CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Added

• Initial project setup and documentation

[1.0.0] - 2025-01-17

Added

• 📝 Core Features

  • PDF file upload and text extraction using PyPDF2
  • Direct text input for summarization
  • AI-powered summarization using Hugging Face Transformers (BART, T5, DistilBART)
  • Bullet-point formatted summary output
  • Real-time progress indicators during processing

• 🎨 User Interface

  • Clean Streamlit web interface
  • Tabbed layout for PDF upload and text input
  • Model selection dropdown (BART, T5, DistilBART)
  • Summary length customization (Short, Medium, Long)
  • Statistics display (word count, compression ratio)
  • Download functionality for generated summaries

• 🐳 Docker Support

  • Multi-stage Dockerfile for optimized builds
  • Docker Compose configuration for easy deployment
  • Development Docker setup with live reload
  • Production-optimized Docker configuration
  • Comprehensive Docker documentation

• 🛠️ Development Tools

  • Modular code architecture with separate modules
  • Comprehensive error handling and user feedback
  • Basic testing framework
  • Docker build and run scripts
  • Development environment setup

• 📚 Documentation

  • Detailed README with installation and usage instructions
  • Docker deployment guide
  • Troubleshooting section
  • API documentation for modules

• 🔒 Security & Performance

  • Non-root Docker container execution
  • Input validation and file size limits
  • Model caching for improved performance
  • Resource limits and health checks

Technical Details

• Backend: Python 3.8+, Streamlit, Hugging Face Transformers, PyTorch
• AI Models: BART (facebook/bart-large-cnn), T5, DistilBART
• PDF Processing: PyPDF2 with comprehensive error handling
• Containerization: Docker with multi-stage builds
• Architecture: Modular design with separate PDF processing and summarization modules

Dependencies

• streamlit>=1.28.0
• transformers>=4.35.0
• torch>=2.0.0
• PyPDF2>=3.0.1
• Additional utilities for text processing and acceleration

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Release Notes

Version 1.0.0 Highlights

🎉 Initial Release - NoteSnap is now available!

This first release provides a complete solution for document summarization with:

• Easy-to-use web interface built with Streamlit
• Multiple AI models for different use cases and performance needs
• Docker support for consistent deployment across environments
• Comprehensive documentation for users and developers

Supported Platforms

• Local Installation: Windows, macOS, Linux with Python 3.8+
• Docker: Any platform supporting Docker containers
• Cloud Deployment: Compatible with cloud platforms supporting Docker

Known Limitations

• PDF processing limited to text-based documents (no OCR for scanned images)
• Maximum file size limit of 10MB for PDF uploads
• Internet connection required for initial model downloads
• GPU acceleration optional but recommended for better performance

Upcoming Features (Roadmap)

• 📱 Mobile-responsive interface improvements
• 🔍 OCR support for scanned PDF documents
• 🌐 Multi-language summarization support
• 📊 Advanced analytics and summary quality metrics
• 🔗 API endpoints for programmatic access
• 📱 Progressive Web App (PWA) capabilities

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Migration Guide

From Development to Production

When deploying to production:

1. Use Docker Compose:

   docker-compose up -d
   

2. Configure Environment Variables:

   • Copy .env.example to .env
   • Adjust settings for your environment

3. Set Resource Limits:

   • Ensure adequate memory (4GB+ recommended)
   • Configure CPU limits based on expected load

Updating Dependencies

To update to newer versions:

# Update Python packages
pip install -r requirements.txt --upgrade

# Rebuild Docker image
docker-compose build --no-cache

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Support

For questions, issues, or contributions:

• 🐛 Report Issues
• 💬 Discussions

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Thank you for using NoteSnap! 🎉