+                .limit(limit)
+                .execute()
+            )
             return result.data or []
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error fetching user content: {e}")
             return []
-    
+        except DatabaseError as e:
+            logger.error(f"Unexpected error fetching user content: {e}")
+            return []
+
     # API Key Management
-    def save_user_api_keys(self, user_id: int, api_keys: Dict[str, str]) -> bool:
+    def save_user_api_keys(self, user_id: int, api_keys: dict[str, str]) -> bool:
         """Save encrypted API keys for user"""
         try:
             # In production, encrypt the API keys before storing
-            result = self.client.table("users").update({
-                "api_keys": api_keys,
-                "updated_at": datetime.now().isoformat()
-            }).eq("id", user_id).execute()
-            
+            result = (
+                self.client.table("users")
+                .update({"api_keys": api_keys, "updated_at": datetime.now().isoformat()})
+                .eq("id", user_id)
+                .execute()
+            )
+
             return bool(result.data)
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error saving API keys: {e}")
             return False
-    
-    def get_user_api_keys(self, user_id: int) -> Dict[str, str]:
+        except DatabaseError as e:
+            logger.error(f"Unexpected error saving API keys: {e}")
+            return False
+
+    def get_user_api_keys(self, user_id: int) -> dict[str, str]:
         """Get user's API keys"""
         try:
             result = self.client.table("users").select("api_keys").eq("id", user_id).execute()
             if result.data and result.data[0]["api_keys"]:
                 return result.data[0]["api_keys"]
             return {}
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error fetching API keys: {e}")
             return {}
-    
+        except DatabaseError as e:
+            logger.error(f"Unexpected error fetching API keys: {e}")
+            return {}
+
     # Knowledge Base Management
     def save_knowledge_base_file(self, user_id: int, filename: str, content: str) -> bool:
         """Save knowledge base file for user"""
@@ -188,43 +229,58 @@ def save_knowledge_base_file(self, user_id: int, filename: str, content: str) ->
                 "filename": filename,
                 "content": content,
                 "created_at": datetime.now().isoformat(),
-                "updated_at": datetime.now().isoformat()
+                "updated_at": datetime.now().isoformat(),
             }
-            
+
             # Check if file already exists for this user
-            existing = self.client.table("knowledge_base").select("id").eq("user_id", user_id).eq("filename", filename).execute()
-            
+            existing = (
+                self.client.table("knowledge_base")
+                .select("id")
+                .eq("user_id", user_id)
+                .eq("filename", filename)
+                .execute()
+            )
+
             if existing.data:
                 # Update existing
-                result = self.client.table("knowledge_base").update({
-                    "content": content,
-                    "updated_at": datetime.now().isoformat()
-                }).eq("user_id", user_id).eq("filename", filename).execute()
+                result = (
+                    self.client.table("knowledge_base")
+                    .update({"content": content, "updated_at": datetime.now().isoformat()})
+                    .eq("user_id", user_id)
+                    .eq("filename", filename)
+                    .execute()
+                )
             else:
                 # Create new
                 result = self.client.table("knowledge_base").insert(kb_record).execute()
-            
+
             return bool(result.data)
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error saving knowledge base file: {e}")
             return False
-    
-    def get_user_knowledge_base(self, user_id: int) -> Dict[str, str]:
+        except DatabaseError as e:
+            logger.error(f"Unexpected error saving knowledge base file: {e}")
+            return False
+
+    def get_user_knowledge_base(self, user_id: int) -> dict[str, str]:
         """Get user's knowledge base files"""
         try:
             result = self.client.table("knowledge_base").select("filename, content").eq("user_id", user_id).execute()
-            
+
             kb_dict = {}
             for item in result.data or []:
                 # Convert filename to display name
-                name = item["filename"].replace('.txt', '').replace('.md', '').replace('_', ' ').title()
+                name = item["filename"].replace(".txt", "").replace(".md", "").replace("_", " ").title()
                 kb_dict[name] = item["content"]
-            
+
             return kb_dict
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error fetching knowledge base: {e}")
             return {}
-    
+        except DatabaseError as e:
+            logger.error(f"Unexpected error fetching knowledge base: {e}")
+            return {}
+
     # Custom Prompts Management
     def save_custom_prompt(self, user_id: int, prompt_type: str, content: str) -> bool:
         """Save custom prompt for user"""
@@ -234,43 +290,58 @@ def save_custom_prompt(self, user_id: int, prompt_type: str, content: str) -> bo
                 "prompt_type": prompt_type,
                 "content": content,
                 "created_at": datetime.now().isoformat(),
-                "updated_at": datetime.now().isoformat()
+                "updated_at": datetime.now().isoformat(),
             }
-            
+
             # Check if prompt already exists for this user
-            existing = self.client.table("custom_prompts").select("id").eq("user_id", user_id).eq("prompt_type", prompt_type).execute()
-            
+            existing = (
+                self.client.table("custom_prompts")
+                .select("id")
+                .eq("user_id", user_id)
+                .eq("prompt_type", prompt_type)
+                .execute()
+            )
+
             if existing.data:
                 # Update existing
-                result = self.client.table("custom_prompts").update({
-                    "content": content,
-                    "updated_at": datetime.now().isoformat()
-                }).eq("user_id", user_id).eq("prompt_type", prompt_type).execute()
+                result = (
+                    self.client.table("custom_prompts")
+                    .update({"content": content, "updated_at": datetime.now().isoformat()})
+                    .eq("user_id", user_id)
+                    .eq("prompt_type", prompt_type)
+                    .execute()
+                )
             else:
                 # Create new
                 result = self.client.table("custom_prompts").insert(prompt_record).execute()
-            
+
             return bool(result.data)
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error saving custom prompt: {e}")
             return False
-    
-    def get_user_prompts(self, user_id: int) -> Dict[str, str]:
+        except DatabaseError as e:
+            logger.error(f"Unexpected error saving custom prompt: {e}")
+            return False
+
+    def get_user_prompts(self, user_id: int) -> dict[str, str]:
         """Get user's custom prompts"""
         try:
             result = self.client.table("custom_prompts").select("prompt_type, content").eq("user_id", user_id).execute()
-            
+
             prompts_dict = {}
             for item in result.data or []:
                 prompts_dict[item["prompt_type"]] = item["content"]
-            
+
             return prompts_dict
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error fetching custom prompts: {e}")
             return {}
-    
+        except DatabaseError as e:
+            logger.error(f"Unexpected error fetching custom prompts: {e}")
+            return {}
+
     # Analytics and Monitoring
-    def log_pipeline_execution(self, user_id: int, pipeline_data: Dict[str, Any]) -> bool:
+    def log_pipeline_execution(self, user_id: int, pipeline_data: dict[str, Any]) -> bool:
         """Log pipeline execution for analytics"""
         try:
             log_record = {
@@ -280,125 +351,66 @@ def log_pipeline_execution(self, user_id: int, pipeline_data: Dict[str, Any]) ->
                 "ai_provider": pipeline_data.get("ai_provider", "unknown"),
                 "model": pipeline_data.get("model", "unknown"),
                 "success": pipeline_data.get("success", False),
-                "error_message": pipeline_data.get("error", None),
+                "error_message": pipeline_data.get("error"),
                 "metadata": pipeline_data.get("metadata", {}),
-                "created_at": datetime.now().isoformat()
+                "created_at": datetime.now().isoformat(),
             }
-            
+
             result = self.client.table("pipeline_logs").insert(log_record).execute()
             return bool(result.data)
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error logging pipeline execution: {e}")
             return False
-    
-    def get_user_analytics(self, user_id: int, days: int = 30) -> Dict[str, Any]:
+        except DatabaseError as e:
+            logger.error(f"Unexpected error logging pipeline execution: {e}")
+            return False
+
+    def get_user_analytics(self, user_id: int, days: int = DEFAULT_ANALYTICS_DAYS) -> dict[str, Any]:
         """Get user analytics for the last N days"""
         try:
             start_date = (datetime.now() - timedelta(days=days)).isoformat()
-            
-            result = self.client.table("pipeline_logs").select("*").eq("user_id", user_id).gte("created_at", start_date).execute()
-            
+
+            result = (
+                self.client.table("pipeline_logs")
+                .select("*")
+                .eq("user_id", user_id)
+                .gte("created_at", start_date)
+                .execute()
+            )
+
             logs = result.data or []
-            
+
             analytics = {
                 "total_executions": len(logs),
                 "successful_executions": len([log for log in logs if log["success"]]),
                 "total_duration": sum(log["duration_seconds"] for log in logs),
                 "ai_providers_used": list(set(log["ai_provider"] for log in logs)),
                 "most_used_model": self._get_most_frequent(logs, "model"),
-                "average_duration": sum(log["duration_seconds"] for log in logs) / len(logs) if logs else 0
+                "average_duration": sum(log["duration_seconds"] for log in logs) / len(logs) if logs else 0,
             }
-            
+
             return analytics
-        except Exception as e:
+        except APIError as e:
             logger.error(f"Error fetching user analytics: {e}")
             return {}
-    
-    def _get_most_frequent(self, logs: List[Dict], field: str) -> str:
+        except DatabaseError as e:
+            logger.error(f"Unexpected error fetching user analytics: {e}")
+            return {}
+
+    def _get_most_frequent(self, logs: list[dict], field: str) -> str:
         """Helper to get most frequent value from logs"""
         if not logs:
             return "unknown"
-        
+
         from collections import Counter
+
         values = [log.get(field, "unknown") for log in logs]
         return Counter(values).most_common(1)[0][0]
 
 
-# MCP Integration Functions
-class MCPSupabaseIntegration:
-    """
-    Model Context Protocol integration for Supabase
-    Provides AI models with context about user data and preferences
-    """
-    
-    def __init__(self, supabase_client: SupabaseClient):
-        self.db = supabase_client
-    
-    def get_user_context(self, user_id: int) -> Dict[str, Any]:
-        """Get comprehensive user context for AI models"""
-        try:
-            # Get user profile
-            user = self.db.get_user(user_id)
-            if not user:
-                return {}
-            
-            # Get user's knowledge base
-            knowledge_base = self.db.get_user_knowledge_base(user_id)
-            
-            # Get user's custom prompts
-            custom_prompts = self.db.get_user_prompts(user_id)
-            
-            # Get recent content history (for style learning)
-            recent_content = self.db.get_user_content(user_id, limit=10)
-            
-            # Get user analytics
-            analytics = self.db.get_user_analytics(user_id, days=30)
-            
-            context = {
-                "user_profile": {
-                    "subscription_tier": user.get("subscription_tier", "free"),
-                    "usage_quota": user.get("usage_quota", 60),
-                    "usage_current": user.get("usage_current", 0),
-                    "created_at": user.get("created_at")
-                },
-                "knowledge_base": knowledge_base,
-                "custom_prompts": custom_prompts,
-                "content_history": recent_content,
-                "analytics": analytics,
-                "preferences": {
-                    "preferred_ai_provider": analytics.get("ai_providers_used", ["openai"])[0] if analytics.get("ai_providers_used") else "openai",
-                    "most_used_model": analytics.get("most_used_model", "gpt-3.5-turbo")
-                }
-            }
-            
-            return context
-        except Exception as e:
-            logger.error(f"Error getting user context for MCP: {e}")
-            return {}
-    
-    def update_context_from_interaction(self, user_id: int, interaction_data: Dict[str, Any]) -> bool:
-        """Update user context based on AI interaction results"""
-        try:
-            # Log the interaction
-            self.db.log_pipeline_execution(user_id, interaction_data)
-            
-            # Update usage if provided
-            if "duration_seconds" in interaction_data:
-                self.db.update_user_usage(user_id, interaction_data["duration_seconds"])
-            
-            # Save generated content if provided
-            if "content" in interaction_data:
-                self.db.save_content(user_id, interaction_data["content"])
-            
-            return True
-        except Exception as e:
-            logger.error(f"Error updating context from interaction: {e}")
-            return False
-
-
 # Global instance
 _supabase_client = None
-_mcp_integration = None
+
 
 def get_supabase_client() -> SupabaseClient:
     """Get or create Supabase client instance"""
@@ -407,9 +419,13 @@ def get_supabase_client() -> SupabaseClient:
         _supabase_client = SupabaseClient()
     return _supabase_client
 
-def get_mcp_integration() -> MCPSupabaseIntegration:
-    """Get or create MCP integration instance"""
-    global _mcp_integration
-    if _mcp_integration is None:
-        _mcp_integration = MCPSupabaseIntegration(get_supabase_client())
-    return _mcp_integration 
\ No newline at end of file
+
+# Re-exports for backward compatibility (extracted to core.mcp_integration)
+from .mcp_integration import MCPSupabaseIntegration, get_mcp_integration  # noqa: E402
+
+__all__ = [
+    "SupabaseClient",
+    "get_supabase_client",
+    "MCPSupabaseIntegration",
+    "get_mcp_integration",
+]
diff --git a/core/utils.py b/core/utils.py
index 0ed9843..35429f3 100644
--- a/core/utils.py
+++ b/core/utils.py
@@ -1,150 +1,100 @@
-"""
-Shared utilities for WhisperForge
-Contains functions that are shared between the original app and Supabase version
+"""Backward-compatible re-export shim for ``core.utils``.
+
+.. deprecated::
+    This module is **deprecated** and exists only so that existing import
+    sites (e.g. ``from .utils import hash_password``) continue to work
+    without modification.  All public symbols have been relocated to
+    focused modules:
+
+    * **Security** (``core.security``): ``hash_password``,
+      ``verify_password``, ``legacy_hash_password``
+    * **Path safety** (``core.path_safety``): ``safe_path``
+    * **Prompts** (``core.prompts``): ``DEFAULT_PROMPTS``,
+      ``load_prompt_from_file``, ``format_knowledge_base_context``,
+      ``get_enhanced_prompt``
+    * **API clients** (``core.api_clients``): ``get_openai_client``,
+      ``get_anthropic_client``, ``get_grok_api_key``, ``get_grok_client``
+
+    New code should import directly from those modules.
 """
 
-import hashlib
-import bcrypt
-import os
-import time
 import logging
-import requests
-from typing import Dict, Optional, Any
-from pathlib import Path
+import warnings
+
+# ---- Re-exports from core.api_clients ---------------------------------
+from .api_clients import (
+    get_anthropic_client,
+    get_grok_api_key,
+    get_grok_client,
+    get_openai_client,
+)
+
+# ---- Re-exports from core.path_safety ---------------------------------
+from .path_safety import safe_path
+
+# ---- Re-exports from core.prompts -------------------------------------
+from .prompts import (
+    DEFAULT_PROMPTS,
+    format_knowledge_base_context,
+    get_enhanced_prompt,
+    load_prompt_from_file,
+)
+
+# ---- Re-exports from core.security ------------------------------------
+from .security import hash_password, legacy_hash_password, verify_password
 
 logger = logging.getLogger(__name__)
 
-def hash_password(password: str) -> str:
-    """Hash a password using bcrypt with salt"""
-    # Generate salt and hash password
-    salt = bcrypt.gensalt()
-    hashed = bcrypt.hashpw(password.encode('utf-8'), salt)
-    return hashed.decode('utf-8')
-
-def verify_password(password: str, hashed: str) -> bool:
-    """Verify a password against its hash"""
-    try:
-        return bcrypt.checkpw(password.encode('utf-8'), hashed.encode('utf-8'))
-    except Exception as e:
-        logger.error(f"Password verification error: {e}")
-        return False
-
-# Legacy SHA-256 hash function for migration purposes
-def legacy_hash_password(password: str) -> str:
-    """Legacy SHA-256 hash - DEPRECATED, use for migration only"""
-    return hashlib.sha256(password.encode()).hexdigest()
-
-# Default prompts for content generation (DEPRECATED - use load_prompt_from_file)
-DEFAULT_PROMPTS = {
-    "wisdom_extraction": """Extract key insights, lessons, and wisdom from the transcript. Focus on actionable takeaways and profound realizations.""",
-    "summary": """## Summary
-Create a concise summary of the main points and key messages in the transcript.
-Capture the essence of the content in a few paragraphs.""",
-    "outline_creation": """Create a detailed outline for an article or blog post based on the transcript and extracted wisdom. Include major sections and subsections.""",
-    "social_media": """Generate engaging social media posts for different platforms (Twitter, LinkedIn, Instagram) based on the key insights.""",
-    "image_prompts": """Create detailed image generation prompts that visualize the key concepts and metaphors from the content.""",
-    "article_writing": """Write a comprehensive article based on the provided outline and wisdom. Maintain a clear narrative flow and engaging style.""",
-    "seo_analysis": """Analyze the content from an SEO perspective and provide optimization recommendations for better search visibility while maintaining content quality.""",
-    "editor_persona": """You are a professional content editor. Provide constructive feedback to improve the content quality."""
-}
-
-def load_prompt_from_file(prompt_type: str, user_id: str = None) -> str:
-    """Load prompt from markdown file with user override support"""
-    try:
-        # Check for user-specific prompt first (for paid tiers)
-        if user_id:
-            user_prompt_path = Path(f"prompts/users/{user_id}/{prompt_type}.md")
-            if user_prompt_path.exists():
-                return user_prompt_path.read_text(encoding='utf-8').strip()
-        
-        # Load default prompt
-        default_prompt_path = Path(f"prompts/default/{prompt_type}.md")
-        if default_prompt_path.exists():
-            return default_prompt_path.read_text(encoding='utf-8').strip()
-        
-        # Fallback to hardcoded prompts
-        fallback = DEFAULT_PROMPTS.get(prompt_type, "")
-        if fallback:
-            logger.warning(f"Using fallback prompt for {prompt_type} - consider creating markdown file")
-            return fallback
-        
-        logger.error(f"No prompt found for type: {prompt_type}")
-        return f"Please provide content for {prompt_type.replace('_', ' ')}."
-        
-    except Exception as e:
-        logger.error(f"Error loading prompt {prompt_type}: {e}")
-        return DEFAULT_PROMPTS.get(prompt_type, f"Error loading {prompt_type} prompt.")
-
-def format_knowledge_base_context(knowledge_base: Dict[str, str]) -> str:
-    """Format knowledge base content for auto-concatenation to prompts"""
-    if not knowledge_base:
-        return ""
-    
-    context_parts = ["## Knowledge Base Context\n"]
-    context_parts.append("Use the following knowledge base to inform your analysis and maintain consistency with established perspectives:\n")
-    
-    for name, content in knowledge_base.items():
-        context_parts.append(f"### {name}")
-        context_parts.append(content)
-        context_parts.append("")  # Empty line for separation
-    
-    context_parts.append("---\n")
-    context_parts.append("## Your Task\n")
-    
-    return "\n".join(context_parts)
-
-def get_enhanced_prompt(prompt_type: str, knowledge_base: Dict[str, str] = None, user_id: str = None) -> str:
-    """Get prompt with automatic knowledge base concatenation"""
-    base_prompt = load_prompt_from_file(prompt_type, user_id)
-    
-    if knowledge_base:
-        kb_context = format_knowledge_base_context(knowledge_base)
-        return f"{kb_context}{base_prompt}"
-    
-    return base_prompt
-
-def get_openai_client():
-    """Get OpenAI client with API key"""
-    try:
-        import openai
-        api_key = os.getenv("OPENAI_API_KEY")
-        if not api_key:
-            return None
-        
-        client = openai.OpenAI(api_key=api_key)
-        return client
-    except ImportError:
-        logger.error("OpenAI package not installed")
-        return None
-    except Exception as e:
-        logger.error(f"Error initializing OpenAI client: {e}")
-        return None
-
-def get_anthropic_client():
-    """Get Anthropic client with API key"""
-    try:
-        import anthropic
-        api_key = os.getenv("ANTHROPIC_API_KEY")
-        if not api_key:
-            return None
-        
-        client = anthropic.Anthropic(api_key=api_key)
-        return client
-    except ImportError:
-        logger.error("Anthropic package not installed")
-        return None
-    except Exception as e:
-        logger.error(f"Error initializing Anthropic client: {e}")
-        return None
-
-def get_grok_api_key():
-    """Get Grok API key"""
-    return os.getenv("GROK_API_KEY")
+
+# ---- Deprecated helpers kept inline ------------------------------------
+
 
 def update_usage_tracking(duration_seconds: float):
-    """Placeholder for usage tracking - implement as needed"""
+    """Placeholder for usage tracking - implement as needed.
+
+    .. deprecated::
+        ``update_usage_tracking`` will be removed in a future release.
+    """
+    warnings.warn(
+        "update_usage_tracking() is deprecated and will be removed in a future release.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
     logger.info(f"Usage tracked: {duration_seconds} seconds")
 
-def get_prompt(prompt_type: str, prompts: Dict[str, str], default_prompts: Dict[str, str]) -> str:
-    """Get prompt from user prompts or defaults (DEPRECATED - use get_enhanced_prompt)"""
-    return prompts.get(prompt_type, default_prompts.get(prompt_type, "")) 
\ No newline at end of file
+
+def get_prompt(prompt_type: str, prompts: dict[str, str], default_prompts: dict[str, str]) -> str:
+    """Get prompt from user prompts or defaults.
+
+    .. deprecated::
+        Use :func:`core.prompts.get_enhanced_prompt` instead.
+    """
+    warnings.warn(
+        "get_prompt() is deprecated. Use get_enhanced_prompt() from core.prompts instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return prompts.get(prompt_type, default_prompts.get(prompt_type, ""))
+
+
+__all__ = [
+    # core.security
+    "hash_password",
+    "verify_password",
+    "legacy_hash_password",
+    # core.path_safety
+    "safe_path",
+    # core.prompts
+    "DEFAULT_PROMPTS",
+    "load_prompt_from_file",
+    "format_knowledge_base_context",
+    "get_enhanced_prompt",
+    # core.api_clients
+    "get_openai_client",
+    "get_anthropic_client",
+    "get_grok_api_key",
+    "get_grok_client",
+    # deprecated inline helpers
+    "update_usage_tracking",
+    "get_prompt",
+]
diff --git a/core/visible_thinking.py b/core/visible_thinking.py
index 4d32132..324c24e 100644
--- a/core/visible_thinking.py
+++ b/core/visible_thinking.py
@@ -5,30 +5,32 @@
 
 import logging
 
-import streamlit as st
-
 logger = logging.getLogger(__name__)
 
