README.md

metadata

title: AI Powered YouTube Transcript Tutor
emoji: 🚀
colorFrom: red
colorTo: red
sdk: streamlit
app_file: src/streamlit_app.py
app_port: 8501
tags:
  - streamlit
pinned: false
short_description: Streamlit template space
license: mit

🎓 AI-Powered YouTube Transcript Tutor

A sophisticated Streamlit application that transforms YouTube videos into interactive learning experiences using AI. Ask questions about video content and get intelligent answers based on the transcript.

🚀 Live Demo

Try the app now: https://ai-powered-youtube-transcript-tutor.streamlit.app/

Experience the full functionality without any setup required!

🌟 Features

Core Functionality

• YouTube Transcript Extraction: Automatically extracts transcripts from YouTube videos
• AI-Powered Q&A: Ask questions about video content and get intelligent responses
• Multi-language Support: Supports transcripts in multiple languages
• Video Metadata Display: Shows video information including title, author, duration, and views

Enhanced UI/UX

• Modern Dark Theme: Clean, professional interface with dark theme
• Responsive Layout: Works seamlessly on desktop and mobile devices
• Loading Indicators: Visual feedback during processing
• Sidebar Navigation: Easy access to processed videos and settings
• Progress Bars: Real-time processing status updates

Advanced Features

• Multiple Video Processing: Handle multiple videos in a single session
• Chat History: Persistent conversation history with export options
• Export Functionality: Export Q&A sessions as PDF, text, or JSON
• Transcript Download: Download video transcripts for offline use
• Fallback System: Works even when OpenAI API quota is exceeded
• Session Management: Advanced session state management

🚀 Quick Start

💡 Want to try it first? Check out the live demo - no installation required!

Prerequisites

• Python 3.8 or higher
• OpenAI API key

Installation

1. Clone the repository

   git clone https://github.com/midlaj-muhammed/AI-Powered-YouTube-Transcript-Tutor.git
   cd AI-Powered-YouTube-Transcript-Tutor
   

2. Create virtual environment

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

3. Install dependencies

   pip install -r requirements.txt
   

4. Set up environment variables

   # Create .env file
   echo "OPENAI_API_KEY=your_openai_api_key_here" > .env
   

5. Run the application

   streamlit run app.py
   

🔧 Configuration

Environment Variables

• OPENAI_API_KEY: Your OpenAI API key for AI-powered responses

Streamlit Configuration

The app includes custom Streamlit configuration in .streamlit/config.toml for optimal performance.

📱 Usage

1. Enter YouTube URL: Paste any YouTube video URL in the input field
2. Process Video: Click "🚀 Process Video" to extract and analyze the transcript
3. Ask Questions: Use the Q&A interface to ask about the video content
4. Export Results: Export conversations in multiple formats
5. Manage Sessions: Use sidebar to navigate between processed videos

🏗️ Project Structure

AI-Powered-YouTube-Transcript-Tutor/
├── app.py                      # Main Streamlit application
├── requirements.txt            # Python dependencies
├── README.md                   # Project documentation
├── .env.example               # Environment variables template
├── .streamlit/
│   └── config.toml            # Streamlit configuration
├── static/
│   └── style.css              # Custom CSS styling
├── src/
│   ├── __init__.py
│   └── utils/
│       ├── __init__.py
│       ├── youtube_handler.py  # YouTube processing
│       ├── text_processor.py   # AI text processing
│       ├── session_manager.py  # Session management
│       ├── export_utils.py     # Export functionality
│       └── logger.py          # Logging utilities
├── config/
│   ├── __init__.py
│   └── settings.py            # Application settings
└── logs/                      # Application logs

🌐 Deployment

Hugging Face Spaces

This application is optimized for deployment on Hugging Face Spaces:

1. Create a new Space on Hugging Face
2. Choose Streamlit SDK
3. Upload all project files
4. Set OPENAI_API_KEY in Repository secrets
5. Your app will be live in minutes!

Local Development

streamlit run app.py --server.port 8501

🔒 Privacy & Security

• No Data Storage: Conversations are only stored in your browser session
• Secure Processing: All API calls are made securely
• Privacy First: No personal data is collected or stored

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Streamlit for the amazing web app framework
• OpenAI for the powerful AI capabilities
• YouTube Transcript API for transcript extraction

📞 Support

If you encounter any issues or have questions, please open an issue.

---

Made with ❤️ using Streamlit and OpenAI

✨ Features

Core Functionality

• YouTube Transcript Extraction: Automatically extracts transcripts from YouTube videos
• AI-Powered Q&A: Ask questions about video content and get intelligent responses
• Multi-language Support: Supports transcripts in multiple languages
• Video Metadata Display: Shows video information including title, author, duration, and views

Enhanced UI/UX

• Modern Design: Clean, professional interface with custom CSS styling
• Responsive Layout: Works seamlessly on desktop and mobile devices
• Loading Indicators: Visual feedback during processing
• Sidebar Navigation: Easy access to processed videos and settings
• Progress Bars: Real-time processing status updates

Advanced Features

• Multiple Video Processing: Handle multiple videos in a single session
• Chat History: Persistent conversation history with export options
• Export Functionality: Export Q&A sessions as PDF, text, or JSON
• Transcript Download: Download video transcripts for offline use
• Caching System: Intelligent caching for improved performance
• Database Integration: SQLite database for storing processed videos and conversations

Technical Improvements

• Error Handling: Comprehensive error handling and user feedback
• Input Validation: Robust YouTube URL validation
• Session Management: Advanced session state management
• Logging System: Detailed logging for debugging and monitoring
• Configuration Management: Flexible configuration via YAML and environment variables

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• OpenAI API key
• Git (for cloning the repository)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/youtube-transcript-chatbot.git
   cd youtube-transcript-chatbot
   

