| | --- |
| | 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/](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](https://ai-powered-youtube-transcript-tutor.streamlit.app/) - no installation required! |
| |
|
| | ### Prerequisites |
| | - Python 3.8 or higher |
| | - OpenAI API key |
| |
|
| | ### Installation |
| |
|
| | 1. **Clone the repository** |
| | ```bash |
| | git clone https://github.com/midlaj-muhammed/AI-Powered-YouTube-Transcript-Tutor.git |
| | cd AI-Powered-YouTube-Transcript-Tutor |
| | ``` |
| |
|
| | 2. **Create virtual environment** |
| | ```bash |
| | python -m venv venv |
| | source venv/bin/activate # On Windows: venv\Scripts\activate |
| | ``` |
| |
|
| | 3. **Install dependencies** |
| | ```bash |
| | pip install -r requirements.txt |
| | ``` |
| |
|
| | 4. **Set up environment variables** |
| | ```bash |
| | # Create .env file |
| | echo "OPENAI_API_KEY=your_openai_api_key_here" > .env |
| | ``` |
| |
|
| | 5. **Run the application** |
| | ```bash |
| | 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](https://huggingface.co/spaces) |
| | 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 |
| | ```bash |
| | 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](LICENSE) file for details. |
| |
|
| | ## 🙏 Acknowledgments |
| |
|
| | - [Streamlit](https://streamlit.io/) for the amazing web app framework |
| | - [OpenAI](https://openai.com/) for the powerful AI capabilities |
| | - [YouTube Transcript API](https://github.com/jdepoix/youtube-transcript-api) for transcript extraction |
| |
|
| | ## 📞 Support |
| |
|
| | If you encounter any issues or have questions, please [open an issue](https://github.com/midlaj-muhammed/AI-Powered-YouTube-Transcript-Tutor/issues). |
| |
|
| | --- |
| |
|
| | **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** |
| | ```bash |
| | git clone https://github.com/yourusername/youtube-transcript-chatbot.git |
| | cd youtube-transcript-chatbot |
| | ``` |
| |
|
| | 2. **Create a virtual environment** |
| | ```bash |
| | python -m venv venv |
| | source venv/bin/activate # On Windows: venv\Scripts\activate |
| | ``` |
| |
|
| | 3. **Install dependencies** |
| | ```bash |
| | pip install -r requirements.txt |
| | ``` |
| |
|
| | 4. **Set up environment variables** |
| | ```bash |
| | cp .env.template .env |
| | # Edit .env file and add your OpenAI API key |
| | ``` |
| |
|
| | 5. **Run the application** |
| | ```bash |
| | streamlit run app.py |
| | ``` |
| |
|
| | 6. **Open your browser** |
| | Navigate to `http://localhost:8501` |
| |
|
| | ## 🔧 Configuration |
| |
|
| | ### Environment Variables |
| |
|
| | Create a `.env` file based on `.env.template`: |
| |
|
| | ```env |
| | # 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: |
| |
|
| | ```yaml |
| | 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** |
| | ```bash |
| | cp .env.template .env |
| | # Add your OpenAI API key to .env |
| | ``` |
| |
|
| | 2. **Build and run** |
| | ```bash |
| | docker-compose up -d |
| | ``` |
| |
|
| | 3. **Access the application** |
| | Open `http://localhost:8501` |
| |
|
| | ### Using Docker |
| |
|
| | 1. **Build the image** |
| | ```bash |
| | docker build -t youtube-chatbot . |
| | ``` |
| |
|
| | 2. **Run the container** |
| | ```bash |
| | docker run -p 8501:8501 -e OPENAI_API_KEY=your_key_here youtube-chatbot |
| | ``` |
| |
|
| | ## 🧪 Testing |
| |
|
| | Run the test suite: |
| |
|
| | ```bash |
| | # 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](LICENSE) file for details. |
| |
|
| | ## 🙏 Acknowledgments |
| |
|
| | - [Streamlit](https://streamlit.io/) for the amazing web framework |
| | - [LangChain](https://langchain.com/) for AI/ML capabilities |
| | - [OpenAI](https://openai.com/) for the language models |
| | - [YouTube Transcript API](https://github.com/jdepoix/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. |
| |
|
| | --- |
| |
|
| | Made with ❤️ by the YouTube Transcript Chatbot Team |
| |
|
