BookBytes is a Python application that converts non-fiction books into chapter-wise audio summaries for faster and more convenient knowledge consumption. Transform a 200-page book into 15-20 digestible 5-minute audio bites!

• Problem: Long books, no time to read
• Solution: Short, condensed audio bytes for simpler consumption
• Value: 200-page book → 15-20 chapters → 5 mins audio bites each → 1.5-2hrs total

• ISBN-based book lookup: Enter any book's ISBN to get started
• Automatic chapter detection: Uses LLM to identify book chapters
• AI-powered summaries: Generates concise, informative chapter summaries
• Text-to-speech conversion: Converts summaries to high-quality audio
• RESTful API: Easy-to-use API for integration
• Database storage: Persistent storage for books, chapters, and audio files

• Backend: Python, Flask
• Database: SQLite
• AI/LLM: OpenAI GPT-3.5-turbo
• Text-to-Speech: Google Text-to-Speech (gTTS)
• Book Data: Open Library API

• Python 3.8 or higher
• OpenAI API key
• Internet connection (for API calls)

1. Clone the repository:

   git clone <repository-url>
   cd bookbytes

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:

   export OPENAI_API_KEY="your-openai-api-key-here"

   Or create a .env file:

   OPENAI_API_KEY=your-openai-api-key-here
   

Alternatively, you can run BookBytes using Docker:

1. Clone the repository:

   git clone <repository-url>
   cd bookbytes

2. Create a .env file with your OpenAI API key:

   echo "OPENAI_API_KEY=your-openai-api-key-here" > .env

3. Build and start the container:

   docker-compose up -d

4. Access the application at http://localhost:5000

5. To stop the container:

   docker-compose down

The application uses Docker volumes to persist data:

• The SQLite database and audio files are stored in a Docker volume named bookbytes-data
• This ensures your data is preserved even if the container is removed

python app.py

The server will start on http://localhost:5000

POST /api/process

curl -X POST http://localhost:5000/api/process \
  -H "Content-Type: application/json" \
  -d '{"isbn": "9780307887894"}'

Response:

{
  "success": true,
  "message": "Successfully processed 12 out of 12 chapters",
  "book": {
    "isbn": "9780307887894",
    "title": "The Power of Habit",
    "author": "Charles Duhigg"
  },
  "chapters_processed": 12
}

GET /api/books

curl http://localhost:5000/api/books

Response:

{
  "books": [
    {
      "isbn": "9780307887894",
      "title": "The Power of Habit",
      "author": "Charles Duhigg",
      "pages": 371,
      "publish_date": "2012",
      "chapter_count": 12
    }
  ]
}

GET /api/books/{isbn}/chapters

curl http://localhost:5000/api/books/9780307887894/chapters

Response:

{
  "chapters": [
    {
      "chapter_number": 1,
      "title": "The Habit Loop",
      "summary": "This chapter introduces the concept of the habit loop...",
      "audio_file_path": "audio/9780307887894_chapter_01.mp3",
      "word_count": 142
    }
  ]
}

GET /api/audio/{isbn}/{chapter_number}

curl http://localhost:5000/api/audio/9780307887894/1 --output chapter1.mp3

GET /health

curl http://localhost:5000/health

bookbytes/
├── app.py                 # Main application file
├── requirements.txt       # Python dependencies
├── README.md             # This file
├── .env                  # Environment variables (create this)
├── bookbytes.db          # SQLite database (auto-created)
├── audio/                # Generated audio files (auto-created)
│   ├── {isbn}_chapter_01.mp3
│   ├── {isbn}_chapter_02.mp3
│   └── ...
├── knowledge/
│   └── docs.md           # Project documentation
└── samples/              # Sample audio files
    └── ...

1. User Input: Provide book ISBN
2. Book Lookup: Fetch book details from Open Library API
3. Chapter Detection: Use LLM to identify book chapters
4. Summary Generation: Generate concise summaries for each chapter
5. Audio Conversion: Convert text summaries to audio using TTS
6. Storage: Save book, chapters, and audio files to database
7. API Access: Serve content through RESTful API

• Format: MP3
• Quality: Standard quality suitable for speech
• Duration: Typically 3-7 minutes per chapter
• Language: English (configurable)

• Fair Use: Generates transformative summaries for educational purposes
• Copyright Compliance: Does not reproduce substantial portions of original works
• Educational Purpose: Designed for knowledge consumption and learning

• LLM Knowledge: Limited to books in the training data
• Chapter Detection: May not work for all books
• Summary Quality: Depends on LLM's knowledge of the specific book
• API Costs: OpenAI API usage incurs costs

• Chapter-wise audio storage with metadata
• Timestamp tracking for audio navigation
• Support for book titles instead of ISBN
• Multiple TTS voice options
• Batch processing capabilities
• Web interface for easier interaction

1. "OpenAI API key not configured"

   • Ensure OPENAI_API_KEY environment variable is set
   • Check that the API key is valid and has sufficient credits

2. "Book not found for ISBN"

   • Verify the ISBN is correct (10 or 13 digits)
   • Try removing hyphens from the ISBN
   • Check if the book exists in Open Library

3. "Could not retrieve chapters"

   • The book might not be in the LLM's training data
   • Try a more popular or well-known book

4. Audio generation fails

   • Check internet connection (gTTS requires internet)
   • Ensure the audio/ directory is writable

1. Container fails to start

   • Check if the required ports are already in use
   • Verify that the .env file exists with a valid OpenAI API key

2. Data persistence issues

   • Ensure the Docker volume is properly created: docker volume ls
   • Check container logs for permission issues: docker-compose logs

3. Performance issues

   • Adjust container resources in docker-compose.yml if needed
   • For production, consider setting FLASK_DEBUG=0

Run the application in debug mode for more detailed logs:

FLASK_DEBUG=1 python app.py

For Docker:

# View logs
docker-compose logs -f

# Restart with debug mode
docker-compose down
FLASK_DEBUG=1 docker-compose up -d

• The Power of Habit - ISBN: 9780307887894
• Atomic Habits - ISBN: 9780735211292
• Thinking, Fast and Slow - ISBN: 9780374533557
• The 7 Habits of Highly Effective People - ISBN: 9780743269513

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Not licenced yet.

• Open Library for book metadata
• OpenAI for LLM capabilities
• Google for Text-to-Speech services
• Flask community for the web framework

Happy Learning! 🎓📚