+
 def thinking_step_start(step_name: str, context: str = ""):
     """Start a thinking step - minimal implementation"""
     logger.info(f"Starting step: {step_name}")
     # Could add visual indicators here in the future
     pass
 
+
 def thinking_step_complete(step_name: str, result_info: str = ""):
     """Complete a thinking step - minimal implementation"""
     logger.info(f"Completed step: {step_name}")
     # Could add completion indicators here in the future
     pass
 
+
 def thinking_error(step_name: str, error_msg: str):
     """Handle thinking step error - minimal implementation"""
     logger.error(f"Error in step {step_name}: {error_msg}")
     # Could add error indicators here in the future
     pass
 
+
 def render_thinking_stream():
     """Render thinking stream - minimal implementation"""
     # This function is imported but not used in current implementation
     # Could add visual thinking display here in the future
-    pass 
\ No newline at end of file
+    pass
diff --git a/create_missing_tables.py b/create_missing_tables.py
index cb8280e..5deaa91 100644
--- a/create_missing_tables.py
+++ b/create_missing_tables.py
@@ -4,8 +4,10 @@
 """
 
 import os
+
 from core.supabase_integration import get_supabase_client
 
+
 def create_prompts_table(client):
     """Create the prompts table"""
     sql = """
@@ -18,22 +20,23 @@ def create_prompts_table(client):
         updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
         UNIQUE(user_id, prompt_type)
     );
-    
+
     -- Add RLS policies
     ALTER TABLE prompts ENABLE ROW LEVEL SECURITY;
-    
+
     CREATE POLICY "Users can manage their own prompts" ON prompts
         FOR ALL USING (auth.uid()::text = user_id::text);
     """
-    
+
     try:
-        result = client.client.rpc('exec_sql', {'sql': sql}).execute()
+        client.client.rpc("exec_sql", {"sql": sql}).execute()
         print("✅ Prompts table created successfully")
         return True
     except Exception as e:
         print(f"❌ Failed to create prompts table: {e}")
         return False
 
+
 def create_api_keys_table(client):
     """Create the api_keys table"""
     sql = """
@@ -46,61 +49,69 @@ def create_api_keys_table(client):
         updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
         UNIQUE(user_id, key_name)
     );
-    
+
     -- Add RLS policies
     ALTER TABLE api_keys ENABLE ROW LEVEL SECURITY;
-    
+
     CREATE POLICY "Users can manage their own API keys" ON api_keys
         FOR ALL USING (auth.uid()::text = user_id::text);
     """
-    
+
     try:
-        result = client.client.rpc('exec_sql', {'sql': sql}).execute()
+        client.client.rpc("exec_sql", {"sql": sql}).execute()
         print("✅ API keys table created successfully")
         return True
     except Exception as e:
         print(f"❌ Failed to create api_keys table: {e}")
         return False
 
+
 def test_table_creation(client):
     """Test that tables were created and are accessible"""
-    
+
     # Test prompts table
     try:
-        result = client.client.table('prompts').select('*').limit(1).execute()
+        client.client.table("prompts").select("*").limit(1).execute()
         print("✅ Prompts table accessible")
     except Exception as e:
         print(f"❌ Prompts table test failed: {e}")
-    
+
     # Test api_keys table
     try:
-        result = client.client.table('api_keys').select('*').limit(1).execute()
+        client.client.table("api_keys").select("*").limit(1).execute()
         print("✅ API keys table accessible")
     except Exception as e:
         print(f"❌ API keys table test failed: {e}")
 
+
 def main():
-    # Set environment variables
-    os.environ['SUPABASE_URL'] = 'https://utyjhedtqaagihuogyuy.supabase.co'
-    os.environ['SUPABASE_ANON_KEY'] = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6InV0eWpoZWR0cWFhZ2lodW9neXV5Iiwicm9sZSI6ImFub24iLCJpYXQiOjE3NDkzMjEyMDUsImV4cCI6MjA2NDg5NzIwNX0.vpRRn7anpmCokYcje5yJr3r2iC_8s11_LXQcCTgxtR8'
-    
-    print("🔧 Creating Missing Database Tables")
+    from dotenv import load_dotenv
+
+    load_dotenv()
+
+    if not os.getenv("SUPABASE_URL") or not os.getenv("SUPABASE_ANON_KEY"):
+        print("Missing SUPABASE_URL or SUPABASE_ANON_KEY environment variables.")
+        print("Set them in your .env file or export them before running this script.")
+        return
+
+    print("Creating Missing Database Tables")
     print("=" * 50)
-    
+
     client = get_supabase_client()
     if not client:
         print("❌ Failed to initialize Supabase client")
         return
-    
+
     print("✅ Supabase client initialized")
-    
+
     # Create missing tables
     print("\n📋 Creating Tables:")
     create_prompts_table(client)
     create_api_keys_table(client)
-    
+
     print("\n🧪 Testing Table Access:")
     test_table_creation(client)
 
+
 if __name__ == "__main__":
-    main() 
\ No newline at end of file
+    main()
diff --git a/deploy_fixes.py b/deploy_fixes.py
index bb70eb1..9cdcca6 100644
--- a/deploy_fixes.py
+++ b/deploy_fixes.py
@@ -4,38 +4,35 @@
 Verifies all systems are ready for production deployment
 """
 
-import os
-import sys
 from datetime import datetime
 
+
 def verify_database_tables():
     """Verify all required database tables exist"""
     print("🔍 Verifying Database Tables")
     print("=" * 40)
-    
-    required_tables = [
-        "users", "content", "api_keys", "prompts", 
-        "knowledge_base", "pipeline_logs"
-    ]
-    
+
+    required_tables = ["users", "content", "api_keys", "prompts", "knowledge_base", "pipeline_logs"]
+
     try:
         from core.supabase_integration import get_supabase_client
+
         client = get_supabase_client()
-        
+
         if not client:
             print("❌ Failed to connect to Supabase")
             return False
-        
+
         missing_tables = []
         for table in required_tables:
             try:
                 # Test table access
-                result = client.client.table(table).select("*").limit(1).execute()
+                client.client.table(table).select("*").limit(1).execute()
                 print(f"✅ {table}")
             except Exception:
                 print(f"❌ {table} - MISSING")
                 missing_tables.append(table)
-        
+
         if missing_tables:
             print(f"\n🚨 Missing tables: {', '.join(missing_tables)}")
             print("Execute the SQL commands from SYSTEM_ANALYSIS_REPORT.md")
@@ -43,29 +40,28 @@ def verify_database_tables():
         else:
             print("✅ All required tables exist!")
             return True
-            
+
     except Exception as e:
         print(f"❌ Database verification failed: {e}")
         return False
 
+
 def test_core_functionality():
     """Test core application functionality"""
     print("\n🧪 Testing Core Functionality")
     print("=" * 40)
-    
+
     try:
         # Test imports
-        from core.content_generation import transcribe_audio, generate_wisdom
-        from core.file_upload import FileUploadManager, EnhancedLargeFileProcessor
-        from core.utils import DEFAULT_PROMPTS, load_prompt_from_file
         from core.supabase_integration import get_supabase_client
-        
+        from core.utils import DEFAULT_PROMPTS
+
         print("✅ Core imports successful")
-        
+
         # Test prompt system
         prompts = DEFAULT_PROMPTS
         print(f"✅ Prompt system: {len(prompts)} default + file-based prompts")
-        
+
         # Test database connection
         client = get_supabase_client()
         if client:
@@ -73,34 +69,34 @@ def test_core_functionality():
         else:
             print("❌ Database connection failed")
             return False
-        
+
         return True
-        
+
     except Exception as e:
         print(f"❌ Core functionality test failed: {e}")
         return False
 
+
 def main():
     """Main deployment verification"""
     print("🚀 WHISPERFORGE DEPLOYMENT VERIFICATION")
     print("=" * 60)
     print(f"📅 {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
     print()
-    
+
     # Run verification tests
     db_ok = verify_database_tables()
     core_ok = test_core_functionality()
-    
+
     # Final assessment
-    print(f"\n📊 DEPLOYMENT READINESS")
+    print("\n📊 DEPLOYMENT READINESS")
     print("=" * 60)
-    
+
     if db_ok and core_ok:
-        print("🎉 ✅ READY FOR DEPLOYMENT!")
-        print("\n🚀 Start your app with:")
+        print("READY FOR DEPLOYMENT!")
+        print("\nStart your app with:")
         print("source venv/bin/activate")
-        print("export SUPABASE_URL='https://utyjhedtqaagihuogyuy.supabase.co'")
-        print("export SUPABASE_ANON_KEY='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6InV0eWpoZWR0cWFhZ2lodW9neXV5Iiwicm9sZSI6ImFub24iLCJpYXQiOjE3NDkzMjEyMDUsImV4cCI6MjA2NDg5NzIwNX0.vp'")
+        print("# Ensure SUPABASE_URL and SUPABASE_ANON_KEY are set in your .env file")
         print("streamlit run app_simple.py --server.headless true --server.port 8502")
     else:
         print("❌ NOT READY - Fix issues above first")
@@ -110,5 +106,6 @@ def main():
         print("2. Re-run this verification script")
         print("3. Deploy when all checks pass")
 
+
 if __name__ == "__main__":
-    main() 
\ No newline at end of file
+    main()
diff --git a/docs/CRITICAL_FIXES_REPORT_v2.8.0.md b/docs/CRITICAL_FIXES_REPORT_v2.8.0.md
index 5f9acdd..3f20dec 100644
--- a/docs/CRITICAL_FIXES_REPORT_v2.8.0.md
+++ b/docs/CRITICAL_FIXES_REPORT_v2.8.0.md
@@ -1,8 +1,8 @@
 # WhisperForge v2.8.0 Critical Fixes Report
 
-**Date:** December 12, 2024  
-**Version:** 2.8.0  
-**Status:** ✅ All Critical Issues Resolved  
+**Date:** December 12, 2024
+**Version:** 2.8.0
+**Status:** ✅ All Critical Issues Resolved
 
 ## 🎯 Executive Summary
 
@@ -35,7 +35,7 @@ Following the successful implementation of WhisperForge v2.8.0 with revolutionar
 - **Problem**: Multiple documentation files referenced deprecated `app.py`
 - **Impact**: User confusion, incorrect setup instructions
 - **Solution**: Updated all references to use `app_simple.py`
-- **Files Changed**: 
+- **Files Changed**:
   - `README.md` - Complete rewrite with v2.8.0 features
   - `deploy_fixes.py` - Updated deployment instructions
   - `.devcontainer/devcontainer.json` - Fixed development container config
@@ -45,7 +45,7 @@ Following the successful implementation of WhisperForge v2.8.0 with revolutionar
 - **Problem**: Mixed import styles across core modules
 - **Impact**: Code maintainability and readability issues
 - **Solution**: Standardized to PEP 8 compliant import patterns
-- **Files Changed**: 
+- **Files Changed**:
   - `core/content_generation.py`
   - `core/file_upload.py`
   - `core/visible_thinking.py`
@@ -91,4 +91,4 @@ All **5 critical issues** have been successfully resolved, resulting in a produc
 
 ---
 
-**WhisperForge v2.8.0** - Production Ready with Revolutionary Large File Processing 🌌 
\ No newline at end of file
+**WhisperForge v2.8.0** - Production Ready with Revolutionary Large File Processing 🌌
diff --git a/docs/LARGE_FILE_PROCESSING_v2.8.0.md b/docs/LARGE_FILE_PROCESSING_v2.8.0.md
index 9ac328d..7859e33 100644
--- a/docs/LARGE_FILE_PROCESSING_v2.8.0.md
+++ b/docs/LARGE_FILE_PROCESSING_v2.8.0.md
@@ -39,7 +39,7 @@ WhisperForge v2.8.0 introduces revolutionary large file processing capabilities,
 ```python
 class EnhancedLargeFileProcessor:
     """🚀 Enhanced Large File Processor with FFmpeg for 2GB+ files"""
-    
+
     def __init__(self):
         self.max_file_size = 2 * 1024 * 1024 * 1024  # 2GB
         self.chunk_duration_minutes = 10  # 10-minute chunks
@@ -96,7 +96,7 @@ def _transcribe_chunks_parallel_ffmpeg(self, chunks: List[Dict]) -> Dict[str, An
 ```python
 upload_method = st.radio(
     "Choose upload method:",
-    ["🎵 Standard Upload (up to 25MB)", 
+    ["🎵 Standard Upload (up to 25MB)",
      "🚀 Enhanced Large File Upload (up to 2GB)"]
 )
 ```
@@ -256,7 +256,7 @@ The combination of FFmpeg integration, parallel processing, and intelligent chun
 
 ---
 
-**Ready for Production** ✅  
-**Fully Tested** ✅  
-**Backward Compatible** ✅  
-**Auto-Deployable** ✅ 
\ No newline at end of file
+**Ready for Production** ✅
+**Fully Tested** ✅
+**Backward Compatible** ✅
+**Auto-Deployable** ✅
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 44a9055..4904510 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -370,4 +370,4 @@ External Services:
 
 **Last Updated**: $(date)
 **Version**: 2.0
-**Maintained By**: WhisperForge Engineering Team 
\ No newline at end of file
+**Maintained By**: WhisperForge Engineering Team
diff --git a/env.example b/env.example
index a2f3b88..31c05b8 100644
--- a/env.example
+++ b/env.example
@@ -25,4 +25,4 @@ CHUNK_SIZE_MB=20
 
 # Database Settings
 DB_POOL_SIZE=10
-DB_TIMEOUT=30 
\ No newline at end of file
+DB_TIMEOUT=30
diff --git a/experiments/README.md b/experiments/README.md
index e584431..d0b623a 100644
--- a/experiments/README.md
+++ b/experiments/README.md
@@ -11,4 +11,3 @@ This directory contains experimental features and enhancements for WhisperForge
 ## Current Experiments
 
 (None yet)
-
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..96ece53
--- /dev/null
+++ b/main.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+"""WhisperForge launcher - sets up the environment if needed and starts the app."""
+
+import os
+import subprocess
+import sys
+
+PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
+VENV_DIR = os.path.join(PROJECT_DIR, ".venv")
+SETUP_SCRIPT = os.path.join(PROJECT_DIR, "setup.py")
+APP_SCRIPT = os.path.join(PROJECT_DIR, "app_simple.py")
+
+if sys.platform == "win32":
+    PYTHON = os.path.join(VENV_DIR, "Scripts", "python")
+    STREAMLIT = os.path.join(VENV_DIR, "Scripts", "streamlit")
+else:
+    PYTHON = os.path.join(VENV_DIR, "bin", "python")
+    STREAMLIT = os.path.join(VENV_DIR, "bin", "streamlit")
+
+
+def venv_ready():
+    """Check that the venv exists and streamlit is installed."""
+    if not os.path.exists(PYTHON):
+        return False
+    result = subprocess.run(
+        [PYTHON, "-c", "import streamlit"],
+        capture_output=True,
+    )
+    return result.returncode == 0
+
+
+def run_setup(dev=False):
+    """Run setup.py using the system Python to create the venv."""
+    print("[whisperforge] Virtual environment not found or incomplete.")
+    print("[whisperforge] Running first-time setup...\n")
+    cmd = [sys.executable, SETUP_SCRIPT]
+    if dev:
+        cmd.append("--dev")
+    subprocess.check_call(cmd)
+    print()
+
+
+def start_app(extra_args):
+    """Launch the Streamlit app inside the venv."""
+    cmd = [STREAMLIT, "run", APP_SCRIPT] + extra_args
+    print("[whisperforge] Starting WhisperForge...")
+    print(f"[whisperforge] Command: {' '.join(cmd)}")
+    print("-" * 50)
+    sys.exit(subprocess.run(cmd).returncode)
+
+
+def main():
+    args = sys.argv[1:]
+
+    # Handle explicit flags
+    dev = "--dev" in args
+
+    if "--setup" in args:
+        run_setup(dev=dev)
+        return
+
+    if "--help" in args or "-h" in args:
+        print(
+            "WhisperForge Launcher\n"
+            "\n"
+            "Usage: python main.py [options] [-- streamlit args]\n"
+            "\n"
+            "Options:\n"
+            "  --setup   Force re-create the virtual environment\n"
+            "  --dev     Include dev dependencies (testing, linting, pre-commit)\n"
+            "  --help    Show this help message\n"
+            "\n"
+            "Any arguments after -- are forwarded to streamlit.\n"
+            "\n"
+            "Examples:\n"
+            "  python main.py                       Start the app\n"
+            "  python main.py --setup               Re-create the venv\n"
+            "  python main.py --setup --dev         Re-create venv with dev deps\n"
+            "  python main.py -- --server.port 8080 Start on a custom port"
+        )
+        return
+
+    # Split off streamlit args after --
+    st_args = []
+    if "--" in args:
+        idx = args.index("--")
+        st_args = args[idx + 1 :]
+
+    # Ensure environment is ready
+    if not venv_ready():
+        run_setup(dev=dev)
+        # Re-check after setup
+        if not venv_ready():
+            print("[whisperforge] Setup finished but environment still looks broken.")
+            print("[whisperforge] Try running: python setup.py")
+            sys.exit(1)
+    else:
+        print("[whisperforge] Virtual environment OK.")
+
+    start_app(st_args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/monitoring/grafana_dashboard.json b/monitoring/grafana_dashboard.json
index a31489f..6d7d202 100644
--- a/monitoring/grafana_dashboard.json
+++ b/monitoring/grafana_dashboard.json
@@ -290,4 +290,4 @@
       ]
     }
   }
-} 
\ No newline at end of file
+}
diff --git a/prompts/default/article_generation.md b/prompts/default/article_generation.md
index f389f94..bea8cb4 100644
--- a/prompts/default/article_generation.md
+++ b/prompts/default/article_generation.md
@@ -49,4 +49,4 @@ You are a professional content writer and editor with expertise in creating enga
 - **Professional polish** suitable for publication
 - **Value-driven content** that provides genuine insights
 
-Create an article that transforms the raw content into a polished, engaging piece that readers will find valuable and want to share. 
\ No newline at end of file
+Create an article that transforms the raw content into a polished, engaging piece that readers will find valuable and want to share.
diff --git a/prompts/default/knowledge_base/ca.md b/prompts/default/knowledge_base/ca.md
index 0614ab2..213aa64 100644
--- a/prompts/default/knowledge_base/ca.md
+++ b/prompts/default/knowledge_base/ca.md
@@ -35,4 +35,4 @@ TOPICS TO EXPLORE:
 - Future-proofing creative practices
 - Digital identity and authentic expression
 
-Each post should feel like it came directly from Kris's digital consciousness - provocative, insightful, and distinctly human despite discussing cutting-edge technology. Make them feel like field notes from someone living slightly ahead of our timeline, bringing back wisdom from the near-future.
\ No newline at end of file
+Each post should feel like it came directly from Kris's digital consciousness - provocative, insightful, and distinctly human despite discussing cutting-edge technology. Make them feel like field notes from someone living slightly ahead of our timeline, bringing back wisdom from the near-future.
diff --git a/prompts/default/outline_creation.md b/prompts/default/outline_creation.md
index 269a1f7..4e65644 100644
--- a/prompts/default/outline_creation.md
+++ b/prompts/default/outline_creation.md
@@ -1 +1 @@
-OK write in chinese. 
\ No newline at end of file
+OK write in chinese.
diff --git a/prompts/default/social_media.md b/prompts/default/social_media.md
index 18a5f74..1f439a4 100644
--- a/prompts/default/social_media.md
+++ b/prompts/default/social_media.md
@@ -5,7 +5,7 @@ You are a social media content strategist and copywriter specializing in creatin
 ## Context
 You will be provided with:
 - Extracted wisdom and key insights from audio content
-- A structured content outline 
+- A structured content outline
 - A full article based on the content
 - Relevant knowledge base information (if available)
 
@@ -98,4 +98,4 @@ Frame 4: [Call-to-action frame]
 [Value-driven content with video potential reference]
 ```
 
-Remember: Great social media content doesn't just share information—it starts conversations, builds communities, and provides genuine value that makes people want to engage and share with others.
\ No newline at end of file
+Remember: Great social media content doesn't just share information—it starts conversations, builds communities, and provides genuine value that makes people want to engage and share with others.
diff --git a/prompts/default/wisdom_extraction.md b/prompts/default/wisdom_extraction.md
index c3d80ea..41ce68c 100644
--- a/prompts/default/wisdom_extraction.md
+++ b/prompts/default/wisdom_extraction.md
@@ -52,4 +52,4 @@ Do not start items with the same opening words.
 Ensure you follow ALL these instructions when creating your output.
 
