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-# 📝 NoteSnap
-
-<div align="center">
-
-![NoteSnap Logo](https://img.shields.io/badge/📝-NoteSnap-blue?style=for-the-badge)
-
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
-[![Streamlit](https://img.shields.io/badge/Streamlit-FF4B4B?style=flat&logo=streamlit&logoColor=white)](https://streamlit.io/)
-[![Docker](https://img.shields.io/badge/Docker-2496ED?style=flat&logo=docker&logoColor=white)](https://www.docker.com/)
-[![Transformers](https://img.shields.io/badge/🤗%20Transformers-FFD21E?style=flat)](https://huggingface.co/transformers/)
-
-[![GitHub stars](https://img.shields.io/github/stars/PRATEEK-260/NoteSnap?style=social)](https://github.com/PRATEEK-260/NoteSnap/stargazers)
-[![GitHub forks](https://img.shields.io/github/forks/PRATEEK-260/NoteSnap?style=social)](https://github.com/PRATEEK-260/NoteSnap/network/members)
-[![GitHub issues](https://img.shields.io/github/issues/PRATEEK-260/NoteSnap)](https://github.com/PRATEEK-260/NoteSnap/issues)
-
-</div>
-
-A powerful web application that transforms lengthy documents and notes into concise, bullet-point summaries using state-of-the-art AI models.
-
----
-
-## 📋 Table of Contents
-
-- [✨ Features](#-features)
-- [🚀 Quick Start](#-quick-start)
-  - [Option 1: Docker (Recommended)](#option-1-docker-recommended)
-  - [Option 2: Local Installation](#option-2-local-installation)
-- [📖 Usage Guide](#-usage-guide)
-- [🖼️ Screenshots](#️-screenshots)
-- [🛠️ Technical Details](#️-technical-details)
-- [🐳 Docker Deployment](#-docker-deployment)
-- [🔧 Configuration](#-configuration)
-- [🚨 Troubleshooting](#-troubleshooting)
-- [🤝 Contributing](#-contributing)
-- [📄 License](#-license)
-- [🙏 Acknowledgments](#-acknowledgments)
-- [📞 Support](#-support)
-
----
-
-## ✨ Features
-
-- **PDF Processing**: Upload PDF files and extract text content automatically
-- **Direct Text Input**: Paste text content directly for immediate summarization
-- **AI-Powered Summarization**: Uses Hugging Face Transformers (BART, T5) for high-quality summaries
-- **Bullet-Point Format**: Clean, readable bullet-point summaries
-- **Multiple AI Models**: Choose from different pre-trained models
-- **Customizable Length**: Adjust summary length (Short, Medium, Long)
-- **Progress Tracking**: Real-time progress indicators during processing
-- **Download Summaries**: Save generated summaries as text files
-- **Statistics**: View compression ratios and word counts
-- **Error Handling**: Comprehensive error handling and user feedback
-
-## 🚀 Quick Start
-
-### 🌐 Try Online (Fastest)
-**[🚀 Live Demo on Hugging Face Spaces](https://huggingface.co/spaces/PRATEEK-260/NoteSnap)**
-- No installation required
-- Instant access in your browser
-- Full functionality available
-
-### Option 1: Docker (Recommended)
-
-#### Prerequisites
-- Docker and Docker Compose installed
-- Internet connection (for downloading AI models)
-
-#### Using Docker Compose (Easiest)
-```bash
-# Clone the repository
-git clone https://github.com/PRATEEK-260/NoteSnap.git
-cd NoteSnap
-
-# Start the application
-docker-compose up -d
-
-# Access the application at http://localhost:8501
-```
-
-#### Using Docker Scripts
-```bash
-# Build the Docker image
-./docker-build.sh
-
-# Run the container
-./docker-run.sh
-
-# For development with live code reloading
-./docker-dev.sh
-```
-
-#### Manual Docker Commands
-```bash
-# Build the image
-docker build -t notesnap .
-
-# Run the container
-docker run -p 8501:8501 notesnap
-```
-
-### Option 2: Local Installation
-
-#### Prerequisites
-- Python 3.8 or higher
-- pip (Python package installer)
-- Internet connection (for downloading AI models)
-
-#### Installation Steps
-1. **Clone the repository**
-   ```bash
-   git clone https://github.com/PRATEEK-260/NoteSnap.git
-   cd NoteSnap
-   ```
-
-2. **Install dependencies**
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. **Run the application**
-   ```bash
-   streamlit run app.py
-   ```
-
-4. **Open your browser**
-   - The application will automatically open at `http://localhost:8501`
-   - If it doesn't open automatically, navigate to the URL manually
-
-## 📖 Usage Guide
-
-### PDF Summarization
-
-1. **Upload PDF**: Click on the "📄 PDF Upload" tab
-2. **Select File**: Choose a PDF file (max 10MB)
-3. **Process**: Click "📖 Extract & Summarize PDF"
-4. **Review**: View the extracted text preview
-5. **Get Summary**: The AI will generate a bullet-point summary
-6. **Download**: Save the summary using the download button
-
-### Text Summarization
-
-1. **Input Text**: Click on the "📝 Text Input" tab
-2. **Paste Content**: Enter or paste your text (minimum 100 characters)
-3. **Summarize**: Click "🚀 Summarize Text"
-4. **Review**: View the generated summary
-5. **Download**: Save the summary as needed
-
-### Settings
-
-- **AI Model**: Choose from BART (recommended), T5, or DistilBART
-- **Summary Length**: Select Short, Medium, or Long summaries
-- **Statistics**: View word counts and compression ratios
-
-## 🛠️ Technical Details
-
-### Architecture
-
-```
-NoteSnap/
-├── app.py                 # Main Streamlit application
-├── modules/
-│   ├── __init__.py
-│   ├── pdf_processor.py   # PDF text extraction
-│   ├── text_summarizer.py # AI summarization
-│   └── utils.py          # Utility functions
-├── requirements.txt       # Python dependencies
-└── README.md             # This file
-```
-
-### AI Models
-
-- **BART (facebook/bart-large-cnn)**: Best quality, recommended for most use cases
-- **T5 Small**: Faster processing, good for shorter texts
-- **DistilBART**: Balanced performance and speed
-
-### Dependencies
-
-- **Streamlit**: Web application framework
-- **Transformers**: Hugging Face AI models
-- **PyTorch**: Deep learning framework
-- **PyPDF2**: PDF text extraction
-- **Additional utilities**: See `requirements.txt`
-
-## 🔧 Configuration
-
-### Model Selection
-
-You can change the default model by modifying the `TextSummarizer` initialization in `app.py`:
-
-```python
-text_summarizer = TextSummarizer(model_name="your-preferred-model")
-```
-
-### Summary Length
-
-Adjust default summary lengths in `modules/text_summarizer.py`:
-
-```python
-self.min_summary_length = 50  # Minimum words
-self.max_summary_length = 300  # Maximum words
-```
-
-### File Size Limits
-
-Modify PDF file size limits in `modules/pdf_processor.py`:
-
-```python
-self.max_file_size = 10 * 1024 * 1024  # 10MB
-```
-
-## 🚨 Troubleshooting
-
-### Common Issues
-
-1. **Model Loading Errors**
-   - Ensure stable internet connection
-   - Check available disk space (models can be 1-2GB)
-   - Try switching to a smaller model (T5 Small or DistilBART)
-
-2. **PDF Processing Issues**
-   - Ensure PDF is not encrypted
-   - Check if PDF contains readable text (not just images)
-   - Try with a smaller PDF file
-
-3. **Memory Errors**
-   - Reduce text length
-   - Close other applications
-   - Try using CPU instead of GPU
-
-4. **Slow Performance**
-   - Use GPU if available
-   - Choose smaller models for faster processing
-   - Process shorter text chunks
-
-### Error Messages
-
-- **"Text is too short"**: Minimum 100 characters required
-- **"No readable text found"**: PDF may contain only images
-- **"Model loading error"**: Check internet connection
-- **"Out of memory"**: Reduce text length or restart application
-
-## 🎯 Best Practices
-
-### For Best Results
-
-1. **Text Quality**: Use well-formatted, coherent text
-2. **Length**: Optimal text length is 500-5000 words
-3. **Content**: Works best with structured content (articles, reports, notes)
-4. **Model Choice**: Use BART for academic/formal content, T5 for general text
-
-### Performance Tips
-
-1. **GPU Usage**: Enable CUDA for faster processing
-2. **Batch Processing**: Process multiple documents separately
-3. **Model Caching**: Models are cached after first load
-4. **Text Preprocessing**: Clean text improves summary quality
-
-## 🖼️ Screenshots
-
-<div align="center">
-
-### Main Interface
-![Main Interface](Screenshots/Main%20interface.png)
-*Clean and intuitive interface with PDF upload and text input options*
-
-### PDF Processing
-![PDF Processing](Screenshots/pdf%20processing.png)
-*Real-time PDF processing with progress indicators*
-
-### Summary Results
-![Summary Results](Screenshots/Summery%20Result.png)
-*Bullet-point summaries with statistics and download options*
-
-### Settings Panel
-![Settings Panel](Screenshots/settings%20panel.png)
-*Customizable AI model selection and summary length options*
-
-</div>
-
-## 🎥 Demo
-
-🚀 **[Live Demo](https://huggingface.co/spaces/PRATEEK-260/NoteSnap)** - Try it now on Hugging Face Spaces!
-
-## 📄 License
-
-This project is open source and available under the MIT License.
-
-## 🤝 Contributing
-
-Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.
-
-## 🐳 Docker Deployment
-
-### Production Deployment
-
-For production deployment, use the standard Docker Compose configuration:
-
-```bash
-# Start in production mode
-docker-compose up -d
-
-# View logs
-docker-compose logs -f
-
-# Stop the application
-docker-compose down
-
-# Update the application
-docker-compose pull
-docker-compose up -d
-```
-
-### Development Mode
-
-For development with live code reloading:
-
-```bash
-# Start development environment
-docker-compose -f docker-compose.dev.yml up
-
-# Or use the convenience script
-./docker-dev.sh
-```
-
-### Docker Configuration
-
-#### Environment Variables
-- `STREAMLIT_SERVER_PORT`: Port for the application (default: 8501)
-- `TRANSFORMERS_CACHE`: Cache directory for AI models
-- `MAX_FILE_SIZE_MB`: Maximum PDF file size (default: 10MB)
-
-#### Volumes
-- `model_cache`: Persistent storage for downloaded AI models
-- `logs`: Application logs
-- `uploads`: Temporary file storage (optional)
-
-#### Resource Limits
-- Memory: 4GB limit, 2GB reserved
-- CPU: 2 cores limit, 1 core reserved
-
-### Docker Troubleshooting
-
-1. **Container won't start**: Check logs with `docker-compose logs`
-2. **Out of memory**: Increase Docker memory limits
-3. **Model download fails**: Ensure internet connectivity
-4. **Permission issues**: Check file ownership and Docker user settings
-
-## 🤝 Contributing
-
-We welcome contributions from the community! Here's how you can help:
-
-### 🌟 Ways to Contribute
-
-- ⭐ **Star this repository** if you find it useful
-- 🐛 **Report bugs** by opening an [issue](https://github.com/PRATEEK-260/NoteSnap/issues)
-- 💡 **Suggest features** or improvements
-- 📖 **Improve documentation**
-- 🔧 **Submit pull requests** with bug fixes or new features
-
-### 🚀 Getting Started
-
-1. **Fork the repository**
-   ```bash
-   # Click the "Fork" button on GitHub, then:
-   git clone https://github.com/YOUR-USERNAME/NoteSnap.git
-   cd NoteSnap
-   ```
-
-2. **Create a feature branch**
-   ```bash
-   git checkout -b feature/amazing-feature
-   ```
-
-3. **Make your changes**
-   - Follow the existing code style
-   - Add tests for new features
-   - Update documentation as needed
-
-4. **Test your changes**
-   ```bash
-   # Run basic tests
-   python test_basic.py
-
-   # Test Docker build
-   ./docker-test.sh
-   ```
-
-5. **Submit a pull request**
-   ```bash
-   git add .
-   git commit -m "Add amazing feature"
-   git push origin feature/amazing-feature
-   ```
-
-### 📋 Development Guidelines
-
-- **Code Style**: Follow PEP 8 for Python code
-- **Documentation**: Update README.md for new features
-- **Testing**: Add tests for new functionality
-- **Docker**: Ensure Docker compatibility
-- **Dependencies**: Keep requirements.txt updated
-
-### 🐛 Reporting Issues
-
-When reporting issues, please include:
-
-- **Environment details** (OS, Python version, Docker version)
-- **Steps to reproduce** the issue
-- **Expected vs actual behavior**
-- **Error messages** or logs
-- **Screenshots** if applicable
-
-[**Report an Issue →**](https://github.com/PRATEEK-260/NoteSnap/issues/new)
-
-### 💬 Discussions
-
-Join our community discussions:
-
-- [**GitHub Discussions**](https://github.com/PRATEEK-260/NoteSnap/discussions) - General questions and ideas
-- [**Issues**](https://github.com/PRATEEK-260/NoteSnap/issues) - Bug reports and feature requests
-
-## 📄 License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
-## 🙏 Acknowledgments
-
-### 🛠️ Built With
-
-- [**Streamlit**](https://streamlit.io/) - Web application framework
-- [**Hugging Face Transformers**](https://huggingface.co/transformers/) - AI/ML models
-- [**PyTorch**](https://pytorch.org/) - Deep learning framework
-- [**PyPDF2**](https://pypdf2.readthedocs.io/) - PDF processing
-- [**Docker**](https://www.docker.com/) - Containerization
-
-### 🎯 Inspiration
-
-- Inspired by the need for efficient document summarization
-- Built to help students, researchers, and professionals save time
-- Leverages state-of-the-art AI models for high-quality summaries
-
-### 🤖 AI Models
-
-Special thanks to the teams behind these amazing models:
-- [**BART**](https://huggingface.co/facebook/bart-large-cnn) by Facebook AI
-- [**T5**](https://huggingface.co/t5-small) by Google Research
-- [**DistilBART**](https://huggingface.co/sshleifer/distilbart-cnn-12-6) by Sam Shleifer
-
-## 📞 Support
-
-If you encounter any issues or have questions:
-
-### 🔍 Self-Help Resources
-
-1. 📖 Check the [troubleshooting section](#-troubleshooting) above
-2. 🐛 Review error messages for specific guidance
-3. 📦 Ensure all dependencies are properly installed
-4. 🔄 Try with different models or settings
-5. 🐳 For Docker issues, check container logs: `docker-compose logs`
-
-### 💬 Get Help
-
-- 🐛 **Bug Reports**: [Open an Issue](https://github.com/PRATEEK-260/NoteSnap/issues/new)
-- 💡 **Feature Requests**: [Start a Discussion](https://github.com/PRATEEK-260/NoteSnap/discussions)
-
----
-
-<div align="center">
-
-**Made with ❤️ by [PRATEEK-260](https://github.com/PRATEEK-260)**
-
-**Happy Summarizing! 📝✨**
-
-[![GitHub](https://img.shields.io/badge/GitHub-PRATEEK--260-181717?style=flat&logo=github)](https://github.com/PRATEEK-260)
-
-</div>
-=======
-# NoteSnap
->>>>>>> 9b4f2dab9437daaefabf059cd647a5761c93c197
+---
+title: NoteSnap
+emoji: 📝
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: "1.28.0"
+app_file: app.py
+pinned: false
+---
+
+# 📝 NoteSnap
+
+A powerful web application that transforms lengthy documents and notes into concise, bullet-point summaries using state-of-the-art AI models.
+
+## Features
+
+- **PDF Processing**: Upload PDF files and extract text content automatically
+- **Direct Text Input**: Paste text content directly for immediate summarization
+- **AI-Powered Summarization**: Uses Hugging Face Transformers (BART, T5) for high-quality summaries
+- **Bullet-Point Format**: Clean, readable bullet-point summaries
+- **Multiple AI Models**: Choose from different pre-trained models
+- **Customizable Length**: Adjust summary length (Short, Medium, Long)
+- **Download Summaries**: Save generated summaries as text files
+
+## Usage
+
+1. Upload a PDF or paste text
+2. Choose your AI model and summary length
+3. Click Summarize
+4. Download your summary
+
+## Tech Stack
+
+- Streamlit
+- Hugging Face Transformers
+- PyTorch
+- PyPDF2