A Flask web application that fetches Vercel's February 2025 changelog updates and organizes them both chronologically and thematically using advanced NLP techniques.

• Fetches and displays February 2025 Vercel updates
• Organizes updates both by date and theme
• Uses advanced NLP for intelligent theme clustering
• Clean, modern UI with tabbed interface
• Real-time update fetching and clustering
• Responsive design with proper loading states

• Python 3.8+
• Flask 3.0.0
• Transformers 4.36.2
• Sentence-Transformers 2.2.2
• Scikit-learn 1.3.2
• BeautifulSoup4 4.12.2
• Feedparser 6.0.10
• PyTorch 2.1.2
• NumPy 1.24.3

1. Clone the repository:

   git clone <your-repo-url>
   cd monthly-updates

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the required packages:

   pip install -r requirements.txt

1. Start the Flask server:

   python app.py

2. Open your web browser and navigate to:

   http://localhost:5002
   

3. Use the interface:

   • Click "Refresh Monthly Updates" to fetch the latest updates
   • Click "Sort into Themes" to view updates clustered by theme
   • Switch between Monthly and Themed views using the tabs

1. Monthly Updates:

   • Fetches Vercel's Atom feed
   • Filters for February 2025 changelog entries
   • Sorts updates chronologically
   • Summarizes content using BART model

2. Themed Updates:

   • Uses Sentence Transformers for semantic text embeddings
   • Applies K-means clustering to group similar updates
   • Automatically determines optimal number of themes
   • Names themes based on most representative updates

3. Features:

   • Smart date parsing and formatting
   • Automatic content summarization
   • Intelligent theme clustering
   • Clean, card-based layout
   • Status indicators for all operations

• Built with Flask backend
• Uses modern HTML/CSS/JavaScript frontend
• Implements responsive design principles
• Includes proper error handling
• Uses async/await for smooth user experience
• Leverages advanced NLP techniques

MIT License - feel free to use this project as you wish.

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