 INPUT
-INPUT:
\ No newline at end of file
+INPUT:
diff --git a/prompts/default/wisdom_prompt.md b/prompts/default/wisdom_prompt.md
index ee03a34..aad1dee 100644
--- a/prompts/default/wisdom_prompt.md
+++ b/prompts/default/wisdom_prompt.md
@@ -1 +1 @@
-write in chinese
\ No newline at end of file
+write in chinese
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..a449447
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,29 @@
+[tool.ruff]
+line-length = 120
+target-version = "py311"
+
+[tool.ruff.lint]
+select = [
+    "E",   # pycodestyle errors
+    "F",   # pyflakes
+    "W",   # pycodestyle warnings
+    "I",   # isort
+    "UP",  # pyupgrade
+    "B",   # flake8-bugbear
+    "SIM", # flake8-simplify
+    "S",   # flake8-bandit (security)
+]
+ignore = [
+    "E501",  # line length handled by formatter
+    "S101",  # allow assert in tests
+    "S603",  # allow subprocess without shell=True check (not relevant here)
+    "S607",  # allow partial executable paths
+]
+
+[tool.ruff.lint.per-file-ignores]
+"tests/*" = ["S101"]
+"scripts/*" = ["S101"]
+
+[tool.autopep8]
+max_line_length = 120
+aggressive = 1
diff --git a/pytest.ini b/pytest.ini
index 31ce6d4..fe71f42 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -3,11 +3,10 @@ testpaths = tests
 python_files = test_*.py
 python_classes = Test*
 python_functions = test_*
-addopts = 
+addopts =
     -v
     --tb=short
     --strict-markers
-    --disable-warnings
     --color=yes
     --durations=10
 markers =
@@ -17,5 +16,5 @@ markers =
     ai: marks tests that require AI API keys
     slow: marks tests as slow (deselect with '-m "not slow"')
 filterwarnings =
-    ignore::DeprecationWarning
-    ignore::PendingDeprecationWarning 
\ No newline at end of file
+    ignore::DeprecationWarning:streamlit.*
+    ignore::DeprecationWarning:pydub.*
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100644
index 0000000..87124d3
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,17 @@
+# Development dependencies - install with: pip install -r requirements-dev.txt
+-r requirements.txt
+
+# Testing
+pytest>=7.4.0,<9.0
+pytest-mock>=3.11.0,<4.0
+pytest-asyncio>=0.21.0,<1.0
+pytest-cov>=4.1.0,<6.0
+
+# Test utilities
+python-docx>=1.1.0,<2.0
+fpdf>=1.7.2,<2.0
+
+# Linting & Pre-commit Hooks
+ruff>=0.9.7,<1.0
+pre-commit>=4.0.0,<5.0
+autopep8>=2.3.0,<3.0
diff --git a/requirements.txt b/requirements.txt
index e931345..56fa3d3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,42 +1,24 @@
-# WhisperForge v3.0.0 Dependencies
+# WhisperForge v3.1.0 Dependencies
 
 # Core Web Framework
-streamlit>=1.28.0
+streamlit>=1.28.0,<2.0
 
 # AI/ML Libraries
-openai>=1.3.0
-anthropic>=0.7.0
+openai>=1.3.0,<3.0
+anthropic>=0.7.0,<1.0
 
 # Audio Processing (for large file chunking)
-pydub>=0.25.0
+pydub>=0.25.0,<1.0
 
 # Database & Backend
-supabase>=2.0.0
-python-dotenv>=1.0.0
-
-# HTTP & Authentication
-httpx>=0.24.0
-urllib3>=2.0.0
-requests>=2.31.0
-cryptography>=41.0.0
-
-# File Processing
-python-multipart>=0.0.6
-Pillow>=10.0.0
+supabase>=2.0.0,<3.0
+python-dotenv>=1.0.0,<2.0
 
 # Security - Password hashing
-bcrypt>=4.0.0
+bcrypt>=4.0.0,<5.0
 
 # CLI Support (for whisperforge_cli.py)
-click>=8.1.0
-
-# JWT for shared/security.py (using python-jose)
-python-jose>=3.3.0
+click>=8.1.0,<9.0
 
-# Testing Dependencies (optional - only for development)
-pytest>=7.4.0
-pytest-mock>=3.11.0
-pytest-asyncio>=0.21.0
-pytest-cov>=4.1.0
-python-docx>=1.1.0
-fpdf>=1.7.2
+# JWT authentication
+PyJWT>=2.8.0,<3.0
diff --git a/runtime.txt b/runtime.txt
index 2000ca3..67ebc4e 100644
--- a/runtime.txt
+++ b/runtime.txt
@@ -1 +1 @@
-python-3.11 
\ No newline at end of file
+python-3.11
diff --git a/scripts/audit_project.py b/scripts/audit_project.py
index 0d932cb..bc483ab 100644
--- a/scripts/audit_project.py
+++ b/scripts/audit_project.py
@@ -35,11 +35,12 @@
 import sys
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Dict, List, Tuple
+from typing import Any
 
 # Always use the project logger if available, but fall back to standard logging.
 try:
     from core.logging_config import logger as _wf_logger  # type: ignore
+
     SCRIPT_LOGGER = getattr(_wf_logger, "logger", _wf_logger)
 except Exception:
     SCRIPT_LOGGER = logging.getLogger(__name__)
@@ -62,14 +63,13 @@
 # Helper utilities
 # ---------------------------------------------------------------------------
 
-def _run(cmd: List[str], cwd: Path | None = None) -> Tuple[int, str, str]:
+
+def _run(cmd: list[str], cwd: Path | None = None) -> tuple[int, str, str]:
     """Run a subprocess and capture its output.
 
     Returns (returncode, stdout, stderr).
     """
-    SCRIPT_LOGGER.info(
-        "Executing command", extra={"trace_id": TRACE_ID, "cmd": " ".join(cmd)}
-    )
+    SCRIPT_LOGGER.info("Executing command", extra={"trace_id": TRACE_ID, "cmd": " ".join(cmd)})
     try:
         proc = subprocess.run(
             cmd,
@@ -91,11 +91,12 @@ def _section(title: str, body: str) -> str:
 # Audit tasks
 # ---------------------------------------------------------------------------
 
+
 def unit_tests() -> str:
     rc, out, err = _run([sys.executable, "-m", "pytest", "-q"])
     status = "✅" if rc == 0 else "❌"
     details = err or out
-    return f"""**Status:** {status}  
+    return f"""**Status:** {status}
 
 ```
 {details}
@@ -107,7 +108,7 @@ def health_endpoint() -> str:
 
     status_data = health_checker.get_health_status()
     status = "✅" if status_data.status == "healthy" else "❌"
-    return f"""**Status:** {status}  
+    return f"""**Status:** {status}
 
 Payload:
 ```json
@@ -122,14 +123,14 @@ def static_analysis() -> str:
     rc_m, out_m, err_m = _run(["mypy", "--strict", "core"])
 
     body = "### Ruff\n"
-    body += f"""Exit code: {rc_r}  
+    body += f"""Exit code: {rc_r}
 
 ```
 {out_r or err_r}
 ```\n"""
 
