README.md

metadata

title: Slash
emoji: 🌖
colorFrom: purple
colorTo: gray
sdk: streamlit
sdk_version: 1.25.0
pinned: false
license: mit
short_description: 'An AI powered book summarizer '

📚 Book Summarizer AI

An intelligent web application that extracts text from PDF books and generates comprehensive summaries using state-of-the-art AI models.

✨ Features

• 📚 PDF Text Extraction: Advanced PDF processing with multiple extraction methods
• 🤖 AI-Powered Summarization: Uses transformer models (BART, T5) for high-quality summaries
• 🌐 Beautiful Web Interface: Modern UI built with Streamlit
• ⚡ FastAPI Backend: Scalable and fast API for processing
• 📝 Configurable Settings: Adjust summary length, chunk size, and AI models
• 📊 Text Analysis: Detailed statistics about book content
• 💾 Download Summaries: Save summaries as text files

🚀 Quick Start

Option 1: Automated Setup (Recommended)

Windows:

# Double-click start.bat or run:
start.bat

Unix/Linux/Mac:

# Make script executable and run:
chmod +x start.sh
./start.sh

Option 2: Manual Setup

1. Install dependencies:

pip install -r requirements.txt

2. Download NLTK data:

python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords')"

3. Start the FastAPI backend:

uvicorn api.main:app --reload --port 8000

4. Start the Streamlit frontend:

streamlit run app.py

5. Open your browser:
   • Frontend: http://localhost:8501
   • API Docs: http://localhost:8000/docs

📖 Usage

1. Upload PDF: Select a PDF book file (max 50MB)
2. Configure Settings: Choose AI model and summary parameters
3. Generate Summary: Click "Generate Summary" and wait for processing
4. Download Result: Save your AI-generated summary

🛠️ Technology Stack

Frontend

• Streamlit: Modern web interface
• Custom CSS: Beautiful styling and responsive design

Backend

• FastAPI: High-performance API framework
• Uvicorn: ASGI server for FastAPI

AI & ML

• Hugging Face Transformers: State-of-the-art NLP models
• PyTorch: Deep learning framework
• BART/T5 Models: Pre-trained summarization models

PDF Processing

• PyPDF2: PDF text extraction
• pdfplumber: Advanced PDF processing
• NLTK: Natural language processing

📁 Project Structure

book-summarizer/
├── app.py                 # Streamlit frontend
├── start.py              # Automated startup script
├── start.bat             # Windows startup script
├── start.sh              # Unix/Linux/Mac startup script
├── api/
│   ├── __init__.py       # API package
│   ├── main.py           # FastAPI backend
│   ├── pdf_processor.py  # PDF text extraction
│   ├── summarizer.py     # AI summarization logic
│   └── utils.py          # Utility functions
├── requirements.txt      # Python dependencies
└── README.md            # Project documentation

⚙️ Configuration

AI Models

• facebook/bart-large-cnn: Best quality, slower processing
• t5-small: Faster processing, good quality
• facebook/bart-base: Balanced performance

Summary Settings

• Max Length: 50-500 words (default: 150)
• Min Length: 10-200 words (default: 50)
• Chunk Size: 500-2000 characters (default: 1000)
• Overlap: 50-200 characters (default: 100)

🔧 API Endpoints

• GET / - API information
• GET /health - Health check
• POST /upload-pdf - Validate PDF file
• POST /extract-text - Extract text from PDF
• POST /summarize - Generate book summary
• GET /models - List available AI models
• POST /change-model - Switch AI model

📋 Requirements

• Python: 3.8 or higher
• Memory: At least 4GB RAM (8GB recommended)
• Storage: 2GB free space for models
• Internet: Required for first-time model download

🐛 Troubleshooting

Common Issues

1. "Module not found" errors:

   pip install -r requirements.txt
   

2. NLTK data missing:

   python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords')"
   

3. API connection failed:

   • Ensure FastAPI is running on port 8000
   • Check firewall settings
   • Verify no other service is using the port

4. Large PDF processing slow:

   • Reduce chunk size in advanced settings
   • Use a faster model (t5-small)
   • Ensure sufficient RAM

5. Model download issues:

   • Check internet connection
   • Clear Hugging Face cache: rm -rf ~/.cache/huggingface

Performance Tips

• GPU Acceleration: Install CUDA for faster processing
• Model Selection: Use smaller models for faster results
• Chunk Size: Smaller chunks = faster processing but may lose context
• Memory: Close other applications to free up RAM

🤝 Contributing

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

📄 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Hugging Face for transformer models
• Streamlit for the web framework
• FastAPI for the backend framework
• The open-source community for various libraries

📞 Support

For issues, questions, or feature requests:

1. Check the troubleshooting section
2. Open an issue on GitHub

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Happy summarizing! 📚✨

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference