🤖 InterviewBot - AI-Powered Interview Preparation Platform

An intelligent interview preparation platform powered by AI that helps job seekers prepare for interviews by generating personalized interview questions based on their resume.

Built with FastAPI, LangChain, LangGraph, MongoDB, and React, this project demonstrates end-to-end skills in Python backend development, modern frontend integration, AI orchestration, and database handling.

Features • Demo • Architecture • Installation • API Documentation

🚀 Features

🤖 AI-Powered Chatbot Assistant

Intelligent Conversation: Powered by Google Gemini and LangGraph for natural interactions
RAG-Based Responses: Retrieval Augmented Generation using indexed website content
Contextual Help: Real-time assistance during interviews without revealing answers
Multi-turn Setup: Interactive questionnaire to configure personalized interviews
Chat History: Persistent conversation storage with session management
Three Conversation Modes:
- Normal Chat - Answer questions about the platform using RAG
- Interview Setup - Interactive multi-turn questionnaire
- Contextual Help - Explanations during interviews

📄 Resume Upload & Parsing

Upload resumes (PDF/Docx) which are parsed into structured JSON using LangChain
Secure storage of parsed resumes in MongoDB
Resume-based question generation

🎤 AI Interview Simulation

MCQ Tests: Multiple choice question tests with various difficulty levels
Mock Interviews: AI-conducted voice/text interviews with realistic scenarios
Technical & Behavioral: Comprehensive coverage of different interview types
Role-Specific: Customized questions based on target job roles
Company-Specific: Questions tailored to target companies (FAANG, startups, etc.)

📊 Progress Tracking & Analytics

Dashboard: Comprehensive performance metrics and insights
Score Tracking: Detailed scoring for each interview session
PDF Reports: Downloadable performance summaries
Progress Visualization: Charts and graphs showing improvement

👤 User Management

Secure JWT-based authentication
Profile management
Session history tracking

🌐 Live Demo

Backend API: https://interview-bot-bali.onrender.com/docs

Frontend: https://interview-bot-wine.vercel.app

🛠️ Tech Stack

Backend Technologies

Technology	Purpose	Version
FastAPI	High-performance async API framework	0.116+
LangGraph	AI conversation flow and state management	0.2+
LangChain	LLM integration and orchestration	0.3+
Google Gemini	Large language model for AI responses	1.5 Pro
ChromaDB	Vector database for RAG implementation	0.4+
MongoDB	Database for user data and chat history	Latest
Motor	Async MongoDB driver	3.7+
PyJWT	JWT token authentication	2.10+
PyPDF	Resume PDF processing	5.9+
ReportLab	PDF report generation	4.0+
Sentence Transformers	Text embeddings	2.3+
BeautifulSoup4	Web scraping for content indexing	4.12+
Passlib & Bcrypt	Password hashing	1.7.4

Frontend Technologies

Technology	Purpose	Version
React	UI framework	19.1+
Vite	Build tool and dev server	7.1+
Redux Toolkit	State management	2.8+
React Router	Client-side routing	7.8+
Tailwind CSS	Utility-first styling	4.1+
Radix UI	Accessible component primitives	Latest
React Hook Form	Form handling	7.62+
Lucide React	Icon library	0.540+
React Speech Recognition	Voice input for interviews	4.0+
React Toastify	Toast notifications	11.0+

AI & ML Stack

Google Gemini 1.5 Pro - Main LLM for chatbot and question generation
all-MiniLM-L6-v2 - Sentence embeddings for RAG
ChromaDB - Vector similarity search
LangGraph - Stateful conversation graphs
LangChain - LLM orchestration and tools

Data Flow Diagram

┌──────────────┐
│     USER     │
└──────┬───────┘
       │
       ├─────────────────────────────────────────────────┐
       │                                                 │
       ▼                                                 ▼
┌─────────────────┐                             ┌─────────────────┐
│ Upload Resume   │                             │  Start Chat     │
└────────┬────────┘                             └────────┬────────┘
         │                                               │
         ▼                                               ▼
┌─────────────────┐                             ┌─────────────────┐
│ Parse PDF       │                             │ Send Message    │
│ (PyPDF/         │                             │ to Chatbot      │
│  LangChain)     │                             └────────┬────────┘
└────────┬────────┘                                      │
         │                                               ▼
         ▼                                     ┌──────────────────────┐
┌─────────────────┐                           │ LangGraph Workflow   │
│ Extract Skills  │                           │ • Intent Detection   │
│ & Experience    │                           │ • Mode Routing       │
└────────┬────────┘                           │ • State Update       │
         │                                    └────────┬─────────────┘
         │                                             │
         │                                    ┌────────┴────────┐
         │                                    │                 │
         │                                    ▼                 ▼
         │                          ┌─────────────┐   ┌──────────────┐
         │                          │ RAG Search  │   │ Multi-turn   │
         │                          │ (ChromaDB)  │   │ Dialogue     │
         │                          └─────────────┘   └──────────────┘
         │                                    │                 │
         │                                    └────────┬────────┘
         │                                             │
         ▼                                             ▼
┌─────────────────┐                          ┌─────────────────┐
│ Store in        │                          │ Generate        │
│ MongoDB         │                          │ Response        │
│ (Resume DB)     │                          │ (Gemini LLM)    │
└────────┬────────┘                          └────────┬────────┘
         │                                            │
         │                                            ▼
         │                                   ┌─────────────────┐
         │                                   │ Save to Chat    │
         │                                   │ History         │
         │                                   └────────┬────────┘
         │                                            │
         └────────────┬───────────────────────────────┘
                      │
                      ▼
            ┌──────────────────┐
            │ Start Interview  │
            └────────┬─────────┘
                     │
         ┌───────────┴───────────┐
         │                       │
         ▼                       ▼
┌──────────────────┐    ┌─────────────────┐
│  MCQ Test        │    │  Mock Interview │
│  • Time-bound    │    │  • AI Questions │
│  • Auto-score    │    │  • Voice/Text   │
└────────┬─────────┘    └────────┬────────┘
         │                       │
         └───────────┬───────────┘
                     │
                     ▼
            ┌──────────────────┐
            │ Generate Report  │
            │ (ReportLab PDF)  │
            └────────┬─────────┘
                     │
                     ▼
            ┌──────────────────┐
            │ Store Results    │
            │ (MongoDB)        │
            └────────┬─────────┘
                     │
                     ▼
            ┌──────────────────┐
            │ Display on       │
            │ Dashboard        │
            └──────────────────┘