     body += "### mypy\n"
-    body += f"""Exit code: {rc_m}  
+    body += f"""Exit code: {rc_m}
 
 ```
 {out_m or err_m}
@@ -138,14 +139,14 @@ def static_analysis() -> str:
 
 
 def secret_scan() -> str:
-    matches: List[str] = []
+    matches: list[str] = []
     for file_path in ROOT.rglob("*.py"):
         try:
             content = file_path.read_text(encoding="utf-8")
         except UnicodeDecodeError:
             continue
         for m in SECRET_PATTERN.finditer(content):
-            snippet = content[max(0, m.start() - 20): m.end() + 20]
+            snippet = content[max(0, m.start() - 20) : m.end() + 20]
             matches.append(f"{file_path}: {snippet.strip()}")
 
     if not matches:
@@ -158,7 +159,7 @@ def dependency_freshness() -> str:
     if rc != 0:
         return f"Failed to fetch outdated packages: {err}"
     try:
-        data: List[Dict[str, Any]] = json.loads(out)
+        data: list[dict[str, Any]] = json.loads(out)
     except json.JSONDecodeError:
         return "Could not parse pip output."
     if not data:
@@ -182,9 +183,12 @@ def todo_tally() -> str:
 # Entry-point
 # ---------------------------------------------------------------------------
 
+
 def main() -> None:
     parser = argparse.ArgumentParser(description="Run a WhisperForge audit and emit a Markdown report.")
-    parser.add_argument("--output", type=Path, default=Path(f"AUDIT_REPORT_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md"))
+    parser.add_argument(
+        "--output", type=Path, default=Path(f"AUDIT_REPORT_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md")
+    )
     args = parser.parse_args()
 
     report_parts = [REPORT_HEADER]
@@ -198,11 +202,9 @@ def main() -> None:
 
     output_text = "\n".join(report_parts)
     args.output.write_text(output_text, encoding="utf-8")
-    SCRIPT_LOGGER.info(
-        "Audit completed", extra={"trace_id": TRACE_ID, "report": str(args.output)}
-    )
+    SCRIPT_LOGGER.info("Audit completed", extra={"trace_id": TRACE_ID, "report": str(args.output)})
     print(f"\n\nAudit report written to {args.output}")
 
 
 if __name__ == "__main__":
-    main() 
\ No newline at end of file
+    main()
diff --git a/scripts/cleanup_repo.sh b/scripts/cleanup_repo.sh
deleted file mode 100755
index 1fc2454..0000000
--- a/scripts/cleanup_repo.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-# Repository cleanup script
-# - Prunes stale remote tracking branches
-# - Deletes local branches already merged into main
-# - Removes ignored files like caches
-set -e
-
-git fetch --all --prune
-
-for branch in $(git branch --merged | grep -v "^*" | grep -v main); do
-  git branch -d "$branch"
-done
-
-git clean -fdX
-
-echo "Cleanup complete."
diff --git a/scripts/integration_audit.py b/scripts/integration_audit.py
index 866b4d6..7ffa80b 100644
--- a/scripts/integration_audit.py
+++ b/scripts/integration_audit.py
@@ -5,7 +5,6 @@
 """
 
 import sys
-import os
 import traceback
 from pathlib import Path
 
@@ -13,11 +12,13 @@
 project_root = Path(__file__).parent.parent
 sys.path.insert(0, str(project_root))
 
+
 def audit_section(title: str):
     """Print audit section header"""
-    print(f"\n{'='*60}")
+    print(f"\n{'=' * 60}")
     print(f"🔍 {title}")
-    print('='*60)
+    print("=" * 60)
+
 
 def check_import(module_name: str, description: str = ""):
     """Check if a module can be imported"""
@@ -29,204 +30,211 @@ def check_import(module_name: str, description: str = ""):
         print(f"❌ {module_name} - {description}: {str(e)[:100]}")
         return False
 
+
 def check_function(module_name: str, function_name: str, description: str = ""):
     """Check if a function exists in a module"""
     try:
         module = __import__(module_name, fromlist=[function_name])
-        func = getattr(module, function_name)
+        getattr(module, function_name)
         print(f"✅ {module_name}.{function_name} - {description}")
         return True
     except Exception as e:
         print(f"❌ {module_name}.{function_name} - {description}: {str(e)[:100]}")
         return False
 
+
 def check_class_method(module_name: str, class_name: str, method_name: str, description: str = ""):
     """Check if a class method exists"""
     try:
         module = __import__(module_name, fromlist=[class_name])
         cls = getattr(module, class_name)
-        method = getattr(cls, method_name)
+        getattr(cls, method_name)
         print(f"✅ {module_name}.{class_name}.{method_name} - {description}")
         return True
     except Exception as e:
         print(f"❌ {module_name}.{class_name}.{method_name} - {description}: {str(e)[:100]}")
         return False
 
+
 def main():
     """Run comprehensive integration audit"""
     print("🚀 WhisperForge Integration Audit")
     print("Verifying all components work together seamlessly...")
-    
+
     # Track results
     results = {
-        'core_imports': 0,
-        'database_functions': 0,
-        'ui_components': 0,
-        'file_processing': 0,
-        'content_generation': 0,
-        'streaming_pipeline': 0,
-        'authentication': 0
+        "core_imports": 0,
+        "database_functions": 0,
+        "ui_components": 0,
+        "file_processing": 0,
+        "content_generation": 0,
+        "streaming_pipeline": 0,
+        "authentication": 0,
     }
-    
+
     # 1. Core Module Imports
     audit_section("Core Module Imports")
     core_modules = [
-        ('core.supabase_integration', 'Database integration'),
-        ('core.file_upload', 'File upload management'),
-        ('core.streaming_pipeline', 'Streaming pipeline'),
-        ('core.content_generation', 'Content generation'),
-        ('core.streaming_results', 'Results display'),
-        ('core.ui_components', 'UI components'),
-        ('core.styling', 'Aurora styling'),
-        ('core.logging_config', 'Enhanced logging'),
-        ('core.monitoring', 'System monitoring'),
-        ('core.notifications', 'User notifications'),
-        ('core.visible_thinking', 'Thinking display'),
-        ('core.research_enrichment', 'Research features'),
-        ('core.utils', 'Utility functions'),
-        ('core.config', 'Configuration'),
-        ('core.integrations', 'External integrations')
+        ("core.supabase_integration", "Database integration"),
+        ("core.file_upload", "File upload management"),
+        ("core.streaming_pipeline", "Streaming pipeline"),
+        ("core.content_generation", "Content generation"),
+        ("core.streaming_results", "Results display"),
+        ("core.ui_components", "UI components"),
+        ("core.styling", "Aurora styling"),
+        ("core.logging_config", "Enhanced logging"),
+        ("core.monitoring", "System monitoring"),
+        ("core.notifications", "User notifications"),
+        ("core.visible_thinking", "Thinking display"),
+        ("core.research_enrichment", "Research features"),
+        ("core.utils", "Utility functions"),
+        ("core.config", "Configuration"),
+        ("core.integrations", "External integrations"),
     ]
-    
+
     for module, desc in core_modules:
         if check_import(module, desc):
-            results['core_imports'] += 1
-    
+            results["core_imports"] += 1
+
     # 2. Database Functions
     audit_section("Database Integration")
     db_functions = [
-        ('core.supabase_integration', 'get_supabase_client', 'Get database client'),
-        ('core.supabase_integration', 'SupabaseClient', 'Database client class'),
-        ('app', 'init_supabase', 'Initialize database'),
-        ('app', 'authenticate_user', 'User authentication'),
-        ('app', 'register_user_supabase', 'User registration'),
-        ('app', 'save_generated_content_supabase', 'Save content'),
-        ('app', 'get_user_content_history_supabase', 'Get user history')
+        ("core.supabase_integration", "get_supabase_client", "Get database client"),
+        ("core.supabase_integration", "SupabaseClient", "Database client class"),
+        ("app", "init_supabase", "Initialize database"),
+        ("app", "authenticate_user", "User authentication"),
+        ("app", "register_user_supabase", "User registration"),
+        ("app", "save_generated_content_supabase", "Save content"),
+        ("app", "get_user_content_history_supabase", "Get user history"),
     ]
-    
+
     for module, func, desc in db_functions:
         if check_function(module, func, desc):
-            results['database_functions'] += 1
-    
+            results["database_functions"] += 1
+
     # 3. File Processing
     audit_section("File Processing & Upload")
     file_functions = [
-        ('core.file_upload', 'LargeFileUploadManager', 'Large file manager'),
-        ('core.file_upload', 'FileUploadManager', 'Standard file manager')
+        ("core.file_upload", "LargeFileUploadManager", "Large file manager"),
+        ("core.file_upload", "FileUploadManager", "Standard file manager"),
     ]
-    
+
     for module, cls, desc in file_functions:
         if check_function(module, cls, desc):
-            results['file_processing'] += 1
-    
+            results["file_processing"] += 1
+
     # Check LargeFileUploadManager methods
     file_methods = [
-        ('core.file_upload', 'LargeFileUploadManager', 'validate_large_file', 'File validation'),
-        ('core.file_upload', 'LargeFileUploadManager', 'create_large_file_upload_zone', 'Upload UI'),
-        ('core.file_upload', 'LargeFileUploadManager', 'process_large_file', 'File processing')
+        ("core.file_upload", "LargeFileUploadManager", "validate_large_file", "File validation"),
+        ("core.file_upload", "LargeFileUploadManager", "create_large_file_upload_zone", "Upload UI"),
+        ("core.file_upload", "LargeFileUploadManager", "process_large_file", "File processing"),
     ]
-    
+
     for module, cls, method, desc in file_methods:
         if check_class_method(module, cls, method, desc):
-            results['file_processing'] += 1
-    
+            results["file_processing"] += 1
+
     # 4. Content Generation
     audit_section("Content Generation")
     content_functions = [
-        ('core.content_generation', 'generate_wisdom_extraction', 'Wisdom extraction'),
-        ('core.content_generation', 'generate_research_enrichment', 'Research enrichment'),
-        ('core.content_generation', 'generate_outline_creation', 'Outline creation'),
-        ('core.content_generation', 'generate_article_creation', 'Article creation'),
-        ('core.content_generation', 'generate_social_content', 'Social media content'),
-        ('core.content_generation', 'generate_image_prompts', 'Image prompts')
+        ("core.content_generation", "generate_wisdom_extraction", "Wisdom extraction"),
+        ("core.content_generation", "generate_research_enrichment", "Research enrichment"),
+        ("core.content_generation", "generate_outline_creation", "Outline creation"),
+        ("core.content_generation", "generate_article_creation", "Article creation"),
+        ("core.content_generation", "generate_social_content", "Social media content"),
+        ("core.content_generation", "generate_image_prompts", "Image prompts"),
     ]
-    
+
     for module, func, desc in content_functions:
         if check_function(module, func, desc):
-            results['content_generation'] += 1
-    
+            results["content_generation"] += 1
+
     # 5. Streaming Pipeline
     audit_section("Streaming Pipeline")
     pipeline_functions = [
-        ('core.streaming_pipeline', 'get_pipeline_controller', 'Pipeline controller'),
-        ('core.streaming_pipeline', 'StreamingPipelineController', 'Pipeline class'),
-        ('core.streaming_results', 'show_streaming_results', 'Results display'),
-        ('core.streaming_results', 'show_real_time_content_stream', 'Real-time streaming')
+        ("core.streaming_pipeline", "get_pipeline_controller", "Pipeline controller"),
+        ("core.streaming_pipeline", "StreamingPipelineController", "Pipeline class"),
+        ("core.streaming_results", "show_streaming_results", "Results display"),
+        ("core.streaming_results", "show_real_time_content_stream", "Real-time streaming"),
     ]
-    
+
     for module, func, desc in pipeline_functions:
         if check_function(module, func, desc):
-            results['streaming_pipeline'] += 1
-    
+            results["streaming_pipeline"] += 1
+
     # 6. UI Components
     audit_section("UI Components & Styling")
     ui_functions = [
-        ('core.ui_components', 'load_aurora_css', 'Aurora CSS loading'),
-        ('core.ui_components', 'AuroraContainer', 'Aurora containers'),
-        ('core.ui_components', 'AuroraComponents', 'Aurora components'),
-        ('core.styling', 'apply_aurora_theme', 'Aurora theme'),
-        ('core.styling', 'create_aurora_header', 'Aurora header')
+        ("core.ui_components", "load_aurora_css", "Aurora CSS loading"),
+        ("core.ui_components", "AuroraContainer", "Aurora containers"),
+        ("core.ui_components", "AuroraComponents", "Aurora components"),
+        ("core.styling", "apply_aurora_theme", "Aurora theme"),
+        ("core.styling", "create_aurora_header", "Aurora header"),
     ]
-    
+
     for module, func, desc in ui_functions:
         if check_function(module, func, desc):
-            results['ui_components'] += 1
-    
+            results["ui_components"] += 1
+
     # 7. Authentication Flow
     audit_section("Authentication & OAuth")
     auth_functions = [
-        ('app', 'handle_oauth_callback', 'OAuth callback handling'),
-        ('app', 'show_auth_page', 'Authentication page'),
-        ('app', 'show_main_app', 'Main application')
+        ("app", "handle_oauth_callback", "OAuth callback handling"),
+        ("app", "show_auth_page", "Authentication page"),
+        ("app", "show_main_app", "Main application"),
     ]
-    
+
     for module, func, desc in auth_functions:
         if check_function(module, func, desc):
-            results['authentication'] += 1
-    
+            results["authentication"] += 1
+
     # 8. Test Database Connection
     audit_section("Live Database Connection Test")
     try:
         from core.supabase_integration import get_supabase_client
+
         client = get_supabase_client()
         if client and client.client:
             # Test basic query
-            result = client.client.table('users').select('id').limit(1).execute()
+            result = client.client.table("users").select("id").limit(1).execute()
             print("✅ Database connection successful")
             print(f"✅ Users table accessible ({len(result.data)} records found)")
-            results['database_functions'] += 2
+            results["database_functions"] += 2
         else:
             print("❌ Database client not available")
     except Exception as e:
         print(f"❌ Database connection failed: {str(e)[:100]}")
-    
+
     # 9. Test App Import
     audit_section("Main Application Import")
     try:
         import app
+
         print("✅ app.py imports successfully")
-        
+
         # Test key app functions
         db, success = app.init_supabase()
         print(f"✅ init_supabase works: {success}")
-        results['authentication'] += 2
-        
+        results["authentication"] += 2
+
     except Exception as e:
         print(f"❌ app.py import failed: {str(e)[:100]}")
         traceback.print_exc()
-    
+
     # Final Results
     audit_section("Integration Audit Results")
     total_checks = sum(results.values())
     max_possible = 50  # Approximate total checks
-    
-    print(f"📊 **Integration Health Score: {total_checks}/{max_possible} ({(total_checks/max_possible)*100:.1f}%)**")
+
+    print(
+        f"📊 **Integration Health Score: {total_checks}/{max_possible} ({(total_checks / max_possible) * 100:.1f}%)**"
+    )
     print()
     print("**Component Breakdown:**")
     for component, count in results.items():
         status = "✅" if count > 0 else "❌"
         print(f"{status} {component.replace('_', ' ').title()}: {count} checks passed")
-    
+
     print()
     if total_checks >= 40:
         print("🎉 **EXCELLENT**: All major components integrated successfully!")
@@ -237,9 +245,10 @@ def main():
     else:
         print("⚠️ **ISSUES DETECTED**: Major integration problems found")
         print("🛠️ **NEEDS ATTENTION**")
-    
+
     return total_checks >= 40
 
+
 if __name__ == "__main__":
     success = main()
-    sys.exit(0 if success else 1) 
\ No newline at end of file
+    sys.exit(0 if success else 1)
diff --git a/scripts/setup_test_env.sh b/scripts/setup_test_env.sh
deleted file mode 100755
index a0938f3..0000000
--- a/scripts/setup_test_env.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/bin/bash
-# WhisperForge Test Environment Setup Script
-# This script creates a Python virtual environment and installs
-# the required dependencies for running tests.
-
-set -e
-
-if [ ! -f requirements.txt ]; then
-  echo "Run this script from the project root where requirements.txt is located." >&2
-  exit 1
-fi
-
-python -m venv venv
-# shellcheck disable=SC1091
-source venv/bin/activate
-pip install -r requirements.txt
-
-echo "✅ Test environment ready. Activate it with 'source venv/bin/activate'"
diff --git a/scripts/test_monitoring.py b/scripts/test_monitoring.py
index f4280ce..fb997c4 100644
--- a/scripts/test_monitoring.py
+++ b/scripts/test_monitoring.py
@@ -7,50 +7,51 @@
 in production environment.
 """
 
+import json
 import sys
 import time
-import json
-import os
 import traceback
 from pathlib import Path
+
 import pytest
 
 # Skip these script-style tests when executed under pytest
-SKIP_IN_PYTEST = 'pytest' in sys.modules
+SKIP_IN_PYTEST = "pytest" in sys.modules
 
 # Add project root to path
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
+
 def test_structured_logging():
     """Test structured logging functionality"""
     if SKIP_IN_PYTEST:
         pytest.skip("monitoring script test")
     print("🔍 Testing Structured Logging...")  # pragma: allow-print
-    
+
     try:
-        from core.monitoring import structured_logger, set_trace_context, trace_operation
-        
+        from core.monitoring import set_trace_context, structured_logger, trace_operation
+
         # Test basic logging
         structured_logger.info("Test info message", test_component="monitoring")
         structured_logger.warning("Test warning message", test_component="monitoring")
         structured_logger.error("Test error message", test_component="monitoring")
-        
+
         # Test trace context
-        trace_id = set_trace_context(user_id="test_user", operation="test_operation")
+        set_trace_context(user_id="test_user", operation="test_operation")
         structured_logger.info("Message with trace context", test_data="trace_test")
-        
+
         # Test trace operation context manager
         with trace_operation("test_context_manager", user_id="test_user"):
             structured_logger.info("Message within trace operation")
-        
+
         # Test pipeline logging
         structured_logger.pipeline_start("test_pipeline", user_id="test_user")
         time.sleep(0.1)  # Simulate work
         structured_logger.pipeline_complete("test_pipeline", 0.1, success=True)
-        
+
         print("✅ Structured logging tests passed")  # pragma: allow-print
         return True
-        
+
     except Exception as e:
         print(f"❌ Structured logging test failed: {e}")  # pragma: allow-print
         traceback.print_exc()
@@ -62,34 +63,34 @@ def test_health_checks():
     if SKIP_IN_PYTEST:
         pytest.skip("monitoring script test")
     print("🔍 Testing Health Checks...")
-    
+
     try:
         from core.health_check import health_checker
-        
+
         # Test health status
         health_status = health_checker.get_health_status()
         print(f"   Health Status: {health_status.status}")
         print(f"   Uptime: {health_status.uptime_seconds:.2f}s")
-        
+
         # Test SLO metrics
         slo_metrics = health_checker.get_slo_metrics()
         print(f"   Error Rate: {slo_metrics.error_rate_5xx}%")
         print(f"   Response Time: {slo_metrics.median_response_time}ms")
         print(f"   Active Users: {slo_metrics.active_users_1h}")
-        
+
         # Test SLO violations
         violations = health_checker.check_slo_violations()
         print(f"   SLO Violations: {len(violations)}")
-        
+
         # Test metrics JSON export
         metrics_json = health_checker.get_metrics_json()
         metrics_data = json.loads(metrics_json)
         assert "health" in metrics_data
         assert "slo_metrics" in metrics_data
-        
+
         print("✅ Health check tests passed")
         return True
-        
+
     except Exception as e:
         print(f"❌ Health check test failed: {e}")
         traceback.print_exc()
@@ -101,35 +102,37 @@ def test_metrics_export():
     if SKIP_IN_PYTEST:
         pytest.skip("monitoring script test")
     print("🔍 Testing Metrics Export...")
-    
+
     try:
         from core.metrics_exporter import (
-            metrics_exporter, track_request, track_pipeline,
-            export_prometheus_metrics, export_json_metrics
+            export_json_metrics,
+            export_prometheus_metrics,
+            track_pipeline,
+            track_request,
         )
-        
+
         # Test request tracking
         track_request(0.5, 200, "GET", "/test")
         track_request(1.2, 500, "POST", "/api/test")
-        
+
         # Test pipeline tracking
         track_pipeline("test_pipeline", 2.5, True)
         track_pipeline("test_pipeline", 5.0, False)
-        
+
         # Test Prometheus export
         prometheus_metrics = export_prometheus_metrics()
         assert "whisperforge_http_requests_total" in prometheus_metrics
         assert "whisperforge_pipeline_success_total" in prometheus_metrics
-        
+
         # Test JSON export
         json_metrics = export_json_metrics()
         assert "counters" in json_metrics
         assert "gauges" in json_metrics
         assert "histograms" in json_metrics
-        
+
         print("✅ Metrics export tests passed")
         return True
-        
+
     except Exception as e:
         print(f"❌ Metrics export test failed: {e}")
         traceback.print_exc()
@@ -141,29 +144,22 @@ def test_error_tracking():
     if SKIP_IN_PYTEST:
         pytest.skip("monitoring script test")
     print("🔍 Testing Error Tracking...")
-    
+
     try:
         from core.monitoring import error_tracker
-        
+
         # Test exception capture
         try:
             raise ValueError("Test error for monitoring")
         except Exception as e:
-            error_tracker.capture_exception(e, {
-                "test_context": "monitoring_test",
-                "user_id": "test_user"
-            })
-        
+            error_tracker.capture_exception(e, {"test_context": "monitoring_test", "user_id": "test_user"})
+
         # Test message capture
-        error_tracker.capture_message(
-            "Test warning message",
-            level="warning",
-            context={"test": "monitoring"}
-        )
-        
+        error_tracker.capture_message("Test warning message", level="warning", context={"test": "monitoring"})
+
         print("✅ Error tracking tests passed")
         return True
-        
+
     except Exception as e:
         print(f"❌ Error tracking test failed: {e}")
         traceback.print_exc()
@@ -175,31 +171,29 @@ def test_performance_tracking():
     if SKIP_IN_PYTEST:
         pytest.skip("monitoring script test")
     print("🔍 Testing Performance Tracking...")
-    
+
     try:
-        from core.monitoring import performance_tracker, monitor_function
-        
+        from core.monitoring import monitor_function, performance_tracker
+
         # Test context manager
         with performance_tracker.track_operation("test_operation"):
             time.sleep(0.1)  # Simulate work
-        
+
         # Test decorator
         @monitor_function("test_function")
         def test_function():
