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YouTube Summarizer Web Application

An AI-powered web application that automatically extracts, transcribes, and summarizes YouTube videos, providing intelligent insights and key takeaways.

🎯 Features

  • Video Transcript Extraction: Automatically fetch transcripts from YouTube videos
  • AI-Powered Summarization: Generate concise summaries using multiple AI models
  • Multi-Model Support: Choose between OpenAI GPT, Anthropic Claude, or DeepSeek
  • Key Points Extraction: Identify and highlight main topics and insights
  • Chapter Generation: Automatically create timestamped chapters
  • Export Options: Save summaries as Markdown, PDF, or plain text
  • Caching System: Reduce API calls with intelligent caching
  • Rate Limiting: Built-in protection against API overuse

🏗️ Architecture

[Web Interface] → [FastAPI Backend] → [YouTube API/Transcript API]
                         ↓
[AI Service] ← [Summary Generation] ← [Transcript Processing]
                         ↓
[Database Cache] → [Summary Storage] → [Export Service]

🚀 Quick Start

Prerequisites

  • Python 3.11+
  • YouTube API Key (optional but recommended)
  • At least one AI service API key (OpenAI, Anthropic, or DeepSeek)

Installation

  1. Clone the repository
git clone https://eniasgit.zeabur.app/demo/youtube-summarizer.git
cd youtube-summarizer
  1. Set up virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies
pip install -r requirements.txt
  1. Configure environment
cp .env.example .env
# Edit .env with your API keys and configuration
  1. Initialize database
alembic init alembic
alembic revision --autogenerate -m "Initial migration"
alembic upgrade head
  1. Run the application
python src/main.py

The application will be available at http://localhost:8082

📁 Project Structure

youtube-summarizer/
├── src/
│   ├── api/              # API endpoints
│   │   ├── routes.py     # Main API routes
│   │   └── websocket.py  # Real-time updates
│   ├── services/         # Business logic
│   │   ├── youtube.py    # YouTube integration
│   │   ├── summarizer.py # AI summarization
│   │   └── cache.py      # Caching service
│   ├── utils/            # Utility functions
│   │   ├── validators.py # Input validation
│   │   └── formatters.py # Output formatting
│   └── main.py           # Application entry point
├── tests/                # Test suite
├── docs/                 # Documentation
├── alembic/             # Database migrations
├── static/              # Frontend assets
├── templates/           # HTML templates
├── requirements.txt     # Python dependencies
├── .env.example        # Environment template
└── README.md           # This file

🔧 Configuration

Essential Environment Variables

Variable Description Required
YOUTUBE_API_KEY YouTube Data API v3 key Optional*
OPENAI_API_KEY OpenAI API key One of these
ANTHROPIC_API_KEY Anthropic Claude API key is required
DEEPSEEK_API_KEY DeepSeek API key for AI
DATABASE_URL Database connection string Yes
SECRET_KEY Session secret key Yes

*YouTube API key improves metadata fetching but transcript extraction works without it.

🧪 Testing

Run the test suite:

pytest tests/ -v
pytest tests/ --cov=src --cov-report=html  # With coverage

📝 API Documentation

Once running, visit:

  • Interactive API docs: http://localhost:8082/docs
  • Alternative docs: http://localhost:8082/redoc

Key Endpoints

  • POST /api/summarize - Submit a YouTube URL for summarization
  • GET /api/summary/{id} - Retrieve a summary
  • GET /api/summaries - List all summaries
  • POST /api/export/{id} - Export summary in different formats

🚢 Deployment

Docker

docker build -t youtube-summarizer .
docker run -p 8082:8082 --env-file .env youtube-summarizer

Production Considerations

  1. Use PostgreSQL instead of SQLite for production
  2. Configure proper CORS settings
  3. Set up SSL/TLS certificates
  4. Implement user authentication
  5. Configure rate limiting per user
  6. Set up monitoring and logging

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

📄 License

This project is part of the Personal AI Assistant ecosystem.

🔗 Related Projects

📞 Support

For issues and questions, please create an issue in the repository.