📁 Project Structure

interview-bot/
│
├── backend/                        # Main FastAPI Application (Port 8000)
│   ├── connections/               # Database connection modules
│   │   └── mongodb.py            # MongoDB connection setup
│   │
│   ├── models/                    # Pydantic models & schemas
│   │   ├── auth.py               # User authentication models
│   │   ├── schemas.py            # Request/response schemas
│   │   └── __init__.py
│   │
│   ├── routers/                   # API route handlers
│   │   ├── auth.py               # Authentication endpoints
│   │   │   • POST /signup        # User registration
│   │   │   • POST /login         # User login
│   │   │   • GET /me             # Get current user
│   │   │
│   │   ├── bot.py                # Interview bot endpoints
│   │   │   • POST /parse-resume  # Upload & parse resume
│   │   │   • POST /generate-questions  # Generate interview questions
│   │   │   • POST /start-interview  # Start interview session
│   │   │   • POST /submit-answer    # Submit MCQ answer
│   │   │
│   │   ├── mock.py               # Mock interview endpoints
│   │   │   • POST /start-mock    # Start mock interview
│   │   │   • POST /next-question # Get next mock question
│   │   │   • POST /submit-response  # Submit response
│   │   │   • GET /mock-feedback  # Get interview feedback
│   │   │
│   │   ├── dashboard.py          # Analytics endpoints
│   │   │   • GET /stats          # Get user statistics
│   │   │   • GET /recent-tests   # Get recent test history
│   │   │   • GET /download-report  # Download PDF report
│   │   │
│   │   ├── main_router.py        # General utility endpoints
│   │   │   • GET /health         # Health check
│   │   │   • POST /contact       # Contact form
│   │   │
│   │   └── __init__.py
│   │
│   ├── utils/                     # Utility functions
│   │   ├── exception.py          # Custom exception handlers
│   │   ├── logger.py             # Logging configuration
│   │   ├── main_utils.py         # Common utility functions
│   │   ├── prompts.py            # LLM prompt templates
│   │   └── __init__.py
│   │
│   ├── main.py                    # FastAPI application entry point
│   ├── validate_env.py            # Environment variable validation
│   ├── verify_setup.py            # Setup verification script
│   ├── requirements.txt           # Python dependencies
│   ├── pyproject.toml            # UV package manager config
│   ├── uv.lock                   # Dependency lock file
│   ├── .env                      # Environment variables
│   ├── .gitignore
│   └── README.md
│
├── chatbot/                       # AI Chatbot Microservice (Port 8001)
│   ├── backend/
│   │   ├── langgraph_agent/      # LangGraph conversation agent
│   │   │   ├── state.py          # Conversation state definitions
│   │   │   │   • ChatState class
│   │   │   │   • Message models
│   │   │   │   • Interview parameters
│   │   │   │
│   │   │   ├── nodes.py          # LangGraph node functions
│   │   │   │   • intent_detection_node()
│   │   │   │   • normal_chat_node()
│   │   │   │   • setup_interview_node()
│   │   │   │   • contextual_help_node()
│   │   │   │
│   │   │   ├── graph.py          # LangGraph workflow definition
│   │   │   │   • create_graph()
│   │   │   │   • State transitions
│   │   │   │   • Conditional routing
│   │   │   │
│   │   │   ├── knowledge_base.py # RAG implementation
│   │   │   │   • Website content scraping
│   │   │   │   • Text chunking & embedding
│   │   │   │   • ChromaDB vector storage
│   │   │   │   • Semantic search
│   │   │   │
│   │   │   └── __init__.py
│   │   │
│   │   ├── database.py           # MongoDB client for chatbot
│   │   ├── main.py              # Chatbot FastAPI app
│   │   │   • POST /chat          # Main chat endpoint
│   │   │   • GET /sessions/{user_id}  # Get user sessions
│   │   │   • GET /history/{user_id}/{session_id}  # Get chat history
│   │   │   • DELETE /session/{user_id}/{session_id}  # Delete session
│   │   │   • POST /index-website  # Re-index website content
│   │   │
│   │   ├── page_capture.py       # Website scraping utility
│   │   ├── run_indexing.py       # Manual indexing script
│   │   ├── test_chatbot.py       # Chatbot test suite
│   │   ├── verify_chatbot.py     # Chatbot verification
│   │   ├── requirements.txt      # Chatbot dependencies
│   │   ├── .env                  # Chatbot environment vars
│   │   └── .env.example
│   │
│   ├── ARCHITECTURE.md           # Detailed chatbot architecture
│   ├── QUICKSTART.md            # Quick setup guide
│   └── README.md                # Chatbot documentation
│
├── frontend/                      # React Application (Port 5173)
│   ├── src/
│   │   ├── assets/               # Static assets (images, icons)
│   │   │
│   │   ├── components/           # Reusable UI components
│   │   │   ├── ui/              # Radix UI components
│   │   │   │   ├── button.jsx
│   │   │   │   ├── card.jsx
│   │   │   │   ├── dialog.jsx
│   │   │   │   └── ...
│   │   │   ├── Chatbot.jsx      # Floating chat widget
│   │   │   ├── Chatbot.css      # Chat widget styles
│   │   │   ├── Header.jsx       # Site header
│   │   │   ├── Footer.jsx       # Site footer
│   │   │   └── ...
│   │   │
│   │   ├── features/            # Feature-specific components
│   │   │   ├── auth/           # Authentication features
│   │   │   ├── interview/      # Interview features
│   │   │   └── dashboard/      # Dashboard features
│   │   │
│   │   ├── hooks/               # Custom React hooks
│   │   │   ├── useAuth.js      # Authentication hook
│   │   │   ├── useInterview.js # Interview hook
│   │   │   └── ...
│   │   │
│   │   ├── layout/              # Layout components
│   │   │   ├── RootLayout.jsx  # Main layout wrapper
│   │   │   ├── AuthLayout.jsx  # Auth pages layout
│   │   │   └── DashboardLayout.jsx  # Dashboard layout
│   │   │
│   │   ├── lib/                 # Utility libraries
│   │   │   └── utils.js        # Helper functions
│   │   │
│   │   ├── pages/               # Page components
│   │   │   ├── Home.jsx        # Landing page
│   │   │   ├── Login.jsx       # Login page
│   │   │   ├── Signup.jsx      # Registration page
│   │   │   ├── Dashboard.jsx   # User dashboard
│   │   │   ├── TestPage.jsx    # MCQ test page
│   │   │   ├── MockPage.jsx    # Mock interview page
│   │   │   └── ...
│   │   │
│   │   ├── services/            # API service layer
│   │   │   ├── api.js          # Axios instance
│   │   │   ├── authService.js  # Auth API calls
│   │   │   ├── botService.js   # Interview API calls
│   │   │   └── chatService.js  # Chatbot API calls
│   │   │
│   │   ├── store/               # Redux store
│   │   │   ├── store.js        # Store configuration
│   │   │   ├── slices/         # Redux slices
│   │   │   │   ├── authSlice.js
│   │   │   │   ├── interviewSlice.js
│   │   │   │   └── chatSlice.js
│   │   │   └── index.js
│   │   │
│   │   ├── main.jsx            # App entry point
│   │   └── styles.css          # Global styles
│   │
│   ├── public/                  # Public static files
│   ├── index.html              # HTML template
│   ├── package.json            # NPM dependencies
│   ├── vite.config.js          # Vite configuration
│   ├── tailwind.config.js      # Tailwind CSS config
│   ├── components.json         # shadcn/ui config
│   ├── .env                    # Frontend environment vars
│   └── .gitignore
│
├── index_website.py             # Website indexing CLI tool
├── test_knowledge.py           # Chatbot knowledge testing
├── DASHBOARD_FIX_NOTE.js       # Dashboard fix notes
├── INDEX_WEBSITE.bat           # Windows batch script
├── INSTALL_REPORTLAB.bat       # ReportLab installer
├── README.md                   # Main project README (this file)
├── .gitignore
└── LICENSE