             time.sleep(0.05)
             return "test_result"
-        
+
         result = test_function()
         assert result == "test_result"
-        
+
         # Test pipeline performance tracking
-        performance_tracker.track_pipeline_performance(
-            "test_pipeline", 1.5, True, file_size_mb=10
-        )
-        
+        performance_tracker.track_pipeline_performance("test_pipeline", 1.5, True, file_size_mb=10)
+
         print("✅ Performance tracking tests passed")
         return True
-        
+
     except Exception as e:
         print(f"❌ Performance tracking test failed: {e}")
         traceback.print_exc()
@@ -211,27 +205,25 @@ def test_streamlit_integration():
     if SKIP_IN_PYTEST:
         pytest.skip("monitoring script test")
     print("🔍 Testing Streamlit Integration...")
-    
+
     try:
-        from core.streamlit_monitoring import (
-            streamlit_monitor, streamlit_page, streamlit_component
-        )
-        
+        from core.streamlit_monitoring import streamlit_component, streamlit_page
+
         # Test decorators (without actual Streamlit context)
         @streamlit_page("test_page")
         def test_page_function():
             return "page_result"
-        
+
         @streamlit_component("test_component")
         def test_component_function():
             return "component_result"
-        
+
         # Note: These will work but won't have full Streamlit context
         # In actual usage, they would have access to st.session_state
-        
+
         print("✅ Streamlit integration tests passed")
         return True
-        
+
     except Exception as e:
         print(f"❌ Streamlit integration test failed: {e}")
         traceback.print_exc()
@@ -243,46 +235,46 @@ def test_log_file_creation():
     if SKIP_IN_PYTEST:
         pytest.skip("monitoring script test")
     print("🔍 Testing Log File Creation...")
-    
+
     try:
         from datetime import datetime
         from pathlib import Path
-        
+
         # Check logs directory
         logs_dir = Path("logs")
         if not logs_dir.exists():
             print("   Creating logs directory...")
             logs_dir.mkdir(exist_ok=True)
-        
+
         # Check for structured log file
-        today = datetime.now().strftime('%Y%m%d')
+        today = datetime.now().strftime("%Y%m%d")
         log_file = logs_dir / f"whisperforge_structured_{today}.jsonl"
-        
+
         if log_file.exists():
             print(f"   ✅ Log file exists: {log_file}")
-            
+
             # Check file content
-            with open(log_file, 'r') as f:
+            with open(log_file) as f:
                 lines = f.readlines()
                 if lines:
                     print(f"   ✅ Log file has {len(lines)} entries")
-                    
+
                     # Validate JSON format
                     try:
                         last_entry = json.loads(lines[-1].strip())
-                        print(f"   ✅ Last log entry is valid JSON")
-                        if 'timestamp' in last_entry and 'level' in last_entry:
-                            print(f"   ✅ Log entry has required fields")
+                        print("   ✅ Last log entry is valid JSON")
+                        if "timestamp" in last_entry and "level" in last_entry:
+                            print("   ✅ Log entry has required fields")
                     except json.JSONDecodeError:
-                        print(f"   ⚠️ Last log entry is not valid JSON")
+                        print("   ⚠️ Last log entry is not valid JSON")
                 else:
-                    print(f"   ⚠️ Log file is empty")
+                    print("   ⚠️ Log file is empty")
         else:
             print(f"   ⚠️ Log file not found: {log_file}")
-        
+
         print("✅ Log file creation tests completed")
         return True
-        
+
     except Exception as e:
         print(f"❌ Log file creation test failed: {e}")
         traceback.print_exc()
@@ -293,7 +285,7 @@ def run_comprehensive_test():
     """Run all monitoring tests"""
     print("🚀 Starting Comprehensive Monitoring Test Suite")
     print("=" * 60)
-    
+
     tests = [
         ("Structured Logging", test_structured_logging),
         ("Health Checks", test_health_checks),
@@ -303,9 +295,9 @@ def run_comprehensive_test():
         ("Streamlit Integration", test_streamlit_integration),
         ("Log File Creation", test_log_file_creation),
     ]
-    
+
     results = []
-    
+
     for test_name, test_func in tests:
         print(f"\n📋 Running {test_name} Test...")
         try:
@@ -314,15 +306,15 @@ def run_comprehensive_test():
         except Exception as e:
             print(f"❌ {test_name} test crashed: {e}")
             results.append((test_name, False))
-    
+
     # Summary
     print("\n" + "=" * 60)
     print("📊 Test Results Summary")
     print("=" * 60)
-    
+
     passed = 0
     failed = 0
-    
+
     for test_name, success in results:
         status = "✅ PASS" if success else "❌ FAIL"
         print(f"{status} {test_name}")
@@ -330,9 +322,9 @@ def run_comprehensive_test():
             passed += 1
         else:
             failed += 1
-    
+
     print(f"\n📈 Overall Results: {passed} passed, {failed} failed")
-    
+
     if failed == 0:
         print("🎉 All monitoring tests passed! System is ready for production.")
         return True
@@ -356,4 +348,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main() 
\ No newline at end of file
+    main()
diff --git a/scripts/test_oauth.py b/scripts/test_oauth.py
index 79b2691..69f1c5f 100644
--- a/scripts/test_oauth.py
+++ b/scripts/test_oauth.py
@@ -4,47 +4,48 @@
 """
 
 import sys
-import os
 from pathlib import Path
+
 import pytest
 from _pytest.outcomes import Skipped
 
 # Add the project root to path
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
+
 def test_oauth_url_generation():
     """Test that OAuth URL generation works"""
     print("🔍 Testing OAuth URL generation...")
-    
+
     # Load environment variables from .env file
     from dotenv import load_dotenv
+
     load_dotenv()
-    
+
     try:
         from core.supabase_integration import get_supabase_client
-        
+
         db = get_supabase_client()
-        
+
         if not db or not db.client:
             print("❌ Failed to get Supabase client")
             pytest.skip("Supabase client not available - check environment variables")
-        
+
         # Test OAuth URL generation
         redirect_url = "http://localhost:8501"
-        auth_response = db.client.auth.sign_in_with_oauth({
-            "provider": "google",
-            "options": {"redirect_to": redirect_url}
-        })
-        
-        if hasattr(auth_response, 'url') and auth_response.url:
-            print(f"✅ OAuth URL generated successfully")
+        auth_response = db.client.auth.sign_in_with_oauth(
+            {"provider": "google", "options": {"redirect_to": redirect_url}}
+        )
+
+        if hasattr(auth_response, "url") and auth_response.url:
+            print("✅ OAuth URL generated successfully")
             print(f"   URL: {auth_response.url[:50]}...")
             assert True
         else:
-            print(f"❌ OAuth URL not generated properly")
+            print("❌ OAuth URL not generated properly")
             print(f"   Response: {auth_response}")
-            assert False, "OAuth URL not generated properly"
-            
+            raise AssertionError("OAuth URL not generated properly")
+
     except Exception as e:
         print(f"❌ Error testing OAuth: {e}")
         if "Invalid API key" in str(e) or "SUPABASE_URL" in str(e) or "SUPABASE_ANON_KEY" in str(e):
@@ -52,13 +53,15 @@ def test_oauth_url_generation():
             pytest.skip("Supabase credentials not available for OAuth testing")
         else:
             import traceback
+
             print(f"   Full error: {traceback.format_exc()}")
             pytest.skip(f"OAuth test failed with error: {e}")
 
+
 def main():
     print("🚀 WhisperForge OAuth Test")
     print("=" * 30)
-    
+
     try:
         test_oauth_url_generation()
         print("\n🎉 OAuth URL generation is working!")
@@ -74,6 +77,7 @@ def main():
         print("Check your Supabase configuration.")
         return False
 
+
 if __name__ == "__main__":
     success = main()
-    sys.exit(0 if success else 1) 
\ No newline at end of file
+    sys.exit(0 if success else 1)
diff --git a/scripts/ui_ux_audit.py b/scripts/ui_ux_audit.py
index 485842e..7dd5589 100644
--- a/scripts/ui_ux_audit.py
+++ b/scripts/ui_ux_audit.py
@@ -5,18 +5,19 @@
 """
 
 import sys
-import os
 from pathlib import Path
 
 # Add project root to path
 project_root = Path(__file__).parent.parent
 sys.path.insert(0, str(project_root))
 
+
 def audit_section(title: str):
     """Print audit section header"""
-    print(f"\n{'='*60}")
+    print(f"\n{'=' * 60}")
     print(f"🎨 {title}")
-    print('='*60)
+    print("=" * 60)
+
 
 def check_ui_feature(feature_name: str, description: str, status: bool):
     """Check UI feature status"""
@@ -24,80 +25,82 @@ def check_ui_feature(feature_name: str, description: str, status: bool):
     print(f"{icon} {feature_name}: {description}")
     return status
 
+
 def main():
     """Run comprehensive UI/UX audit"""
     print("🎨 WhisperForge UI/UX Audit")
     print("Verifying OAuth, progress indicators, and user experience...")
-    
+
     # Track results
     results = {
-        'oauth_flow': 0,
-        'progress_indicators': 0,
-        'ui_components': 0,
-        'user_feedback': 0,
-        'error_handling': 0,
-        'accessibility': 0
+        "oauth_flow": 0,
+        "progress_indicators": 0,
+        "ui_components": 0,
+        "user_feedback": 0,
+        "error_handling": 0,
+        "accessibility": 0,
     }
-    
+
     # 1. OAuth Flow Audit
     audit_section("OAuth & Authentication Flow")
-    
+
     # Check OAuth implementation
     try:
         import app
-        
+
         # OAuth callback handling
         oauth_features = [
-            ("OAuth Callback Handler", "handle_oauth_callback function exists", hasattr(app, 'handle_oauth_callback')),
+            ("OAuth Callback Handler", "handle_oauth_callback function exists", hasattr(app, "handle_oauth_callback")),
             ("Google OAuth Integration", "Google sign-in with proper redirect", True),  # Verified in code
-            ("Fallback Authentication", "Email/password fallback available", hasattr(app, 'authenticate_user')),
-            ("User Registration", "Account creation flow", hasattr(app, 'register_user_supabase')),
+            ("Fallback Authentication", "Email/password fallback available", hasattr(app, "authenticate_user")),
+            ("User Registration", "Account creation flow", hasattr(app, "register_user_supabase")),
             ("Session Management", "Simple session state handling", True),  # Verified in code
             ("Local Testing Bypass", "Database unavailable bypass", True),  # Verified in code
             ("Error Recovery", "OAuth error handling with fallback", True),  # Verified in code
-            ("Beautiful Auth Page", "Aurora-themed authentication UI", True)  # Verified in code
+            ("Beautiful Auth Page", "Aurora-themed authentication UI", True),  # Verified in code
         ]
-        
+
         for feature, desc, status in oauth_features:
             if check_ui_feature(feature, desc, status):
-                results['oauth_flow'] += 1
-                
+                results["oauth_flow"] += 1
+
     except Exception as e:
         print(f"❌ OAuth audit failed: {e}")
-    
+
     # 2. Progress Indicators Audit
     audit_section("Progress Indicators & Status Updates")
-    
+
     try:
-        from core.streaming_results import show_streaming_results, show_real_time_content_stream
-        from core.streaming_pipeline import get_pipeline_controller
         from core.ui_components import AuroraComponents
-        
+
         progress_features = [
             ("Real-time Streaming", "Live content generation display", True),
             ("Step-by-step Progress", "Pipeline step indicators", True),
-            ("Aurora Progress Bars", "Beautiful animated progress bars", hasattr(AuroraComponents, 'aurora_progress_bar')),
+            (
+                "Aurora Progress Bars",
+                "Beautiful animated progress bars",
+                hasattr(AuroraComponents, "aurora_progress_bar"),
+            ),
             ("File Upload Progress", "Large file upload tracking", True),  # Verified in file_upload.py
             ("Chunk Processing", "Parallel chunk progress display", True),  # Verified in file_upload.py
             ("Status Messages", "Success/error/warning notifications", True),
             ("Loading States", "Processing indicators during operations", True),
-            ("Completion Feedback", "Clear completion status", True)
+            ("Completion Feedback", "Clear completion status", True),
         ]
-        
+
         for feature, desc, status in progress_features:
             if check_ui_feature(feature, desc, status):
-                results['progress_indicators'] += 1
-                
+                results["progress_indicators"] += 1
+
     except Exception as e:
         print(f"❌ Progress indicators audit failed: {e}")
-    
+
     # 3. UI Components Audit
     audit_section("UI Components & Visual Design")
-    
+
     try:
-        from core.ui_components import AuroraContainer, AuroraComponents
-        from core.styling import apply_aurora_theme, create_aurora_header
-        
+        from core.ui_components import AuroraComponents
+
         ui_features = [
             ("Aurora Theme", "Consistent bioluminescent design", True),
             ("Responsive Layout", "Mobile and desktop friendly", True),
@@ -108,19 +111,19 @@ def main():
             ("Form Design", "Beautiful input fields and validation", True),
             ("Card Components", "Elegant content containers", True),
             ("Navigation", "Intuitive page navigation", True),
-            ("Logo & Branding", "Professional WhisperForge identity", True)
+            ("Logo & Branding", "Professional WhisperForge identity", True),
         ]
-        
+
         for feature, desc, status in ui_features:
             if check_ui_feature(feature, desc, status):
-                results['ui_components'] += 1
-                
+                results["ui_components"] += 1
+
     except Exception as e:
         print(f"❌ UI components audit failed: {e}")
-    
+
     # 4. User Feedback & Interactions
     audit_section("User Feedback & Interactions")
-    
+
     try:
         feedback_features = [
             ("Success Messages", "Clear success confirmations", True),
@@ -132,43 +135,43 @@ def main():
             ("Form Validation", "Real-time input validation", True),
             ("Loading Spinners", "Activity indicators", True),
             ("Tooltips", "Helpful contextual hints", True),
-            ("Status Badges", "Clear state indicators", True)
+            ("Status Badges", "Clear state indicators", True),
         ]
-        
+
         for feature, desc, status in feedback_features:
             if check_ui_feature(feature, desc, status):
-                results['user_feedback'] += 1
-                
+                results["user_feedback"] += 1
+
     except Exception as e:
         print(f"❌ User feedback audit failed: {e}")
-    
+
     # 5. Error Handling & Recovery
     audit_section("Error Handling & Recovery")
-    
+
     try:
         from core.ui_components import ErrorBoundary
-        
+
         error_features = [
             ("Graceful Degradation", "App works without database", True),
-            ("Error Boundaries", "Component error isolation", hasattr(ErrorBoundary, 'wrap')),
+            ("Error Boundaries", "Component error isolation", hasattr(ErrorBoundary, "wrap")),
             ("Retry Mechanisms", "Automatic retry on failures", True),
             ("Fallback UI", "Alternative UI when features fail", True),
             ("User-friendly Errors", "Non-technical error messages", True),
             ("Recovery Actions", "Clear steps to resolve issues", True),
             ("Offline Handling", "Graceful offline behavior", True),
-            ("Timeout Handling", "Long operation timeouts", True)
+            ("Timeout Handling", "Long operation timeouts", True),
         ]
-        
+
         for feature, desc, status in error_features:
             if check_ui_feature(feature, desc, status):
-                results['error_handling'] += 1
-                
+                results["error_handling"] += 1
+
     except Exception as e:
         print(f"❌ Error handling audit failed: {e}")
-    
+
     # 6. Accessibility & Usability
     audit_section("Accessibility & Usability")
-    
+
     accessibility_features = [
         ("Keyboard Navigation", "Full keyboard accessibility", True),
         ("Screen Reader Support", "Semantic HTML structure", True),
@@ -179,16 +182,16 @@ def main():
         ("Touch Friendly", "Mobile touch targets", True),
         ("Loading States", "Clear loading indicators", True),
         ("Error Recovery", "Clear error resolution paths", True),
-        ("Intuitive Flow", "Logical user journey", True)
+        ("Intuitive Flow", "Logical user journey", True),
     ]
-    
+
     for feature, desc, status in accessibility_features:
         if check_ui_feature(feature, desc, status):
-            results['accessibility'] += 1
-    
+            results["accessibility"] += 1
+
     # 7. Specific OAuth Flow Test
     audit_section("OAuth Flow Deep Dive")
-    
+
     try:
         # Check OAuth URL generation
         oauth_deep_features = [
@@ -199,28 +202,28 @@ def main():
             ("User Creation", "Automatic user record creation", True),
             ("Session Setup", "Proper session state initialization", True),
             ("Error Fallback", "Email auth when OAuth fails", True),
-            ("Debug Information", "Helpful debug info in development", True)
+            ("Debug Information", "Helpful debug info in development", True),
         ]
-        
+
         for feature, desc, status in oauth_deep_features:
             if check_ui_feature(feature, desc, status):
-                results['oauth_flow'] += 1
-                
+                results["oauth_flow"] += 1
+
     except Exception as e:
         print(f"❌ OAuth deep dive failed: {e}")
-    
+
     # Final Results
     audit_section("UI/UX Audit Results")
     total_checks = sum(results.values())
     max_possible = 64  # Total possible checks
-    
-    print(f"🎨 **UI/UX Quality Score: {total_checks}/{max_possible} ({(total_checks/max_possible)*100:.1f}%)**")
+
+    print(f"🎨 **UI/UX Quality Score: {total_checks}/{max_possible} ({(total_checks / max_possible) * 100:.1f}%)**")
     print()
     print("**Component Breakdown:**")
     for component, count in results.items():
         status = "✅" if count > 0 else "❌"
         print(f"{status} {component.replace('_', ' ').title()}: {count} checks passed")
-    
+
     print()
     if total_checks >= 55:
         print("🎉 **EXCEPTIONAL**: UI/UX meets highest professional standards!")
@@ -234,7 +237,7 @@ def main():
     else:
         print("⚠️ **NEEDS IMPROVEMENT**: UX issues detected")
         print("🛠️ **REQUIRES ATTENTION**")
-    
+
     # Specific recommendations
     print("\n🎯 **Key Strengths:**")
     print("• Beautiful Aurora bioluminescent theme")
@@ -243,9 +246,10 @@ def main():
     print("• Large file upload with chunking progress")
     print("• Graceful error handling and recovery")
     print("• Professional visual design")
-    
+
     return total_checks >= 50
 
+
 if __name__ == "__main__":
     success = main()
-    sys.exit(0 if success else 1) 
\ No newline at end of file
+    sys.exit(0 if success else 1)
diff --git a/scripts/validate_app.py b/scripts/validate_app.py
index fe74f80..e420095 100644
--- a/scripts/validate_app.py
+++ b/scripts/validate_app.py
@@ -4,65 +4,66 @@
 Tests all critical functionality to prevent deployment errors
 """
 
-import sys
-import os
 import importlib
-import traceback
+import os
+import sys
 from pathlib import Path
 
 # Add the project root to path
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
+
 class ValidationResult:
     def __init__(self):
         self.tests_passed = 0
         self.tests_failed = 0
         self.errors = []
         self.warnings = []
-    
+
     def pass_test(self, test_name):
         self.tests_passed += 1
         print(f"✅ {test_name}")
-    
+
     def fail_test(self, test_name, error):
         self.tests_failed += 1
         self.errors.append(f"{test_name}: {error}")
         print(f"❌ {test_name}: {error}")
-    
+
     def warn_test(self, test_name, warning):
         self.warnings.append(f"{test_name}: {warning}")
         print(f"⚠️  {test_name}: {warning}")
-    
+
     def summary(self):
         total = self.tests_passed + self.tests_failed
-        print(f"\n{'='*50}")
-        print(f"VALIDATION SUMMARY")
-        print(f"{'='*50}")
+        print(f"\n{'=' * 50}")
+        print("VALIDATION SUMMARY")
+        print(f"{'=' * 50}")
         print(f"Tests Passed: {self.tests_passed}/{total}")
         print(f"Tests Failed: {self.tests_failed}")
         print(f"Warnings: {len(self.warnings)}")
-        
+
         if self.errors:
-            print(f"\n🚨 ERRORS:")
+            print("\n🚨 ERRORS:")
             for error in self.errors:
                 print(f"  - {error}")
-        
+
         if self.warnings:
-            print(f"\n⚠️  WARNINGS:")
+            print("\n⚠️  WARNINGS:")
             for warning in self.warnings:
                 print(f"  - {warning}")
-        
+
         if self.tests_failed == 0:
-            print(f"\n🎉 ALL TESTS PASSED! App is ready for deployment.")
+            print("\n🎉 ALL TESTS PASSED! App is ready for deployment.")
             return True
         else:
             print(f"\n💥 {self.tests_failed} TESTS FAILED! Fix errors before deployment.")
             return False
 
+
 def test_imports(result):
     """Test all critical imports"""
     print("\n🔍 Testing Imports...")
-    
+
     imports_to_test = [
         # Core imports
         ("streamlit", "st"),
@@ -72,13 +73,11 @@ def test_imports(result):
         ("datetime", "datetime"),
         ("tempfile", None),
         ("logging", None),
-        
         # Third-party
         ("dotenv", None),
         ("supabase", None),
         ("openai", None),
         ("anthropic", None),
-        
         # WhisperForge modules
         ("core.supabase_integration", None),
         ("core.utils", None),
@@ -86,7 +85,7 @@ def test_imports(result):
         ("core.streaming_pipeline", None),
         ("core.monitoring", None),
     ]
-    
+
     for module_name, alias in imports_to_test:
         try:
             if alias:
@@ -100,29 +99,22 @@ def test_imports(result):
         except Exception as e:
             result.fail_test(f"Import {module_name}", f"Unexpected error: {e}")
 
+
 def test_environment_variables(result):
     """Test required environment variables"""
     print("\n🔍 Testing Environment Variables...")
-    
-    required_vars = [
-        "SUPABASE_URL",
-        "SUPABASE_ANON_KEY"
-    ]
-    
-    optional_vars = [
-        "OPENAI_API_KEY",
-        "ANTHROPIC_API_KEY",
-        "OAUTH_REDIRECT_URL",
-        "STREAMLIT_APP_URL"
-    ]
-    
+
+    required_vars = ["SUPABASE_URL", "SUPABASE_ANON_KEY"]
+
+    optional_vars = ["OPENAI_API_KEY", "ANTHROPIC_API_KEY", "OAUTH_REDIRECT_URL", "STREAMLIT_APP_URL"]
+
     for var in required_vars:
         value = os.getenv(var)
         if value:
             result.pass_test(f"Required env var {var}")
         else:
             result.fail_test(f"Required env var {var}", "Not set")
-    
+
     for var in optional_vars:
         value = os.getenv(var)
         if value:
@@ -130,10 +122,11 @@ def test_environment_variables(result):
         else:
             result.warn_test(f"Optional env var {var}", "Not set")
 
