AI-Powered Document Processing Suite

A collection of AI applications for document processing, transcript analysis, and presentation generation.

Applications

OpenAI Transcript to Slides

Convert meeting transcripts into professional PowerPoint presentations with AI-generated visuals.

Features:

• Smart transcript analysis with GPT-4o-mini
• Automated slide generation
• DALL-E 3 image creation
• Performance timing metrics

Quick Start:

cd openai
pip install -r requirements.txt
streamlit run app.py

CrewAI Transcript to Slides

Convert meeting transcripts into professional PowerPoint presentations using collaborative AI agents for optimized cost and performance.

Features:

• Multi-agent AI workflow (Analyzer → Designer → Optimizer)
• 90% cost reduction vs image-based approaches
• Text-only slide generation for maximum efficiency
• GPT-4o-mini optimization with token control
• Professional business presentation output

Quick Start:

cd crewai
pip install -r requirements.txt
streamlit run app.py

Global Setup

Prerequisites

• Python 3.8+
• OpenAI API key

Environment Configuration

1. Copy .env.example to .env (if available)
2. Add your API keys:

OPENAI_API_KEY=your-api-key-here

See ENV_GUIDE.md for detailed environment variable management across folders

Virtual Environment

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Repository Structure

transcript_to_powerpoint/
├── .env                    # Shared environment variables
├── .gitignore             # Global ignore patterns
├── README.md              # This file
├── openai/                # OpenAI-based transcript processor
│   ├── .env              # App-specific environment (optional)
│   ├── app.py            # Main Streamlit application
│   ├── requirements.txt  # Dependencies
│   ├── ARCHITECTURE.md   # Technical documentation
│   └── README.md         # App-specific guide
├── crewai/               # CrewAI multi-agent transcript processor
│   ├── .env              # App-specific environment (optional)
│   ├── app.py            # Main Streamlit application
│   ├── requirements.txt  # Dependencies
│   └── README.md         # App-specific guide
└── [future apps]/        # Additional AI applications

Adding New Applications

1. Create App Directory:

   mkdir new-app-name
   cd new-app-name

2. App Structure:

   new-app-name/
   ├── .env              # App-specific config (optional)
   ├── app.py            # Main application
   ├── requirements.txt  # Dependencies
   └── README.md         # Documentation
   

3. Environment Variables:

   • Use root .env for shared variables (API keys)
   • Create app .env only for app-specific settings

Development Guidelines

Environment Variables

• Root .env: Shared across all apps (API keys, global settings)
• App .env: App-specific overrides or additional variables
• Load order: App .env overrides root .env

Dependencies

• Each app has its own requirements.txt
• Keep dependencies minimal and app-specific
• Document any special installation requirements

Documentation

• Each app must have its own README.md
• Include quick start, features, and usage examples
• Technical details go in ARCHITECTURE.md (if complex)

Contributing

1. Create new apps in separate directories
2. Follow the established structure
3. Include proper documentation
4. Test with sample data before committing

License

[Specify your license here]