🤖 Chatbot Architecture

System Overview

┌─────────────────────────────────────────────────────────────────┐
│                          USER INTERFACE                          │
│                         (React Frontend)                         │
│                                                                   │
│  ┌────────────────────────────────────────────────────────┐    │
│  │  Floating Chat Widget (Chatbot.jsx)                    │    │
│  │  • Message display                                      │    │
│  │  • Input textarea                                       │    │
│  │  • Session management                                   │    │
│  │  • Interview launch button                              │    │
│  └────────────────────────────────────────────────────────┘    │
└───────────────────────────┬─────────────────────────────────────┘
                            │
                            │ HTTP POST /chat
                            │ {user_id, message, session_id}
                            ▼
┌─────────────────────────────────────────────────────────────────┐
│                       CHATBOT BACKEND                            │
│                      (FastAPI + LangGraph)                       │
│                                                                   │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │                    main.py (API)                          │  │
│  │  • /chat endpoint                                         │  │
│  │  • Load conversation state                                │  │
│  │  • Invoke LangGraph                                       │  │
│  │  • Save to MongoDB                                        │  │
│  └─────────────────────┬────────────────────────────────────┘  │
│                        │                                         │
│                        ▼                                         │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │              LangGraph Workflow (graph.py)                │  │
│  │                                                            │  │
│  │    ┌─────────────────────────────────────────┐           │  │
│  │    │    Intent Detection Node                 │           │  │
│  │    │  • Classify user message                 │           │  │
│  │    │  • Route to appropriate mode             │           │  │
│  │    └──────────┬──────────────────────────────┘           │  │
│  │               │                                            │  │
│  │      ┌────────┴────────┬──────────────┐                  │  │
│  │      │                 │              │                   │  │
│  │      ▼                 ▼              ▼                   │  │
│  │  ┌────────┐      ┌─────────┐    ┌──────────┐            │  │
│  │  │ Normal │      │  Setup  │    │   Help   │            │  │
│  │  │  Chat  │      │Interview│    │Contextual│            │  │
│  │  │  Node  │      │   Node  │    │   Node   │            │  │
│  │  └───┬────┘      └────┬────┘    └────┬─────┘            │  │
│  │      │                │              │                   │  │
│  │      │                │              │                   │  │
│  │      └────────┬───────┴────────┬─────┘                  │  │
│  │               │                │                          │  │
│  │               ▼                ▼                          │  │
│  │      ┌─────────────┐  ┌──────────────┐                  │  │
│  │      │  RAG Search │  │Multi-turn Q&A│                  │  │
│  │      │ (ChromaDB)  │  │  State Mgmt  │                  │  │
│  │      └─────────────┘  └──────────────┘                  │  │
│  └──────────────────────────────────────────────────────────┘  │
│                                                                  │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │         Google Gemini LLM (nodes.py)                      │  │
│  │  • Intent classification                                  │  │
│  │  • Response generation                                    │  │
│  │  • Context understanding                                  │  │
│  └──────────────────────────────────────────────────────────┘  │
└────────────────┬──────────────────────────┬───────────────────┘
                 │                          │
                 ▼                          ▼
    ┌──────────────────────┐    ┌────────────────────────┐
    │   ChromaDB           │    │   MongoDB              │
    │   (Vector Store)     │    │   (Chat History)       │
    │                      │    │                        │
    │  • Website content   │    │  • Messages            │
    │  • Text embeddings   │    │  • Sessions            │
    │  • Semantic search   │    │  • Conversation state  │
    └──────────────────────┘    └────────────────────────┘

Conversation Flow

1. Normal Chat (RAG-based)

User: "How do I upload my resume?"
    ↓
Intent Detection → "normal"
    ↓
Search ChromaDB for relevant content
    ↓
Retrieve top matching documents
    ↓
Generate response using context + Gemini
    ↓
Return answer: "To upload your resume, go to..."

2. Interview Setup (Multi-turn Dialogue)

User: "I want to start an interview"
    ↓
Intent Detection → "setup"
    ↓
Check current setup_step in state
    ↓
Ask next question in sequence:
  Step 1: "Would you like MCQ or Mock interview?"
  Step 2: "What type? (Technical/Behavioral/HR/Mixed)"
  Step 3: "What role are you preparing for?"
  Step 4: "What difficulty level? (Easy/Medium/Hard)"
  Step 5: "How many questions?"
  Step 6: "Any target companies? (e.g., FAANG)"
  Step 7: "Ready to start?"
    ↓
Collect answer → Save to state.interview_params
    ↓
Move to next step
    ↓
When complete (all steps done):
  Return interview_params + should_launch_interview=true

3. Contextual Help During Interview

User is in interview with question: "Explain REST API"
User asks in chat: "What is REST?"
    ↓
Intent Detection → "help"
    ↓
Get current_question from state
    ↓
Search knowledge base + use interview context
    ↓
Generate helpful explanation (no direct answer)
    ↓
Return: "REST stands for Representational State Transfer..."

⚙️ Installation & Setup

Prerequisites

Python: 3.9 or higher
Node.js: 16.x or higher
MongoDB: Atlas account (free tier works)
Google Gemini API: API key from Google AI Studio

Step 1: Clone the Repository

git clone https://github.com/Himanshu0518/Interview-Bot.git
cd Interview-Bot

Step 2: Backend Setup (Main API)

cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies using uv (recommended)
pip install uv
uv sync

# Or install with pip
pip install -r requirements.txt

# Create .env file
cp .env.example .env

Edit backend/.env:

MONGODB_URL="mongodb+srv://username:password@cluster.mongodb.net/"
GOOGLE_API_KEY="your-gemini-api-key"
MONGODB_DATABASE_NAME="InterviewBot"
JWT_SECRET_KEY="your-secret-key-minimum-32-characters-long"
JWT_ALGORITHM="HS256"
ACCESS_TOKEN_EXPIRE_MINUTES=30

Step 3: Chatbot Backend Setup

cd ../chatbot/backend

# Install chatbot dependencies
pip install -r requirements.txt

# Create .env file
cp .env.example .env

Edit chatbot/backend/.env:

GOOGLE_API_KEY="your-gemini-api-key"
MONGODB_URL="mongodb+srv://username:password@cluster.mongodb.net/"
MONGODB_DATABASE_NAME="InterviewBot"
WEBSITE_BASE_URL="http://localhost:5173"
EMBEDDING_MODEL="all-MiniLM-L6-v2"

Important: Index website content (one-time setup)

# Make sure frontend is running first, then:
python -m langgraph_agent.knowledge_base

Step 4: Frontend Setup

cd ../../frontend

# Install dependencies
npm install

# Create .env file
cp .env.example .env

Edit frontend/.env:

VITE_BASE_URL="http://localhost:8000"
VITE_CHATBOT_URL="http://localhost:8001"

Step 5: Running the Application

You need three terminals running simultaneously:

Terminal 1 - Main Backend:

cd backend
uv run uvicorn main:app --reload --port 8000
# Or with regular Python:
# uvicorn main:app --reload --port 8000

Backend runs at: http://localhost:8000

Terminal 2 - Chatbot Backend:

cd chatbot/backend
python main.py

Chatbot runs at: http://localhost:8001

Terminal 3 - Frontend:

cd frontend
npm run dev

Frontend runs at: http://localhost:5173

Step 6: Verify Setup

Visit http://localhost:5173 in your browser. You should see the InterviewBot landing page.

🔧 Configuration

Environment Variables

Backend `.env`

# MongoDB Configuration
MONGODB_URL="mongodb+srv://username:password@cluster.mongodb.net/"
MONGODB_DATABASE_NAME="InterviewBot"

# Google Gemini API
GOOGLE_API_KEY="your-gemini-api-key"

# JWT Configuration
JWT_SECRET_KEY="your-very-long-secret-key-minimum-32-characters"
JWT_ALGORITHM="HS256"
ACCESS_TOKEN_EXPIRE_MINUTES=30

# Optional: CORS Origins
CORS_ORIGINS="http://localhost:5173,http://localhost:3000"

Chatbot Backend `.env`

# Google Gemini API
GOOGLE_API_KEY="your-gemini-api-key"

# MongoDB Configuration
MONGODB_URL="mongodb+srv://username:password@cluster.mongodb.net/"
MONGODB_DATABASE_NAME="InterviewBot"

# Website Configuration
WEBSITE_BASE_URL="http://localhost:5173"

# Embedding Model
EMBEDDING_MODEL="all-MiniLM-L6-v2"

# ChromaDB Configuration (optional)
CHROMA_DB_PATH="./chroma_db"

Frontend `.env`

# API Endpoints
VITE_BASE_URL="http://localhost:8000"
VITE_CHATBOT_URL="http://localhost:8001"

# Optional: Analytics
VITE_GA_TRACKING_ID="your-google-analytics-id"

🤝 Contributing

We welcome contributions from the community! Here's how you can help:

Ways to Contribute

Report Bugs: Open an issue with detailed reproduction steps
Suggest Features: Share your ideas for new features
Improve Documentation: Help make docs clearer
Write Code: Submit pull requests for bug fixes or features
Test: Help test new features and report issues

Contribution Process

Fork the repository

git clone https://github.com/yourusername/Interview-Bot.git
cd Interview-Bot

Create a feature branch

git checkout -b feature/AmazingFeature

Make your changes

# Write code
# Add tests
# Update documentation

Commit your changes

git add .
git commit -m 'Add some AmazingFeature'

Push to the branch

git push origin feature/AmazingFeature

Open a Pull Request

Go to GitHub repository
Click "New Pull Request"
Describe your changes
Wait for review

👥 Authors & Contributors

Core Development Team

Built by passionate students from IIIT Una combining AI with real-world solutions.

Himanshu Singh

Full Stack Developer

🎓 BTech ECE @ IIIT Una

💡 AI/ML, Web Development

Kumar Abhishek

ML Engineer

🎓 BTech ECE @ IIIT Una

💡 AI/ML, GenAI, Web Development

Contributors

Thanks to all contributors who have helped improve this project!

Name		Name	Last commit message	Last commit date
Latest commit History 43 Commits
backend		backend
chatbot		chatbot
frontend		frontend
INDEX_WEBSITE.bat		INDEX_WEBSITE.bat
INSTALL_REPORTLAB.bat		INSTALL_REPORTLAB.bat
README.md		README.md
index_website.py		index_website.py
test_knowledge.py		test_knowledge.py

Himanshu0518/Interview-Bot

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