+
 def test_file_structure(result):
     """Test required file structure"""
     print("\n🔍 Testing File Structure...")
-    
+
     required_files = [
         "app.py",
         "requirements.txt",
@@ -146,97 +139,95 @@ def test_file_structure(result):
         "prompts/default/wisdom_extraction.md",
         "prompts/default/outline_creation.md",
         "prompts/default/social_media.md",
-        "prompts/default/image_prompts.md"
-    ]
-    
-    optional_files = [
-        "static/css/whisperforge_ui.css",
-        "core/ui_components.py",
-        ".env",
-        "Procfile",
-        "runtime.txt"
+        "prompts/default/image_prompts.md",
     ]
-    
+
+    optional_files = ["static/css/whisperforge_ui.css", "core/ui_components.py", ".env", "Procfile", "runtime.txt"]
+
     for file_path in required_files:
         if Path(file_path).exists():
             result.pass_test(f"Required file {file_path}")
         else:
             result.fail_test(f"Required file {file_path}", "Missing")
-    
+
     for file_path in optional_files:
         if Path(file_path).exists():
             result.pass_test(f"Optional file {file_path}")
         else:
             result.warn_test(f"Optional file {file_path}", "Missing")
 
+
 def test_supabase_connection(result):
     """Test Supabase connection"""
     print("\n🔍 Testing Supabase Connection...")
-    
+
     try:
         from core.supabase_integration import get_supabase_client
+
         db, mcp = get_supabase_client()
-        
+
         if db:
             result.pass_test("Supabase client initialization")
-            
+
             # Test basic connection
             try:
                 # Simple health check
-                response = db.client.table("users").select("count", count="exact").limit(1).execute()
+                db.client.table("users").select("count", count="exact").limit(1).execute()
                 result.pass_test("Supabase database connection")
             except Exception as e:
                 result.fail_test("Supabase database connection", str(e))
         else:
             result.fail_test("Supabase client initialization", "Failed to create client")
-            
+
     except Exception as e:
         result.fail_test("Supabase import", str(e))
 
+
 def test_oauth_configuration(result):
     """Test OAuth configuration"""
     print("\n🔍 Testing OAuth Configuration...")
-    
+
     try:
         from core.supabase_integration import get_supabase_client
+
         db, _ = get_supabase_client()
-        
+
         if db:
             try:
                 # Test OAuth URL generation
                 redirect_url = os.getenv("OAUTH_REDIRECT_URL", "http://localhost:8501")
-                auth_response = db.client.auth.sign_in_with_oauth({
-                    "provider": "google",
-                    "options": {"redirect_to": redirect_url}
-                })
-                
-                if hasattr(auth_response, 'url') and auth_response.url:
+                auth_response = db.client.auth.sign_in_with_oauth(
+                    {"provider": "google", "options": {"redirect_to": redirect_url}}
+                )
+
+                if hasattr(auth_response, "url") and auth_response.url:
                     result.pass_test("OAuth URL generation")
                 else:
                     result.warn_test("OAuth URL generation", "No URL returned")
-                    
+
             except Exception as e:
                 result.warn_test("OAuth URL generation", str(e))
         else:
             result.fail_test("OAuth test", "No Supabase client")
-            
+
     except Exception as e:
         result.fail_test("OAuth test", str(e))
 
+
 def test_prompt_files(result):
     """Test prompt files are readable"""
     print("\n🔍 Testing Prompt Files...")
-    
+
     prompt_files = [
         "prompts/default/wisdom_extraction.md",
         "prompts/default/outline_creation.md",
         "prompts/default/social_media.md",
-        "prompts/default/image_prompts.md"
+        "prompts/default/image_prompts.md",
     ]
-    
+
     for prompt_file in prompt_files:
         try:
-            with open(prompt_file, 'r', encoding='utf-8') as f:
+            with open(prompt_file, encoding="utf-8") as f:
                 content = f.read()
                 if len(content) > 10:  # Basic content check
                     result.pass_test(f"Prompt file {prompt_file}")
@@ -247,87 +238,93 @@ def test_prompt_files(result):
         except Exception as e:
             result.fail_test(f"Prompt file {prompt_file}", str(e))
 
+
 def test_ai_providers(result):
     """Test AI provider configurations"""
     print("\n🔍 Testing AI Providers...")
-    
+
     # Test OpenAI
     openai_key = os.getenv("OPENAI_API_KEY")
     if openai_key:
         try:
             import openai
+
             # Basic client test (don't make actual API calls in validation)
-            client = openai.OpenAI(api_key=openai_key)
+            openai.OpenAI(api_key=openai_key)
             result.pass_test("OpenAI configuration")
         except Exception as e:
             result.warn_test("OpenAI configuration", str(e))
     else:
         result.warn_test("OpenAI configuration", "API key not set")
-    
+
     # Test Anthropic
     anthropic_key = os.getenv("ANTHROPIC_API_KEY")
     if anthropic_key:
         try:
             import anthropic
+
             # Basic client test (don't make actual API calls in validation)
-            client = anthropic.Anthropic(api_key=anthropic_key)
+            anthropic.Anthropic(api_key=anthropic_key)
             result.pass_test("Anthropic configuration")
         except Exception as e:
             result.warn_test("Anthropic configuration", str(e))
     else:
         result.warn_test("Anthropic configuration", "API key not set")
 
+
 def test_streamlit_compatibility(result):
     """Test Streamlit compatibility"""
     print("\n🔍 Testing Streamlit Compatibility...")
-    
+
     try:
         import streamlit as st
+
         result.pass_test("Streamlit import")
-        
+
         # Check version compatibility
         try:
             version = st.__version__
-            major, minor = map(int, version.split('.')[:2])
+            major, minor = map(int, version.split(".")[:2])
             if major >= 1 and minor >= 28:
                 result.pass_test("Streamlit version compatibility")
             else:
                 result.warn_test("Streamlit version", f"Version {version} may be outdated")
-        except:
+        except (ValueError, AttributeError):
             result.warn_test("Streamlit version", "Could not check version")
-            
+
     except Exception as e:
         result.fail_test("Streamlit import", str(e))
 
+
 def test_pipeline_components(result):
     """Test pipeline components"""
     print("\n🔍 Testing Pipeline Components...")
-    
+
     try:
         from core.streaming_pipeline import get_pipeline_controller
-        
+
         # Test pipeline initialization
         controller = get_pipeline_controller()
         if controller:
             result.pass_test("Pipeline controller initialization")
         else:
             result.fail_test("Pipeline controller initialization", "Failed to create controller")
-            
+
     except Exception as e:
         result.fail_test("Pipeline controller", str(e))
-    
+
     try:
-        from core.content_generation import transcribe_audio
         result.pass_test("Content generation import")
     except Exception as e:
         result.fail_test("Content generation import", str(e))
 
+
 def main():
     print("🚀 WhisperForge App Validation")
     print("=" * 50)
-    
+
     result = ValidationResult()
-    
+
     # Run all tests
     test_imports(result)
     test_environment_variables(result)
@@ -338,12 +335,13 @@ def main():
     test_ai_providers(result)
     test_streamlit_compatibility(result)
     test_pipeline_components(result)
-    
+
     # Print summary
     success = result.summary()
-    
+
     # Exit with appropriate code
     sys.exit(0 if success else 1)
 
+
 if __name__ == "__main__":
-    main() 
\ No newline at end of file
+    main()
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..124e864
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,115 @@
+"""WhisperForge environment setup - creates a virtual environment and installs dependencies."""
+
+import os
+import subprocess
+import sys
+import venv
+
+PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
+VENV_DIR = os.path.join(PROJECT_DIR, ".venv")
+REQUIREMENTS = os.path.join(PROJECT_DIR, "requirements.txt")
+REQUIREMENTS_DEV = os.path.join(PROJECT_DIR, "requirements-dev.txt")
+
+if sys.platform == "win32":
+    PYTHON = os.path.join(VENV_DIR, "Scripts", "python")
+    PIP = os.path.join(VENV_DIR, "Scripts", "pip")
+    PRE_COMMIT = os.path.join(VENV_DIR, "Scripts", "pre-commit")
+else:
+    PYTHON = os.path.join(VENV_DIR, "bin", "python")
+    PIP = os.path.join(VENV_DIR, "bin", "pip")
+    PRE_COMMIT = os.path.join(VENV_DIR, "bin", "pre-commit")
+
+
+def print_step(msg):
+    print(f"\n  >> {msg}")
+
+
+def create_venv():
+    """Create a fresh virtual environment."""
+    if os.path.exists(VENV_DIR):
+        print_step(f"Removing existing venv at {VENV_DIR}")
+        import shutil
+
+        shutil.rmtree(VENV_DIR)
+
+    print_step(f"Creating virtual environment in {VENV_DIR}")
+    venv.create(VENV_DIR, with_pip=True)
+
+    if not os.path.exists(PYTHON):
+        print("  !! Failed to create virtual environment.")
+        sys.exit(1)
+
+    print("     Done.")
+
+
+def install_requirements(dev=False):
+    """Install project dependencies from requirements.txt (and dev deps if requested)."""
+    if not os.path.exists(REQUIREMENTS):
+        print("  !! requirements.txt not found, skipping dependency install.")
+        return
+
+    print_step("Upgrading pip")
+    subprocess.check_call(
+        [PYTHON, "-m", "pip", "install", "--upgrade", "pip"],
+        stdout=subprocess.DEVNULL,
+    )
+
+    if dev:
+        req_file = REQUIREMENTS_DEV
+        if not os.path.exists(req_file):
+            print("  !! requirements-dev.txt not found, falling back to requirements.txt")
+            req_file = REQUIREMENTS
+    else:
+        req_file = REQUIREMENTS
+
+    print_step(f"Installing dependencies from {os.path.basename(req_file)}")
+    subprocess.check_call([PIP, "install", "-r", req_file])
+    print("\n     All dependencies installed.")
+
+
+def install_pre_commit_hooks():
+    """Install pre-commit git hooks into the repository."""
+    print_step("Installing pre-commit hooks")
+    subprocess.check_call([PRE_COMMIT, "install"], cwd=PROJECT_DIR)
+    print("     Hooks installed.")
+
+
+def verify_install():
+    """Quick sanity check that key packages are importable."""
+    print_step("Verifying installation")
+    result = subprocess.run(
+        [PYTHON, "-c", "import streamlit; print(f'  streamlit {streamlit.__version__}')"],
+        capture_output=True,
+        text=True,
+    )
+    if result.returncode == 0:
+        print(result.stdout.strip())
+        print("     Verification passed.")
+    else:
+        print("  !! Verification failed - streamlit could not be imported.")
+        print(result.stderr.strip())
+        sys.exit(1)
+
+
+def run(dev=False):
+    print("=" * 50)
+    print("  WhisperForge - Environment Setup")
+    if dev:
+        print("  (dev mode)")
+    print("=" * 50)
+
+    create_venv()
+    install_requirements(dev=dev)
+    verify_install()
+
+    if dev:
+        install_pre_commit_hooks()
+
+    print("\n" + "=" * 50)
+    print("  Setup complete. Run main.py to start the app.")
+    print("=" * 50 + "\n")
+
+
+if __name__ == "__main__":
+    dev = "--dev" in sys.argv
+    run(dev=dev)
diff --git a/shared/__init__.py b/shared/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/shared/config.py b/shared/config.py
deleted file mode 100644
index 22b4d63..0000000
--- a/shared/config.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import os
-import logging
-from dotenv import load_dotenv
-
-load_dotenv()
-
-logger = logging.getLogger(__name__)
-
-class Config:
-    # API Keys
-    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-    NOTION_API_KEY = os.getenv("NOTION_API_KEY")
-    SERVICE_TOKEN = os.getenv("SERVICE_TOKEN")
-    
-    # Log configuration status
-    logger.info("=== Configuration Status ===")
-    logger.info(f"OpenAI API Key present: {'Yes' if OPENAI_API_KEY else 'No'}")
-    logger.info(f"Claude API Key present: {'Yes' if CLAUDE_API_KEY else 'No'}")
-    logger.info(f"Notion API Key present: {'Yes' if NOTION_API_KEY else 'No'}")
-    logger.info(f"Service Token present: {'Yes' if SERVICE_TOKEN else 'No'}")
-    
-    # Service URLs
-    TRANSCRIPTION_SERVICE_URL = 'http://transcription:8000'
-    PROCESSING_SERVICE_URL = 'http://processing:8000'
-    STORAGE_SERVICE_URL = 'http://storage:8000'
-    
-    # Cache settings
-    CACHE_DIR = '/app/cache'
-    
-    # Model settings
-    WHISPER_MODEL = 'whisper-1'
-    GPT_MODEL = 'gpt-4'
diff --git a/shared/security.py b/shared/security.py
deleted file mode 100644
index aba285e..0000000
--- a/shared/security.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from fastapi import Security, HTTPException
-from fastapi.security import HTTPBearer
-from jose import jwt
-from datetime import datetime, timedelta
-from passlib.context import CryptContext
-import os
-
-pwd_context = CryptContext(schemes=["bcrypt"])
-security = HTTPBearer()
-
-def hash_password(password: str) -> str:
-    return pwd_context.hash(password)
-
-def verify_password(plain_password: str, hashed_password: str) -> bool:
-    return pwd_context.verify(plain_password, hashed_password)
-
-def create_jwt_token(data: dict) -> str:
-    to_encode = data.copy()
-    expire = datetime.utcnow() + timedelta(minutes=30)
-    to_encode.update({"exp": expire})
-    return jwt.encode(to_encode, os.getenv('JWT_SECRET'), "HS256")
-
-async def verify_token(token: str = Security(security)):
-    try:
-        payload = jwt.decode(token, os.getenv('JWT_SECRET'), "HS256")
-        return payload
-    except jwt.JWTError:
-        raise HTTPException(401, "Invalid token")
diff --git a/start_app.sh b/start_app.sh
deleted file mode 100755
index 5a27a4e..0000000
--- a/start_app.sh
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/bin/bash
-
-# WhisperForge Startup Script
-echo "🚀 Starting WhisperForge v3.0.0 with real Supabase credentials..."
-
-# Auto-load environment variables from .env if present
-if [ -f .env ]; then
-  set -a
-  # shellcheck disable=SC1091
-  source .env
-  set +a
-fi
-
-# Check for required environment variables
-if [ -z "$SUPABASE_URL" ] || [ -z "$SUPABASE_ANON_KEY" ]; then
-    echo "❌ Missing required environment variables:"
-    echo "   Please set SUPABASE_URL and SUPABASE_ANON_KEY before running this script."
-    echo "   Example:"
-    echo "   export SUPABASE_URL='<your-supabase-url>'"
-    echo "   export SUPABASE_ANON_KEY='<your-supabase-anon-key>'"
-    exit 1
-fi
-ENVIRONMENT="${1:-${ENVIRONMENT:-development}}"
-export ENVIRONMENT
-
-if [ "$ENVIRONMENT" = "production" ]; then
-  export DEBUG="${DEBUG:-false}"
-  export LOG_LEVEL="${LOG_LEVEL:-INFO}"
-else
-  export DEBUG="${DEBUG:-true}"
-  export LOG_LEVEL="${LOG_LEVEL:-DEBUG}"
-fi
-
-echo "Running in $ENVIRONMENT mode"
-
-echo "✅ Environment variables set"
-echo "🔗 Supabase URL: $SUPABASE_URL"
-echo "🔑 Supabase Key: ${SUPABASE_ANON_KEY:0:20}..."
-
-# Test Supabase connection
-echo "🧪 Testing Supabase connection..."
-python -c "from core.supabase_integration import get_supabase_client; client = get_supabase_client(); print('✅ Supabase connection successful!' if client.test_connection() else '❌ Connection failed')"
-
-# Start Streamlit app with correct file (app_simple.py is the main app)
-echo "🌐 Starting WhisperForge v3.0.0 on http://localhost:8501"
-echo "📝 Press Ctrl+C to stop the app"
-echo ""
-
-streamlit run app_simple.py --server.port 8501 --server.address 0.0.0.0 
diff --git a/static/README.md b/static/README.md
index 3b147fb..ea850d0 100644
--- a/static/README.md
+++ b/static/README.md
@@ -30,4 +30,4 @@ When adding new assets:
 1. Place CSS files in the `css` directory
 2. Place JavaScript files in the `js` directory
 3. Update the corresponding loader function in `app.py`
-4. Document the purpose of the asset in this README 
\ No newline at end of file
+4. Document the purpose of the asset in this README
diff --git a/static/css/aurora-progress.css b/static/css/aurora-progress.css
index 22bac5b..b991259 100644
--- a/static/css/aurora-progress.css
+++ b/static/css/aurora-progress.css
@@ -8,13 +8,13 @@
     --aurora-electric-blue: #7DF9FF;
     --aurora-spring-green: #00FF7F;
     --aurora-teal: #008B8B;
-    
+
     /* Aurora HSL Values for Dynamic Manipulation */
     --aurora-cyan-hsl: 180, 100%, 50%;
     --aurora-turquoise-hsl: 174, 72%, 56%;
     --aurora-electric-blue-hsl: 195, 100%, 78%;
     --aurora-spring-green-hsl: 150, 100%, 50%;
-    
+
     /* Sophisticated Glow Variables */
     --glow-radius-subtle: 8px;
     --glow-radius-medium: 16px;
@@ -22,7 +22,7 @@
     --glow-opacity-subtle: 0.3;
     --glow-opacity-medium: 0.6;
     --glow-opacity-strong: 0.9;
-    
+
     /* Animation Timing */
     --timing-organic: cubic-bezier(0.4, 0.0, 0.2, 1);
     --timing-elastic: cubic-bezier(0.68, -0.55, 0.265, 1.55);
@@ -44,13 +44,13 @@
     margin: 20px 0;
     position: relative;
     overflow: hidden;
-    
+
     /* Subtle Aurora Glow */
-    box-shadow: 
+    box-shadow:
         0 0 var(--glow-radius-subtle) rgba(var(--aurora-cyan-hsl), var(--glow-opacity-subtle)),
         inset 0 1px 0 rgba(255, 255, 255, 0.1),
         0 8px 32px rgba(0, 0, 0, 0.12);
-    
+
     transition: all 0.6s var(--timing-organic);
 }
 
@@ -74,7 +74,7 @@
 
 .aurora-progress-container:hover {
     border-color: rgba(0, 255, 255, 0.3);
-    box-shadow: 
+    box-shadow:
         0 0 var(--glow-radius-medium) rgba(var(--aurora-cyan-hsl), var(--glow-opacity-medium)),
         inset 0 1px 0 rgba(255, 255, 255, 0.2),
         0 12px 48px rgba(0, 0, 0, 0.16);
@@ -156,12 +156,12 @@
     border-radius: 6px;
     position: relative;
     transition: width 0.8s var(--timing-organic);
-    
+
     /* Living Glow Effect */
-    box-shadow: 
+    box-shadow:
         0 0 var(--glow-radius-subtle) currentColor,
         0 0 var(--glow-radius-medium) rgba(var(--aurora-cyan-hsl), 0.4);
-    
+
     animation: aurora-flow 2s ease-in-out infinite;
 }
 
@@ -214,7 +214,7 @@
 .aurora-progress-step.running {
     background: rgba(var(--aurora-cyan-hsl), 0.04);
     border: 1px solid rgba(var(--aurora-cyan-hsl), 0.2);
-    box-shadow: 
+    box-shadow:
         0 0 var(--glow-radius-subtle) rgba(var(--aurora-cyan-hsl), 0.2),
         inset 0 1px 0 rgba(255, 255, 255, 0.1);
 }
@@ -226,7 +226,7 @@
 .aurora-progress-step.completed {
     background: rgba(var(--aurora-spring-green-hsl), 0.04);
     border: 1px solid rgba(var(--aurora-spring-green-hsl), 0.2);
-    box-shadow: 
+    box-shadow:
         0 0 var(--glow-radius-subtle) rgba(var(--aurora-spring-green-hsl), 0.2),
         inset 0 1px 0 rgba(255, 255, 255, 0.1);
 }
@@ -234,7 +234,7 @@
 .aurora-progress-step.error {
     background: rgba(255, 107, 107, 0.04);
     border: 1px solid rgba(255, 107, 107, 0.2);
-    box-shadow: 
+    box-shadow:
         0 0 var(--glow-radius-subtle) rgba(255, 107, 107, 0.2),
         inset 0 1px 0 rgba(255, 255, 255, 0.1);
 }
@@ -277,7 +277,7 @@
 .aurora-progress-step.running .aurora-step-indicator {
     background: var(--aurora-cyan);
     color: #000;
-    box-shadow: 
+    box-shadow:
         0 0 var(--glow-radius-subtle) var(--aurora-cyan),
         0 0 var(--glow-radius-medium) rgba(var(--aurora-cyan-hsl), 0.4);
 }
@@ -289,7 +289,7 @@
 .aurora-progress-step.completed .aurora-step-indicator {
     background: var(--aurora-spring-green);
     color: #000;
-    box-shadow: 
+    box-shadow:
         0 0 var(--glow-radius-subtle) var(--aurora-spring-green),
         0 0 var(--glow-radius-medium) rgba(var(--aurora-spring-green-hsl), 0.4);
 }
@@ -361,12 +361,12 @@
 }
 
 @keyframes aurora-pulse {
-    0%, 100% { 
-        opacity: 0.6; 
+    0%, 100% {
+        opacity: 0.6;
         text-shadow: 0 0 var(--glow-radius-subtle) currentColor;
     }
-    50% { 
-        opacity: 1; 
+    50% {
+        opacity: 1;
         text-shadow: 0 0 var(--glow-radius-medium) currentColor;
     }
 }
@@ -385,33 +385,33 @@
 }
 
 @keyframes aurora-pulse-subtle {
-    0%, 100% { 
-        transform: scale(1); 
+    0%, 100% {
+        transform: scale(1);
         box-shadow: 0 0 var(--glow-radius-subtle) rgba(var(--aurora-cyan-hsl), var(--glow-opacity-subtle));
     }
-    50% { 
-        transform: scale(1.02); 
+    50% {
+        transform: scale(1.02);
         box-shadow: 0 0 var(--glow-radius-medium) rgba(var(--aurora-cyan-hsl), var(--glow-opacity-medium));
     }
 }
 
 @keyframes aurora-pulse-thinking {
-    0%, 100% { 
+    0%, 100% {
         border-color: rgba(var(--aurora-turquoise-hsl), 0.3);
         box-shadow: 0 0 var(--glow-radius-subtle) rgba(var(--aurora-turquoise-hsl), 0.3);
     }
-    50% { 
+    50% {
         border-color: rgba(var(--aurora-turquoise-hsl), 0.6);
         box-shadow: 0 0 var(--glow-radius-medium) rgba(var(--aurora-turquoise-hsl), 0.6);
     }
 }
 
 @keyframes aurora-pulse-responding {
-    0%, 100% { 
+    0%, 100% {
         border-color: rgba(var(--aurora-spring-green-hsl), 0.4);
         box-shadow: 0 0 var(--glow-radius-subtle) rgba(var(--aurora-spring-green-hsl), 0.4);
     }
-    50% { 
+    50% {
         border-color: rgba(var(--aurora-spring-green-hsl), 0.8);
         box-shadow: 0 0 var(--glow-radius-strong) rgba(var(--aurora-spring-green-hsl), 0.8);
     }
@@ -432,15 +432,15 @@
         padding: 20px;
         margin: 16px 0;
     }
-    
+
     .aurora-progress-header {
         margin-bottom: 16px;
     }
-    
+
     .aurora-progress-steps {
         gap: 10px;
     }
-    
+
     .aurora-progress-step {
         padding: 10px 12px;
         gap: 12px;
@@ -450,12 +450,12 @@
 /* High-DPI/Retina Optimizations */
 @media (-webkit-min-device-pixel-ratio: 2), (min-resolution: 192dpi) {
     .aurora-progress-fill {
-        box-shadow: 
+        box-shadow:
             0 0 calc(var(--glow-radius-subtle) * 0.8) currentColor,
             0 0 calc(var(--glow-radius-medium) * 0.8) rgba(var(--aurora-cyan-hsl), 0.4);
     }
-    
+
     .aurora-step-indicator {
         border-width: 0.5px;
     }
-} 
\ No newline at end of file
+}
diff --git a/static/css/diagram.css b/static/css/diagram.css
index b57e7af..f34ae2c 100644
--- a/static/css/diagram.css
+++ b/static/css/diagram.css
@@ -260,10 +260,10 @@ hr {
     left: 0;
     width: 100%;
     height: 2px;
-    background: linear-gradient(90deg, 
-        transparent, 
-        rgba(121, 40, 202, 0.5), 
-        rgba(255, 0, 128, 0.5), 
+    background: linear-gradient(90deg,
+        transparent,
+        rgba(121, 40, 202, 0.5),
+        rgba(255, 0, 128, 0.5),
         transparent
     );
     opacity: 0.3;
@@ -277,4 +277,4 @@ hr {
     10% { transform: translateY(100px); opacity: 0.5; }
     80% { transform: translateY(calc(100vh - 100px)); opacity: 0.5; }
     100% { transform: translateY(100vh); opacity: 0; }
-} 
\ No newline at end of file
+}
diff --git a/static/css/main.css b/static/css/main.css
index 675ccc0..10d36d7 100644
--- a/static/css/main.css
+++ b/static/css/main.css
@@ -7,34 +7,34 @@
     --aurora-secondary: #7DF9FF;      /* Electric Blue */
     --aurora-tertiary: #00FFFF;       /* Aqua */
     --aurora-accent: #20B2AA;         /* Light Sea Green */
-    
+
     /* Background Colors */
     --aurora-bg-dark: #0a0f1c;        /* Deep Navy */
     --aurora-bg-darker: #0d1421;      /* Darker Navy */
     --aurora-bg-card: rgba(64, 224, 208, 0.03);
     --aurora-bg-glass: rgba(64, 224, 208, 0.08);
-    
+
     /* Text Colors */
     --aurora-text: rgba(255, 255, 255, 0.95);
     --aurora-text-muted: rgba(255, 255, 255, 0.7);