2. Create a virtual environment

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

3. Install dependencies

   pip install -r requirements.txt
   

4. Set up environment variables

   cp .env.template .env
   # Edit .env file and add your OpenAI API key
   

5. Run the application

   streamlit run app.py
   

6. Open your browser Navigate to http://localhost:8501

🔧 Configuration

Environment Variables

Create a .env file based on .env.template:

# Required
OPENAI_API_KEY=your_openai_api_key_here

# Optional
LOG_LEVEL=INFO
CACHE_DIRECTORY=cache
DATABASE_PATH=data/chatbot.db
MAX_CACHE_SIZE_MB=500

Configuration File

Modify config/config.yaml to customize application behavior:

app:
  title: "AI-Powered YouTube Transcript Tutor"
  description: "Ask questions from YouTube lecture transcripts using AI"

processing:
  default_chunk_size: 1000
  chunk_overlap: 200
  supported_languages: ["en", "es", "fr", "de", "it", "pt", "ru", "ja", "ko", "zh"]

ai:
  model_temperature: 0.7
  max_tokens: 2000
  retrieval_k: 4

📖 Usage Guide

Processing a Video

1. Enter YouTube URL: Paste a YouTube video URL in the input field
2. Click Process Video: The application will:
   • Extract the video transcript
   • Display video metadata
   • Create an AI knowledge base
   • Enable Q&A functionality

Asking Questions

1. Enter your question in the text input field
2. Click Ask to get an AI-generated answer
3. View source references to see which parts of the transcript were used

Managing Sessions

• View processed videos in the sidebar
• Switch between videos by clicking on video titles
• Export chat history in PDF, text, or JSON format
• Clear chat history using the sidebar button

Advanced Features

• Language Selection: Choose transcript language in settings
• Export Options: Download transcripts and chat histories
• Cache Management: Automatic caching for improved performance
• Database Storage: Persistent storage of processed videos and conversations

🐳 Docker Deployment

Using Docker Compose (Recommended)

1. Create environment file

   cp .env.template .env
   # Add your OpenAI API key to .env
   

2. Build and run

   docker-compose up -d
   

3. Access the application Open http://localhost:8501

Using Docker

1. Build the image

   docker build -t youtube-chatbot .
   

2. Run the container

   docker run -p 8501:8501 -e OPENAI_API_KEY=your_key_here youtube-chatbot
   

🧪 Testing

Run the test suite:

# Install development dependencies
pip install -e .[dev]

# Run tests
pytest

# Run tests with coverage
pytest --cov=src

# Run specific test file
pytest tests/test_youtube_handler.py

📁 Project Structure

youtube-transcript-chatbot/
├── app.py                      # Main Streamlit application
├── src/                        # Source code
│   ├── utils/                  # Utility modules
│   │   ├── youtube_handler.py  # YouTube operations
│   │   ├── text_processor.py   # Text processing and AI
│   │   ├── session_manager.py  # Session management
│   │   ├── export_utils.py     # Export functionality
│   │   ├── database.py         # Database operations
│   │   ├── cache_manager.py    # Caching system
│   │   └── logger.py           # Logging configuration
├── config/                     # Configuration files
│   ├── config.yaml            # Application configuration
│   └── settings.py            # Settings management
├── static/                     # Static assets
│   └── style.css              # Custom CSS styles
├── tests/                      # Test files
├── requirements.txt           # Python dependencies
├── .env.template             # Environment template
├── Dockerfile                # Docker configuration
├── docker-compose.yml        # Docker Compose configuration
└── README.md                 # This file

🔍 Troubleshooting

Common Issues

1. OpenAI API Key Error

   • Ensure your API key is correctly set in the .env file
   • Check that you have sufficient API credits

2. YouTube Video Not Found

   • Verify the URL is correct and the video is public
   • Some videos may have transcripts disabled

3. Transcript Not Available

   • Try selecting a different language in settings
   • Some videos may not have auto-generated transcripts

4. Performance Issues

   • Clear cache using the sidebar option
   • Reduce chunk size in configuration
   • Check available disk space

Getting Help

• Check the logs in the logs/ directory
• Enable debug mode by setting LOG_LEVEL=DEBUG in .env
• Review the application configuration in config/config.yaml

🚀 Deployment Options

Local Development

• Use streamlit run app.py for development
• Enable debug mode for detailed logging

Production Deployment

Streamlit Cloud

1. Push code to GitHub repository
2. Connect to Streamlit Cloud
3. Add secrets for environment variables

Heroku

1. Create Procfile: web: streamlit run app.py --server.port=$PORT
2. Set environment variables in Heroku dashboard
3. Deploy using Git or GitHub integration

AWS/GCP/Azure

• Use Docker container deployment
• Set up load balancer for high availability
• Configure environment variables in cloud console

🤝 Contributing

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

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Streamlit for the amazing web framework
• LangChain for AI/ML capabilities
• OpenAI for the language models
• YouTube Transcript API for transcript extraction

📊 Performance Tips

Optimization Recommendations

• Use caching: Enable vectorstore caching for frequently accessed videos
• Adjust chunk size: Smaller chunks (500-800) for better precision, larger (1200-1500) for broader context
• Monitor memory: Clear cache periodically for long-running sessions
• Database maintenance: Regularly clean up old conversations and videos

Scaling Considerations

• Horizontal scaling: Use multiple instances behind a load balancer
• Database optimization: Consider PostgreSQL for high-volume deployments
• Caching layer: Implement Redis for distributed caching
• API rate limiting: Monitor OpenAI API usage and implement rate limiting

📞 Support

For support, please open an issue on GitHub or contact the development team.

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Made with ❤️ by the YouTube Transcript Chatbot Team