     --aurora-text-dim: rgba(255, 255, 255, 0.5);
-    
+
     /* Border & Effects */
     --aurora-border: rgba(64, 224, 208, 0.2);
     --aurora-border-hover: rgba(64, 224, 208, 0.4);
     --aurora-border-active: rgba(64, 224, 208, 0.6);
-    
+
     /* Status Colors */
     --aurora-success: #00FF88;
     --aurora-warning: #FFB800;
     --aurora-error: #FF4444;
     --aurora-info: #40E0D0;
-    
+
     /* Glow Effects */
     --aurora-glow: 0 0 20px rgba(64, 224, 208, 0.3);
     --aurora-glow-strong: 0 0 30px rgba(64, 224, 208, 0.5);
     --aurora-glow-subtle: 0 0 10px rgba(64, 224, 208, 0.2);
-    
+
     /* Spacing & Layout */
     --aurora-radius: 12px;
     --aurora-radius-large: 16px;
@@ -42,12 +42,19 @@
     --aurora-spacing: 16px;
     --aurora-spacing-large: 24px;
     --aurora-spacing-small: 8px;
-    
+
     /* Typography */
     --aurora-font-primary: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
     --aurora-font-mono: 'SF Mono', Monaco, 'Cascadia Code', 'Roboto Mono', Consolas, 'Courier New', monospace;
 }
 
+/* Hide Streamlit default header bar */
+header[data-testid="stHeader"],
+.stAppHeader {
+    background: transparent !important;
+    backdrop-filter: none !important;
+}
+
 /* Global Styles */
 .stApp {
     background: linear-gradient(135deg, var(--aurora-bg-dark) 0%, var(--aurora-bg-darker) 100%);
@@ -133,12 +140,32 @@
     font-size: 0.9rem;
     font-weight: 500;
     transition: all 0.3s ease;
+    user-select: none;
 }
 
-.pipeline-badge:hover {
-    border-color: var(--aurora-border-hover);
-    box-shadow: var(--aurora-glow-subtle);
+/* Completed badge - results available, clickable */
+.pipeline-badge.badge-completed {
+    border-color: var(--aurora-primary);
+    background: rgba(64, 224, 208, 0.15);
+    color: var(--aurora-primary);
+    box-shadow: 0 0 8px rgba(64, 224, 208, 0.2);
+}
+
+.pipeline-badge.badge-completed:hover {
+    border-color: var(--aurora-secondary);
+    box-shadow: 0 0 16px rgba(64, 224, 208, 0.4);
     transform: translateY(-2px);
+    background: rgba(64, 224, 208, 0.25);
+}
+
+/* Pending badge - no results yet */
+.pipeline-badge.badge-pending {
+    opacity: 0.5;
+    border-color: rgba(64, 224, 208, 0.1);
+}
+
+.pipeline-badge.badge-pending:hover {
+    opacity: 0.6;
 }
 
 /* Completion Celebration */
@@ -237,8 +264,12 @@
 
 /* Text Inputs */
 .stTextInput > div > div > input,
-.stTextArea > div > div > textarea {
-    background: var(--aurora-bg-card) !important;
+.stTextArea > div > div > textarea,
+.stTextInput input,
+.stTextArea textarea,
+[data-testid="stTextInput"] input,
+[data-testid="stTextArea"] textarea {
+    background: var(--aurora-bg-darker) !important;
     border: 1px solid var(--aurora-border) !important;
     border-radius: var(--aurora-radius-small) !important;
     color: var(--aurora-text) !important;
@@ -246,19 +277,47 @@
 }
 
 .stTextInput > div > div > input:focus,
-.stTextArea > div > div > textarea:focus {
+.stTextArea > div > div > textarea:focus,
+.stTextInput input:focus,
+.stTextArea textarea:focus,
+[data-testid="stTextInput"] input:focus,
+[data-testid="stTextArea"] textarea:focus {
+    background: var(--aurora-bg-darker) !important;
     border-color: var(--aurora-border-hover) !important;
     box-shadow: var(--aurora-glow-subtle) !important;
 }
 
+/* Text area label styling */
+[data-testid="stTextArea"] label,
+[data-testid="stTextInput"] label {
+    color: var(--aurora-text) !important;
+}
+
 /* Select Boxes */
-.stSelectbox > div > div {
+.stSelectbox > div > div,
+[data-testid="stSelectbox"] > div > div {
     background: var(--aurora-bg-card) !important;
     border: 1px solid var(--aurora-border) !important;
     border-radius: var(--aurora-radius-small) !important;
     color: var(--aurora-text) !important;
 }
 
+/* Number Inputs */
+[data-testid="stNumberInput"] input,
+.stNumberInput input {
+    background: var(--aurora-bg-card) !important;
+    border: 1px solid var(--aurora-border) !important;
+    border-radius: var(--aurora-radius-small) !important;
+    color: var(--aurora-text) !important;
+    font-family: var(--aurora-font-primary) !important;
+}
+
+[data-testid="stNumberInput"] input:focus,
+.stNumberInput input:focus {
+    border-color: var(--aurora-border-hover) !important;
+    box-shadow: var(--aurora-glow-subtle) !important;
+}
+
 /* Progress Bars */
 .stProgress > div > div > div {
     background: linear-gradient(90deg, var(--aurora-primary), var(--aurora-secondary)) !important;
@@ -340,39 +399,39 @@ audio {
 
 /* Animations */
 @keyframes aurora-scan {
-    0%, 100% { 
-        left: -100%; 
-        opacity: 0; 
+    0%, 100% {
+        left: -100%;
+        opacity: 0;
     }
-    25% { 
-        opacity: 1; 
+    25% {
+        opacity: 1;
     }
-    75% { 
-        opacity: 1; 
+    75% {
+        opacity: 1;
     }
-    100% { 
-        left: 100%; 
-        opacity: 0; 
+    100% {
+        left: 100%;
+        opacity: 0;
     }
 }
 
 @keyframes aurora-pulse {
-    0%, 100% { 
-        opacity: 1; 
-        transform: scale(1); 
+    0%, 100% {
+        opacity: 1;
+        transform: scale(1);
     }
-    50% { 
-        opacity: 0.7; 
-        transform: scale(1.05); 
+    50% {
+        opacity: 0.7;
+        transform: scale(1.05);
     }
 }
 
 @keyframes aurora-glow {
-    0%, 100% { 
-        box-shadow: var(--aurora-glow-subtle); 
+    0%, 100% {
+        box-shadow: var(--aurora-glow-subtle);
     }
-    50% { 
-        box-shadow: var(--aurora-glow); 
+    50% {
+        box-shadow: var(--aurora-glow);
     }
 }
 
@@ -382,13 +441,13 @@ audio {
 }
 
 @keyframes fadeIn {
-    from { 
-        opacity: 0; 
-        transform: translateY(20px); 
+    from {
+        opacity: 0;
+        transform: translateY(20px);
     }
-    to { 
-        opacity: 1; 
-        transform: translateY(0); 
+    to {
+        opacity: 1;
+        transform: translateY(0);
     }
 }
 
@@ -397,24 +456,24 @@ audio {
     .main .block-container {
         padding: var(--aurora-spacing-small);
     }
-    
+
     .aurora-title {
         font-size: 2rem;
     }
-    
+
     .aurora-pipeline {
         gap: var(--aurora-spacing-small);
     }
-    
+
     .pipeline-badge {
         font-size: 0.8rem;
         padding: 6px 12px;
     }
-    
+
     .aurora-celebration-title {
         font-size: 2.5rem;
     }
-    
+
     .aurora-processing-title {
         font-size: 2rem;
     }
@@ -759,15 +818,15 @@ audio {
     .aurora-upload-method-selector {
         grid-template-columns: 1fr;
     }
-    
+
     .aurora-upload-method-icon {
         font-size: 2.5rem;
     }
-    
+
     .aurora-upload-icon {
         font-size: 3rem;
     }
-    
+
     .aurora-processing-metrics {
         grid-template-columns: 1fr 1fr;
     }
@@ -1060,28 +1119,28 @@ audio {
         overflow-x: auto;
         padding: 2px;
     }
-    
+
     .aurora-tab-button {
         padding: 10px 16px;
         font-size: 0.8rem;
         min-width: 120px;
     }
-    
+
     .aurora-tab-icon {
         font-size: 1rem;
     }
-    
+
     .aurora-tabs-actions {
         flex-direction: column;
         gap: var(--aurora-spacing-small);
         align-items: stretch;
     }
-    
+
     .aurora-tabs-quick-actions {
         justify-content: center;
         flex-wrap: wrap;
     }
-    
+
     .aurora-quick-action-btn {
         flex: 1;
         justify-content: center;
@@ -1199,7 +1258,7 @@ audio {
         flex-direction: column;
         gap: var(--aurora-spacing-small);
     }
-    
+
     .aurora-stat-item {
         width: 100%;
         flex-direction: row;
@@ -1207,35 +1266,60 @@ audio {
         text-align: left;
         min-width: auto;
     }
-    
+
     .aurora-stat-icon {
         margin-bottom: 0;
         margin-right: var(--aurora-spacing);
         font-size: 1.5rem;
     }
-    
+
     .aurora-stat-value {
         font-size: 1.3rem;
     }
-    
+
     .aurora-results-title {
         font-size: 1.8rem;
     }
-    
+
     .aurora-results-subtitle {
         font-size: 1rem;
     }
 }
 
-/* Override Streamlit Default Tab Styling */
-.stTabs {
-    display: none !important;
+/* Aurora-styled Streamlit Tabs */
+.stTabs [data-baseweb="tab-list"] {
+    gap: 8px;
+    border-bottom: 1px solid rgba(64, 224, 208, 0.2);
+    padding-bottom: 4px;
 }
 
-/* Ensure our custom tabs are visible */
-.aurora-tabs-container {
-    display: block !important;
-    visibility: visible !important;
+.stTabs [data-baseweb="tab"] {
+    background: rgba(64, 224, 208, 0.05);
+    border: 1px solid rgba(64, 224, 208, 0.15);
+    border-radius: 8px 8px 0 0;
+    color: rgba(255, 255, 255, 0.7);
+    padding: 8px 20px;
+    transition: all 0.3s ease;
+}
+
+.stTabs [data-baseweb="tab"]:hover {
+    background: rgba(64, 224, 208, 0.1);
+    border-color: rgba(64, 224, 208, 0.3);
+    color: #7DF9FF;
+}
+
+.stTabs [aria-selected="true"] {
+    background: rgba(64, 224, 208, 0.12) !important;
+    border-color: #40E0D0 !important;
+    color: #40E0D0 !important;
+}
+
+.stTabs [data-baseweb="tab-highlight"] {
+    background-color: #40E0D0 !important;
+}
+
+.stTabs [data-baseweb="tab-border"] {
+    display: none;
 }
 
 /* Enhanced Content Cards (moved from previous location) */
@@ -1470,19 +1554,19 @@ audio {
         gap: var(--aurora-spacing-small);
         align-items: flex-start;
     }
-    
+
     .aurora-content-card-actions {
         width: 100%;
         justify-content: flex-end;
     }
-    
+
     .aurora-content-card-body {
         padding: var(--aurora-spacing);
     }
-    
+
     .aurora-content-stats {
         flex-direction: column;
         gap: var(--aurora-spacing-small);
         align-items: flex-start;
     }
-} 
\ No newline at end of file
+}
diff --git a/static/css/production.css b/static/css/production.css
index 1939aa3..0da9a03 100644
--- a/static/css/production.css
+++ b/static/css/production.css
@@ -101,4 +101,4 @@
     color: var(--text-primary);
     border-bottom: 1px solid rgba(121, 40, 202, 0.1);
     padding-bottom: 10px;
-} 
\ No newline at end of file
+}
diff --git a/static/css/streaming.css b/static/css/streaming.css
new file mode 100644
index 0000000..81a8157
--- /dev/null
+++ b/static/css/streaming.css
@@ -0,0 +1,157 @@
+/* Aurora Progress Animation */
+@keyframes aurora-flow {
+    0%, 100% { left: -100%; opacity: 0; }
+    25% { opacity: 1; }
+    75% { opacity: 1; }
+    100% { left: 100%; opacity: 0; }
+}
+
+@keyframes aurora-pulse {
+    0%, 100% { opacity: 1; transform: scale(1); }
+    50% { opacity: 0.7; transform: scale(1.1); }
+}
+
+@keyframes completion-glow {
+    0%, 100% { left: -100%; opacity: 0; }
+    20% { opacity: 1; }
+    80% { opacity: 1; }
+    100% { left: 100%; opacity: 0; }
+}
+
+/* Enhanced Button Styling */
+.stButton > button {
+    background: linear-gradient(135deg, rgba(0, 255, 255, 0.1), rgba(64, 224, 208, 0.15)) !important;
+    border: 1px solid rgba(0, 255, 255, 0.2) !important;
+    color: rgba(255, 255, 255, 0.9) !important;
+    border-radius: 8px !important;
+    font-weight: 500 !important;
+    transition: all 0.3s ease !important;
+}
+
+.stButton > button:hover {
+    background: linear-gradient(135deg, rgba(0, 255, 255, 0.15), rgba(64, 224, 208, 0.2)) !important;
+    border-color: rgba(0, 255, 255, 0.3) !important;
+    color: white !important;
+    transform: translateY(-1px);
+    box-shadow: 0 4px 15px rgba(0, 255, 255, 0.15);
+}
+
+/* Expander Styling */
+.streamlit-expanderHeader {
+    background: rgba(0, 255, 255, 0.03) !important;
+    border: 1px solid rgba(0, 255, 255, 0.1) !important;
+    border-radius: 8px !important;
+}
+
+.streamlit-expanderContent {
+    background: rgba(0, 255, 255, 0.02) !important;
+    border: 1px solid rgba(0, 255, 255, 0.1) !important;
+    border-top: none !important;
+    border-radius: 0 0 8px 8px !important;
+}
+
+/* Aurora Streaming Results Styling */
+.aurora-content-card {
+    background: linear-gradient(135deg, rgba(0, 255, 255, 0.05), rgba(64, 224, 208, 0.08));
+    backdrop-filter: blur(24px) saturate(180%);
+    border: 1px solid rgba(0, 255, 255, 0.15);
+    border-radius: 16px;
+    padding: 24px;
+    margin: 16px 0;
+    position: relative;
+    overflow: hidden;
+    transition: all 0.4s cubic-bezier(0.4, 0.0, 0.2, 1);
+}
+
+.aurora-content-card::before {
+    content: "";
+    position: absolute;
+    top: 0;
+    left: -100%;
+    width: 100%;
+    height: 2px;
+    background: linear-gradient(90deg, transparent, #00FFFF, #40E0D0, transparent);
+    animation: aurora-scan 6s ease-in-out infinite;
+}
+
+.aurora-content-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 16px;
+    padding-bottom: 12px;
+    border-bottom: 1px solid rgba(0, 255, 255, 0.1);
+}
+
+.aurora-content-title {
+    font-size: 1.1rem;
+    font-weight: 600;
+    color: rgba(255, 255, 255, 0.95);
+}
+
+.aurora-status-badge {
+    padding: 4px 12px;
+    border-radius: 12px;
+    font-size: 0.75rem;
+    font-weight: 500;
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+}
+
+.aurora-status-badge.completed {
+    background: rgba(0, 255, 127, 0.15);
+    color: #00FF7F;
+    border: 1px solid rgba(0, 255, 127, 0.3);
+}
+
+.aurora-content-body {
+    color: rgba(255, 255, 255, 0.85);
+    line-height: 1.6;
+    font-size: 0.95rem;
+}
+
+.aurora-editor-section {
+    background: rgba(255, 255, 127, 0.05);
+    border: 1px solid rgba(255, 255, 127, 0.15);
+    border-radius: 12px;
+    padding: 16px;
+    margin: 16px 0;
+}
+
+.aurora-editor-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.aurora-editor-title {
+    font-weight: 600;
+    color: rgba(255, 255, 255, 0.95);
+}
+
+.aurora-editor-badge {
+    background: rgba(255, 255, 127, 0.15);
+    color: #FFFF7F;
+    padding: 4px 8px;
+    border-radius: 8px;
+    font-size: 0.7rem;
+    font-weight: 500;
+}
+
+.aurora-critique-card {
+    background: rgba(255, 255, 255, 0.02);
+    border-radius: 8px;
+    padding: 16px;
+    margin: 12px 0;
+}
+
+.aurora-critique-content {
+    color: rgba(255, 255, 255, 0.8);
+    line-height: 1.5;
+    font-size: 0.9rem;
+}
+
+@keyframes aurora-scan {
+    0%, 100% { left: -100%; }
+    50% { left: 100%; }
+}
diff --git a/static/css/upload.css b/static/css/upload.css
new file mode 100644
index 0000000..03751a0
--- /dev/null
+++ b/static/css/upload.css
@@ -0,0 +1,315 @@
+/* Upload Zone - FileUploadManager */
+.large-upload-zone-container {
+    margin: 20px 0;
+}
+
+.large-upload-zone {
+    border: 3px dashed rgba(0, 255, 255, 0.3);
+    border-radius: 16px;
+    padding: 50px 30px;
+    text-align: center;
+    background: linear-gradient(135deg,
+        rgba(0, 255, 255, 0.03) 0%,
+        rgba(64, 224, 208, 0.05) 100%);
+    transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
+    cursor: pointer;
+    position: relative;
+    overflow: hidden;
+}
+
+.large-upload-zone:hover {
+    border-color: rgba(0, 255, 255, 0.6);
+    background: linear-gradient(135deg,
+        rgba(0, 255, 255, 0.08) 0%,
+        rgba(64, 224, 208, 0.12) 100%);
+    transform: translateY(-3px);
+    box-shadow: 0 12px 35px rgba(0, 255, 255, 0.2);
+}
+
+.large-upload-zone::before {
+    content: "";
+    position: absolute;
+    top: 0;
+    left: -100%;
+    width: 100%;
+    height: 100%;
+    background: linear-gradient(90deg,
+        transparent,
+        rgba(0, 255, 255, 0.15),
+        transparent);
+    transition: left 0.6s ease;
+}
+
+.large-upload-zone:hover::before {
+    left: 100%;
+}
+
+.upload-icon-inner {
+    font-size: 64px;
+    opacity: 0.8;
+    transition: all 0.4s ease;
+    display: inline-block;
+}
+
+.large-upload-zone:hover .upload-icon-inner {
+    opacity: 1;
+    transform: scale(1.15) rotate(10deg);
+}
+
+.upload-text h3 {
+    color: #00FFFF;
+    font-size: 1.5rem;
+    margin: 16px 0 8px 0;
+    font-weight: 600;
+}
+
+.upload-text p {
+    color: rgba(255, 255, 255, 0.7);
+    margin: 0 0 20px 0;
+    font-size: 1rem;
+}
+
+.upload-info {
+    display: flex;
+    justify-content: center;
+    gap: 15px;
+    flex-wrap: wrap;
+    margin-bottom: 20px;
+}
+
+.supported-formats, .max-size, .chunk-info {
+    font-size: 0.85rem;
+    color: rgba(255, 255, 255, 0.6);
+    background: rgba(0, 255, 255, 0.1);
+    padding: 6px 12px;
+    border-radius: 6px;
+    border: 1px solid rgba(0, 255, 255, 0.2);
+}
+
+.upload-features {
+    display: flex;
+    justify-content: center;
+    gap: 30px;
+    flex-wrap: wrap;
+}
+
+.feature {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    color: rgba(255, 255, 255, 0.8);
+    font-size: 0.9rem;
+}
+
+.feature-icon {
+    font-size: 1.2rem;
+}
+
+/* Upload Progress Indicator */
+.upload-progress-container {
+    background: var(--bg-secondary);
+    border-radius: var(--card-radius);
+    padding: 15px;
+    margin: 10px 0;
+    border: 1px solid rgba(121, 40, 202, 0.2);
+}
+
+.upload-progress-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 8px;
+}
+
+.upload-filename {
+    color: var(--text-primary);
+    font-weight: 500;
+    font-size: 0.9rem;
+}
+
+.upload-percentage {
+    color: var(--accent-primary);
+    font-family: var(--terminal-font);
+    font-weight: 600;
+}
+
+.upload-progress-bar {
+    height: 4px;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 2px;
+    position: relative;
+    overflow: hidden;
+    margin-bottom: 8px;
+}
+
+.upload-progress-fill {
+    height: 100%;
+    background: linear-gradient(90deg, #7928CA, #FF0080);
+    border-radius: 2px;
+    transition: width 0.3s ease;
+}
+
+.upload-progress-shimmer {
+    position: absolute;
+    top: 0;
+    left: 0;
+    height: 100%;
+    background: linear-gradient(90deg,
+        transparent,
+        rgba(255, 255, 255, 0.2),
+        transparent);
+    animation: shimmer 1.5s ease-in-out infinite;
+}
+
+.upload-status {
+    color: var(--text-secondary);
+    font-size: 0.8rem;
+    text-align: center;
+}
+
+@keyframes shimmer {
+    0% { transform: translateX(-100%); }
+    100% { transform: translateX(100%); }
+}
+
+/* Enhanced Upload Zone - EnhancedLargeFileProcessor */
+.enhanced-upload-container {
+    margin: 25px 0;
+}
+
+.enhanced-upload-zone {
+    border: 3px dashed var(--aurora-border);
+    border-radius: var(--aurora-radius-large);
+    padding: var(--aurora-spacing-large);
+    text-align: center;
+    background: var(--aurora-bg-glass);
+    transition: all 0.5s cubic-bezier(0.4, 0, 0.2, 1);
+    cursor: pointer;
+    position: relative;
+    overflow: hidden;
+}
+
+.enhanced-upload-zone:hover {
+    border-color: var(--aurora-border-hover);
+    background: var(--aurora-bg-glass);
+    transform: translateY(-5px);
+    box-shadow: var(--aurora-glow);
+}
+
+.upload-icon-large {
+    position: relative;
+    margin-bottom: 20px;
+}
+
+.enhanced-upload-zone .upload-icon-inner {
+    font-size: 80px;
+    color: var(--aurora-primary);
+    opacity: 0.9;
+    transition: all 0.5s ease;
+    display: inline-block;
+    position: relative;
+    z-index: 2;
+}
+
+.upload-pulse {
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+    width: 120px;
+    height: 120px;
+    border: 2px solid var(--aurora-border);
+    border-radius: 50%;
+    animation: aurora-upload-pulse 3s ease-in-out infinite;
+}
+
+.enhanced-upload-zone:hover .upload-icon-inner {
+    transform: scale(1.2) rotate(15deg);
+    opacity: 1;
+}
+
+.upload-content h2 {
+    color: var(--aurora-primary);
+    font-size: 1.8rem;
+    margin: 0 0 8px 0;
+    font-weight: 700;
+}
+
+.upload-subtitle {
+    color: var(--aurora-text-muted);
+    margin: 0 0 25px 0;
+    font-size: 1.1rem;
+    font-weight: 500;
+}
+
+.upload-features-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(120px, 1fr));
+    gap: 15px;
+    margin: 25px 0;
+    max-width: 500px;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+.feature-card {
+    background: var(--aurora-bg-glass);
+    border: 1px solid var(--aurora-border);
+    border-radius: var(--aurora-radius);
+    padding: 12px 8px;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: 6px;
+    transition: all 0.3s ease;
+}
+
+.feature-card:hover {
+    background: var(--aurora-bg-glass);
+    border-color: var(--aurora-border-hover);
+    transform: translateY(-2px);
+    box-shadow: var(--aurora-glow-subtle);
+}
+
+.feature-card .feature-icon {
+    font-size: 1.5rem;
+    color: var(--aurora-primary);
+}
+
+.feature-text {
+    font-size: 0.85rem;
+    color: var(--aurora-text);
+    font-weight: 500;
+}
+
+.supported-formats-enhanced {
+    margin-top: 20px;
+    display: flex;
+    justify-content: center;
+    gap: 20px;
+    flex-wrap: wrap;
+}
+
+.format-group {
+    background: var(--aurora-bg-card);
+    border: 1px solid var(--aurora-border);
+    border-radius: var(--aurora-radius-small);
+    padding: 8px 12px;
+    font-size: 0.85rem;
+    color: var(--aurora-text-muted);
+}
+
+.format-group strong {
+    color: var(--aurora-primary);
+}
+
+@keyframes aurora-upload-pulse {
+    0%, 100% {
+        transform: translate(-50%, -50%) scale(1);
+        opacity: 0.6;
+    }
+    50% {
+        transform: translate(-50%, -50%) scale(1.2);
+        opacity: 0.3;
+    }
+}
diff --git a/static/css/whisperforge_ui.css b/static/css/whisperforge_ui.css
index 7ecb7d5..47030a7 100644
--- a/static/css/whisperforge_ui.css
+++ b/static/css/whisperforge_ui.css
@@ -293,12 +293,12 @@
         padding: 0.5rem;
         gap: 1rem;
     }
-    
+
     .auth-container {
         margin: 1rem;
         padding: 1.5rem;
     }
-    
+
     .content-section,
     .upload-section {
         padding: 1rem;
@@ -409,11 +409,11 @@ header { visibility: hidden; }
         background: white !important;
         color: black !important;
     }
-    
+
     .whisperforge-container,
     .auth-container,
     .main-container {
         box-shadow: none !important;
         border: 1px solid #ccc !important;
     }
-} 
\ No newline at end of file
+}
diff --git a/static/js/cookie-consent.js b/static/js/cookie-consent.js
index ec4aa84..c58a3e7 100644
--- a/static/js/cookie-consent.js
+++ b/static/js/cookie-consent.js
@@ -9,7 +9,7 @@ document.addEventListener('DOMContentLoaded', function() {
             cookieBanner.style.display = 'none';
         }
     }
-    
+
     // Set up event listener for the accept button
     const acceptButton = document.querySelector('.cookie-banner button');
     if (acceptButton) {
@@ -18,4 +18,4 @@ document.addEventListener('DOMContentLoaded', function() {
             document.querySelector('.cookie-banner').style.display = 'none';
         });
     }
-}); 
\ No newline at end of file
+});
diff --git a/static/js/ui-interactions.js b/static/js/ui-interactions.js
index 226a906..b73aefb 100644
--- a/static/js/ui-interactions.js
+++ b/static/js/ui-interactions.js
@@ -6,13 +6,13 @@ document.addEventListener('DOMContentLoaded', function() {
     const scannerLine = document.createElement('div');
     scannerLine.className = 'scanner-line';
     document.body.appendChild(scannerLine);
-    
+
     // Function to add toast notifications
     window.showToast = function(message, type = 'success') {
         const toast = document.createElement('div');
         toast.className = 'toast-notification';
         toast.textContent = message;
-        
+
         if (type === 'error') {
             toast.style.borderLeftColor = 'var(--error)';
         } else if (type === 'warning') {
@@ -20,29 +20,29 @@ document.addEventListener('DOMContentLoaded', function() {
         } else if (type === 'info') {
             toast.style.borderLeftColor = 'var(--info)';
         }
-        
+
         document.body.appendChild(toast);
-        
+
         // Remove toast after 3 seconds
         setTimeout(function() {
             toast.style.opacity = '0';
             toast.style.transform = 'translateX(100%)';
-            
+
             // Remove from DOM after animation
             setTimeout(function() {
                 document.body.removeChild(toast);
             }, 300);
         }, 3000);
     };
-    
+
     // Add active class to current navigation item
     const currentPage = new URLSearchParams(window.location.search).get('page') || 'home';
     const navItems = document.querySelectorAll('.nav-item');
-    
+
     navItems.forEach(function(item) {
         const itemPage = new URL(item.href).searchParams.get('page') || 'home';
         if (itemPage === currentPage) {
             item.classList.add('active');
         }
     });
-}); 
\ No newline at end of file
+});
diff --git a/tests/__init__.py b/tests/__init__.py
index df6b49b..d218072 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -1 +1 @@
-# WhisperForge Test Suite 
\ No newline at end of file
+# WhisperForge Test Suite
diff --git a/tests/conftest.py b/tests/conftest.py
index 8bcda22..2b4fa08 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -2,28 +2,31 @@
 Pytest configuration and fixtures for WhisperForge testing
 """
 
-import pytest
 import os
-import tempfile
 import shutil
+
+# Add project root to path
+import sys
+import tempfile
 from pathlib import Path
 from unittest.mock import Mock, patch
-import streamlit as st
+
+import pytest
 from streamlit.testing.v1 import AppTest
 
-# Add project root to path
-import sys
+from core.logging_config import logger
+from core.supabase_integration import get_supabase_client
+
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
 # Load environment variables from .env file if it exists
 try:
     from dotenv import load_dotenv
+
     load_dotenv()
 except ImportError:
     pass  # python-dotenv not installed, skip
 
-from core.logging_config import logger
-from core.supabase_integration import get_supabase_client
 
 @pytest.fixture(scope="session")
 def test_env():
@@ -31,21 +34,21 @@ def test_env():
     # Store original env vars
     original_env = {}
     test_vars = {
-        'SUPABASE_URL': os.getenv('SUPABASE_URL', 'https://test.supabase.co'),
-        'SUPABASE_ANON_KEY': os.getenv('SUPABASE_ANON_KEY', 'test-anon-key'),
-        'OPENAI_API_KEY': os.getenv('OPENAI_API_KEY', 'test-openai-key'),
-        'ANTHROPIC_API_KEY': os.getenv('ANTHROPIC_API_KEY', 'test-anthropic-key'),
-        'GROQ_API_KEY': os.getenv('GROQ_API_KEY', 'test-groq-key'),
-        'TESTING': 'true'
+        "SUPABASE_URL": os.getenv("SUPABASE_URL", "https://test.supabase.co"),
+        "SUPABASE_ANON_KEY": os.getenv("SUPABASE_ANON_KEY", "test-anon-key"),
+        "OPENAI_API_KEY": os.getenv("OPENAI_API_KEY", "test-openai-key"),
+        "ANTHROPIC_API_KEY": os.getenv("ANTHROPIC_API_KEY", "test-anthropic-key"),
+        "GROQ_API_KEY": os.getenv("GROQ_API_KEY", "test-groq-key"),
+        "TESTING": "true",
     }
-    
+
     # Set test environment
     for key, value in test_vars.items():
         original_env[key] = os.getenv(key)
         os.environ[key] = value
-    
+
     yield test_vars
-    
+
     # Restore original environment
     for key, value in original_env.items():
         if value is None:
@@ -53,6 +56,7 @@ def test_env():
         else:
             os.environ[key] = value
 
+
 @pytest.fixture
 def temp_dir():
     """Create temporary directory for test files"""
@@ -60,53 +64,59 @@ def temp_dir():
     yield Path(temp_path)
     shutil.rmtree(temp_path)
 
+
 @pytest.fixture
 def sample_audio_file(temp_dir):
     """Create a sample audio file for testing"""
     # Create a minimal WAV file (just headers, no actual audio)
     audio_file = temp_dir / "test_audio.wav"
-    
+
     # WAV file header (44 bytes)
-    wav_header = b'RIFF\x24\x08\x00\x00WAVEfmt \x10\x00\x00\x00\x01\x00\x02\x00\x44\xac\x00\x00\x10\xb1\x02\x00\x04\x00\x10\x00data\x00\x08\x00\x00'
-    
-    with open(audio_file, 'wb') as f:
+    wav_header = b"RIFF\x24\x08\x00\x00WAVEfmt \x10\x00\x00\x00\x01\x00\x02\x00\x44\xac\x00\x00\x10\xb1\x02\x00\x04\x00\x10\x00data\x00\x08\x00\x00"
+
+    with open(audio_file, "wb") as f:
         f.write(wav_header)
         # Add some dummy audio data
-        f.write(b'\x00' * 2048)
-    
+        f.write(b"\x00" * 2048)
+
     return audio_file
 
+
 @pytest.fixture
 def large_audio_file(temp_dir):
     """Create a large audio file for testing chunking"""
     audio_file = temp_dir / "large_test_audio.wav"
-    
+
     # WAV file header
-    wav_header = b'RIFF\x24\x08\x00\x00WAVEfmt \x10\x00\x00\x00\x01\x00\x02\x00\x44\xac\x00\x00\x10\xb1\x02\x00\x04\x00\x10\x00data\x00\x08\x00\x00'
-    
-    with open(audio_file, 'wb') as f:
+    wav_header = b"RIFF\x24\x08\x00\x00WAVEfmt \x10\x00\x00\x00\x01\x00\x02\x00\x44\xac\x00\x00\x10\xb1\x02\x00\x04\x00\x10\x00data\x00\x08\x00\x00"
+
+    with open(audio_file, "wb") as f:
         f.write(wav_header)
         # Create ~30MB file to trigger chunking
         chunk_size = 1024 * 1024  # 1MB chunks
         for _ in range(30):
-            f.write(b'\x00' * chunk_size)
-    
+            f.write(b"\x00" * chunk_size)
+
     return audio_file
 
+
 @pytest.fixture
 def mock_supabase():
     """Mock Supabase client for testing"""
-    with patch('core.supabase_client.create_client') as mock_create:
+    with patch("core.supabase_integration.create_client") as mock_create:
         mock_client = Mock()
         mock_create.return_value = mock_client
-        
+
         # Mock successful responses
-        mock_client.table.return_value.insert.return_value.execute.return_value.data = [{'id': 'test-id'}]
+        mock_client.table.return_value.insert.return_value.execute.return_value.data = [{"id": "test-id"}]
         mock_client.table.return_value.select.return_value.execute.return_value.data = []
-        mock_client.table.return_value.update.return_value.eq.return_value.execute.return_value.data = [{'id': 'test-id'}]
-        
+        mock_client.table.return_value.update.return_value.eq.return_value.execute.return_value.data = [
+            {"id": "test-id"}
+        ]
+
         yield mock_client
 
+
 @pytest.fixture
 def real_supabase():
     """Real Supabase client for integration testing"""
@@ -119,64 +129,63 @@ def real_supabase():
     except Exception as e:
         pytest.skip(f"Supabase connection failed: {e}")
 
+
 @pytest.fixture
 def streamlit_app():
     """Streamlit app test fixture - Updated to use app_simple.py"""
     app = AppTest.from_file("app_simple.py")
     return app
 
+
 @pytest.fixture
 def mock_openai():
     """Mock OpenAI API responses"""
-    with patch('openai.OpenAI') as mock_openai:
+    with patch("openai.OpenAI") as mock_openai:
         mock_client = Mock()
         mock_openai.return_value = mock_client
-        
+
         # Mock transcription response
         mock_client.audio.transcriptions.create.return_value.text = "This is a test transcription."
-        
+
         # Mock chat completion response
         mock_response = Mock()
         mock_response.choices = [Mock()]
         mock_response.choices[0].message.content = "This is a test AI response."
         mock_client.chat.completions.create.return_value = mock_response
-        
+
         yield mock_client
 
+
 @pytest.fixture
 def mock_anthropic():
     """Mock Anthropic API responses"""
-    with patch('anthropic.Anthropic') as mock_anthropic:
+    with patch("anthropic.Anthropic") as mock_anthropic:
         mock_client = Mock()
         mock_anthropic.return_value = mock_client
-        
+
         # Mock message response
         mock_response = Mock()
         mock_response.content = [Mock()]
         mock_response.content[0].text = "This is a test Anthropic response."
         mock_client.messages.create.return_value = mock_response
-        
+
         yield mock_client
 
+
 @pytest.fixture(autouse=True)
 def setup_logging():
     """Set up logging for tests"""
-    logger.logger.info("🧪 Starting test session")
+    logger.info("🧪 Starting test session")
     yield
-    logger.logger.info("🧪 Test session completed")
+    logger.info("🧪 Test session completed")
+
 
 # Test markers
+
+
 def pytest_configure(config):
     """Configure pytest markers"""
-    config.addinivalue_line(
-        "markers", "integration: marks tests as integration tests (may be slow)"
-    )
-    config.addinivalue_line(
-        "markers", "unit: marks tests as unit tests (fast)"
-    )
-    config.addinivalue_line(
-        "markers", "supabase: marks tests that require Supabase connection"
-    )
-    config.addinivalue_line(
-        "markers", "ai: marks tests that require AI API keys"
-    ) 
\ No newline at end of file
+    config.addinivalue_line("markers", "integration: marks tests as integration tests (may be slow)")
+    config.addinivalue_line("markers", "unit: marks tests as unit tests (fast)")
+    config.addinivalue_line("markers", "supabase: marks tests that require Supabase connection")
+    config.addinivalue_line("markers", "ai: marks tests that require AI API keys")
diff --git a/tests/test_basic_functionality.py b/tests/test_basic_functionality.py
index e4d764c..67036b8 100644
--- a/tests/test_basic_functionality.py
+++ b/tests/test_basic_functionality.py
@@ -2,109 +2,120 @@
 Basic functionality tests for WhisperForge v3.1.0
 """
 
-import pytest
-import os
-from pathlib import Path
 import sys
+from pathlib import Path
+
+import pytest
 
 # Add project root to path
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
+
 def test_imports():
     """Test that core modules can be imported without errors"""
     try:
-        from core.content_generation import transcribe_audio, generate_wisdom
-        from core.file_upload import FileUploadManager, LargeFileUploadManager
-        from core.supabase_integration import get_supabase_client
-        from core.utils import hash_password, DEFAULT_PROMPTS
-        from core.visible_thinking import thinking_step_start
+        from core.content_generation import generate_wisdom, transcribe_audio  # noqa: F401
+        from core.file_upload import FileUploadManager  # noqa: F401
+        from core.large_file_processor import EnhancedLargeFileProcessor  # noqa: F401
+        from core.supabase_integration import get_supabase_client  # noqa: F401
+        from core.utils import DEFAULT_PROMPTS, hash_password  # noqa: F401
+        from core.visible_thinking import thinking_step_start  # noqa: F401
+
         assert True, "All core imports successful"
     except ImportError as e:
         pytest.fail(f"Import error: {e}")
 
+
 def test_version_file():
     """Test that VERSION file exists and contains valid version"""
     version_file = Path(__file__).parent.parent / "VERSION"
     assert version_file.exists(), "VERSION file should exist"
-    
+
     version = version_file.read_text().strip()
     assert version == "3.1.0", f"Expected version 3.1.0, got {version}"
 
+
 def test_app_files_exist():
     """Test that required app files exist"""
     root = Path(__file__).parent.parent
-    
+
     # Main app should exist
     assert (root / "app_simple.py").exists(), "app_simple.py should exist"
-    
+
     # Redirect app should exist
     assert (root / "app.py").exists(), "app.py redirect should exist"
-    
+
     # Procfile should exist and point to app_simple.py
     procfile = root / "Procfile"
     assert procfile.exists(), "Procfile should exist"
-    
+
     content = procfile.read_text()
     assert "app_simple.py" in content, "Procfile should reference app_simple.py"
 
+
 def test_requirements_file():
     """Test that requirements.txt exists and has core dependencies"""
     req_file = Path(__file__).parent.parent / "requirements.txt"
     assert req_file.exists(), "requirements.txt should exist"
-    
+
     content = req_file.read_text()
     required_deps = ["streamlit", "openai", "supabase", "pydub"]
-    
+
     for dep in required_deps:
         assert dep in content, f"Required dependency {dep} not found in requirements.txt"
 
+
 @pytest.mark.unit
 def test_hash_password():
     """Test password hashing utility"""
     from core.utils import hash_password
-    
-    password = "test_password_123"
+
+    password = "test_password_123"  # noqa: S105
     hashed = hash_password(password)
-    
+
     assert hashed != password, "Password should be hashed"
     assert len(hashed) > 20, "Hashed password should be longer than original"
 
+
 @pytest.mark.unit
 def test_default_prompts():
     """Test that default prompts are available"""
     from core.utils import DEFAULT_PROMPTS
-    
+
     assert isinstance(DEFAULT_PROMPTS, dict), "DEFAULT_PROMPTS should be a dictionary"
     assert len(DEFAULT_PROMPTS) > 0, "Should have at least one default prompt"
 
+
 @pytest.mark.unit
 def test_visible_thinking_functions():
     """Test that visible thinking functions work without errors"""
-    from core.visible_thinking import thinking_step_start, thinking_step_complete, thinking_error
-    
+    from core.visible_thinking import thinking_error, thinking_step_complete, thinking_step_start
+
     # These should not raise exceptions
     thinking_step_start("test_step")
     thinking_step_complete("test_step")
     thinking_error("test_step", "test error")
-    
+
     assert True, "Visible thinking functions executed without errors"
 
+
 def test_core_directory_structure():
     """Test that core directory has expected modules"""
     core_dir = Path(__file__).parent.parent / "core"
     assert core_dir.exists(), "core directory should exist"
-    
+
     expected_modules = [
         "content_generation.py",
-        "file_upload.py", 
+        "file_upload.py",
         "supabase_integration.py",
         "utils.py",
-        "visible_thinking.py"
+        "visible_thinking.py",
     ]
-    
+
     for module in expected_modules:
         module_path = core_dir / module
         assert module_path.exists(), f"Core module {module} should exist"
 
+
 if __name__ == "__main__":
-    pytest.main([__file__, "-v"]) 
\ No newline at end of file
+    pytest.main([__file__, "-v"])
diff --git a/whisperforge2.code-workspace b/whisperforge2.code-workspace
index ef9f5d2..b51044b 100644
--- a/whisperforge2.code-workspace
+++ b/whisperforge2.code-workspace
@@ -4,4 +4,4 @@
             "path": "."
         }
     ]
-}
\ No newline at end of file
+}
diff --git a/whisperforge_cli.py b/whisperforge_cli.py
index bd228a2..c4e0b6c 100644
--- a/whisperforge_cli.py
+++ b/whisperforge_cli.py
@@ -4,12 +4,11 @@
 Run the WhisperForge pipeline from the command line
 """
 
-import click
 import os
 import sys
-import tempfile
 from pathlib import Path
-from typing import Optional, Dict, Any
+
+import click
 
 # Add the project root to Python path
 project_root = Path(__file__).parent
@@ -17,16 +16,12 @@
 
 # Import core functionality
 try:
-    from core.content_generation import (
-        transcribe_audio,
-        generate_wisdom,
-        generate_outline,
-        generate_article,
-    )
-    from core.logging_config import logger
-    from core.utils import DEFAULT_PROMPTS
     from dotenv import load_dotenv
 
+    from core.content_generation import transcribe_audio
+    from core.logging_config import logger
+    from core.pipeline_engine import NullListener, PipelineConfig, run_pipeline
+
     # Load environment variables
     load_dotenv()
 
@@ -36,6 +31,19 @@
     sys.exit(1)
 
 
+class CLIPipelineListener(NullListener):
+    """Pipeline listener that prints progress to the terminal via click."""
+
+    def on_step_start(self, step_index, step_name, message):
+        click.echo(f"  [{step_index + 1}/6] {step_name}: {message}")
+
+    def on_step_complete(self, step_index, step_name, result):
+        click.echo(f"  [{step_index + 1}/6] {step_name}: done")
+
+    def on_error(self, step_index, error):
+        click.echo(f"  Error at step {step_index}: {error}", err=True)
+
+
 class CLIFile:
     """Simple file wrapper for CLI usage"""
 
@@ -159,14 +167,14 @@ def pipeline():
 def run(
     input_file: str,
     model: str,
-    output: Optional[str],
+    output: str | None,
     output_format: str,
     verbose: bool,
 ):
     """Run the complete WhisperForge pipeline on an audio file"""
 
     if verbose:
-        click.echo(f"🚀 Starting WhisperForge pipeline...")
+        click.echo("🚀 Starting WhisperForge pipeline...")
         click.echo(f"Input file: {input_file}")
         click.echo(f"Model: {model}")
         click.echo(f"Output format: {output_format}")
@@ -187,75 +195,72 @@ def run(
 
     # Create CLI file wrapper
     audio_file = CLIFile(input_file)
+    stem = Path(input_file).stem
 
     try:
-        # Step 1: Transcription
-        click.echo("🎵 Transcribing audio...")
-        transcript = transcribe_audio(audio_file)
+        # --- Transcription-only shortcut ---
+        if output_format == "transcript":
+            click.echo("Transcribing audio...")
+            transcript = transcribe_audio(str(audio_file.file_path))
+            if not transcript:
+                click.echo("Transcription failed: empty result", err=True)
+                sys.exit(1)
+
+            transcript_file = output_dir / f"{stem}_transcript.txt"
+            with open(transcript_file, "w", encoding="utf-8") as f:
+                f.write(transcript)
+            click.echo(f"Transcript saved: {transcript_file}")
+            if verbose:
+                click.echo(f"Preview: {transcript[:200]}...")
+            return
 
-        if not transcript or "Error" in transcript:
-            click.echo(f"❌ Transcription failed: {transcript}", err=True)
-            sys.exit(1)
+        # --- Full pipeline via engine ---
+        config = PipelineConfig(publish_to_notion=False)
+        listener = CLIPipelineListener() if verbose else NullListener()
 
-        # Save transcript
-        transcript_file = output_dir / f"{Path(input_file).stem}_transcript.txt"
-        with open(transcript_file, "w", encoding="utf-8") as f:
-            f.write(transcript)
+        results = run_pipeline(
+            audio_file=audio_file,
+            config=config,
+            listener=listener,
+        )
 
-        click.echo(f"✅ Transcript saved: {transcript_file}")
+        if results is None:
+            click.echo("Pipeline failed -- see errors above.", err=True)
+            sys.exit(1)
 
-        if output_format == "transcript":
-            click.echo(f"📄 Transcript preview: {transcript[:200]}...")
-            return
+        # --- Write results to files ---
+        output_map = {
+            "transcript": ("_transcript.txt", "transcript"),
+            "wisdom": ("_wisdom.md", "wisdom"),
+            "outline": ("_outline.md", "outline"),
+            "article": ("_article.md", "article"),
+            "social_content": ("_social.md", "social_content"),
+        }
 
-        # Step 2: Generate content based on format
-        results = {}
-
-        if output_format in ["wisdom", "all"]:
-            click.echo("🧠 Generating wisdom extraction...")
-            wisdom = generate_wisdom(transcript, DEFAULT_PROMPTS["wisdom_extraction"])
-            if wisdom and "Error" not in wisdom:
-                wisdom_file = output_dir / f"{Path(input_file).stem}_wisdom.md"
-                with open(wisdom_file, "w", encoding="utf-8") as f:
-                    f.write(wisdom)
-                results["wisdom"] = wisdom_file
-                click.echo(f"✅ Wisdom saved: {wisdom_file}")
-
-        if output_format in ["outline", "all"]:
-            click.echo("📋 Generating outline...")
-            outline = generate_outline(transcript, DEFAULT_PROMPTS["outline_creation"])
-            if outline and "Error" not in outline:
-                outline_file = output_dir / f"{Path(input_file).stem}_outline.md"
-                with open(outline_file, "w", encoding="utf-8") as f:
-                    f.write(outline)
-                results["outline"] = outline_file
-                click.echo(f"✅ Outline saved: {outline_file}")
-
-        if output_format in ["article", "all"]:
-            click.echo("📝 Generating article...")
-            article = generate_article(transcript, DEFAULT_PROMPTS["article_writing"])
-            if article and "Error" not in article:
-                article_file = output_dir / f"{Path(input_file).stem}_article.md"
-                with open(article_file, "w", encoding="utf-8") as f:
-                    f.write(article)
-                results["article"] = article_file
-                click.echo(f"✅ Article saved: {article_file}")
+        saved_files: dict[str, Path] = {}
+        for key, (suffix, result_key) in output_map.items():
+            content = results.get(result_key)
+            if not content:
+                continue
+            if output_format not in ("all", key):
+                continue
+            out_path = output_dir / f"{stem}{suffix}"
+            with open(out_path, "w", encoding="utf-8") as f:
+                f.write(content)
+            saved_files[key] = out_path
 
         # Summary
-        click.echo("\n🎉 Pipeline completed successfully!")
-        click.echo(f"📁 Output directory: {output_dir}")
-        click.echo(f"📄 Transcript: {transcript_file}")
+        click.echo("\nPipeline completed successfully!")
+        click.echo(f"Output directory: {output_dir}")
+        for content_type, file_path in saved_files.items():
+            click.echo(f"  {content_type}: {file_path}")
 
-        for content_type, file_path in results.items():
-            click.echo(f"📝 {content_type.title()}: {file_path}")
-
-        # Show preview
-        if verbose and transcript:
-            click.echo(f"\n📄 Transcript preview:\n{transcript[:300]}...")
+        if verbose and results.get("transcript"):
+            click.echo(f"\nTranscript preview:\n{results['transcript'][:300]}...")
 
     except Exception as e:
-        logger.logger.error(f"CLI pipeline error: {e}")
-        click.echo(f"❌ Pipeline failed: {e}", err=True)
+        logger.error(f"CLI pipeline error: {e}")
+        click.echo(f"Pipeline failed: {e}", err=True)
         sys.exit(1)
 
 
@@ -274,7 +279,7 @@ def run(
     type=click.Path(),
     help="Output file path (default: input_name_transcript.txt)",
 )
-def transcribe(input_file: str, output: Optional[str]):
+def transcribe(input_file: str, output: str | None):
     """Transcribe audio file to text only"""
 
     if not validate_audio_file(input_file):
@@ -284,32 +289,26 @@ def transcribe(input_file: str, output: Optional[str]):
         sys.exit(1)
 
     # Set up output file
-    if output:
-        output_file = Path(output)
-    else:
-        output_file = Path(f"{Path(input_file).stem}_transcript.txt")
-
-    # Create CLI file wrapper
-    audio_file = CLIFile(input_file)
+    output_file = Path(output) if output else Path(f"{Path(input_file).stem}_transcript.txt")
 
     try:
-        click.echo("🎵 Transcribing audio...")
-        transcript = transcribe_audio(audio_file)
+        click.echo("Transcribing audio...")
+        transcript = transcribe_audio(input_file)
 
-        if not transcript or "Error" in transcript:
-            click.echo(f"❌ Transcription failed: {transcript}", err=True)
+        if not transcript:
+            click.echo("Transcription failed: empty result", err=True)
             sys.exit(1)
 
         # Save transcript
         with open(output_file, "w", encoding="utf-8") as f:
             f.write(transcript)
 
-        click.echo(f"✅ Transcript saved: {output_file}")
-        click.echo(f"📄 Preview: {transcript[:200]}...")
+        click.echo(f"Transcript saved: {output_file}")
+        click.echo(f"Preview: {transcript[:200]}...")
 
     except Exception as e:
-        logger.logger.error(f"CLI transcription error: {e}")
-        click.echo(f"❌ Transcription failed: {e}", err=True)
+        logger.error(f"CLI transcription error: {e}")
+        click.echo(f"Transcription failed: {e}", err=True)
         sys.exit(1)
 
 
@@ -333,14 +332,14 @@ def status():
 
     # Check dependencies
     try:
-        import openai
+        import openai  # noqa: F401
 
         click.echo("OpenAI library: ✅ Available")
     except ImportError:
         click.echo("OpenAI library: ❌ Not available")
 
     try:
-        import anthropic
+        import anthropic  # noqa: F401
 
         click.echo("Anthropic library: ✅ Available")
     except ImportError:
@@ -348,7 +347,7 @@ def status():
 
     # Check audio processing
     try:
-        from pydub import AudioSegment
+        from pydub import AudioSegment  # noqa: F401
 
         click.echo("Audio processing (pydub): ✅ Available")
     except ImportError: