Update README.md

README.md

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-# News Summarization and Text-to-Speech Application
-
-A web-based application that extracts news articles related to companies, performs sentiment analysis, conducts comparative analysis, and generates a text-to-speech output in Hindi.
-
-## Features
-
-- **News Extraction**: Scrapes at least 10 unique news articles about a given company using BeautifulSoup
-- **Sentiment Analysis**: Analyzes the sentiment of each article (positive, negative, neutral)
-- **Comparative Analysis**: Compares sentiment across articles to derive insights
-- **Text-to-Speech**: Converts summarized content to Hindi speech
-- **User Interface**: Simple web interface built with Streamlit
-- **API Communication**: Backend and frontend communicate through APIs
-
-## Project Structure
-
-```
-.
-├── app.py              # Main Streamlit application
-├── api.py              # API endpoints
-├── utils.py            # Utility functions for scraping, sentiment analysis, etc.
-├── healthcheck.py      # Script to verify all dependencies and services
-├── requirements.txt    # Project dependencies
-├── Dockerfile          # Docker configuration for deployment
-├── Spacefile           # Hugging Face Spaces configuration
-└── README.md           # Project documentation
-```
-
-## Setup Instructions
-
-1. **Clone the repository**:
-   ```
-   git clone https://github.com/yourusername/news-summarization-tts.git
-   cd news-summarization-tts
-   ```
-
-2. **Create a virtual environment** (recommended):
-   ```
-   python -m venv venv
-   source venv/bin/activate  # On Windows: venv\Scripts\activate
-   ```
-
-3. **Install dependencies**:
-   ```
-   pip install -r requirements.txt
-   ```
-
-4. **Install system dependencies** (for text-to-speech functionality):
-   - On Ubuntu/Debian:
-     ```
-     sudo apt-get install espeak ffmpeg
-     ```
-   - On Windows:
-     Download and install espeak from http://espeak.sourceforge.net/download.html
-
-5. **Run the healthcheck** (to verify all dependencies are working):
-   ```
-   python healthcheck.py
-   ```
-
-6. **Run the API server**:
-   ```
-   uvicorn api:app --reload
-   ```
-
-7. **Run the Streamlit application** (in a separate terminal):
-   ```
-   streamlit run app.py
-   ```
-
-## Models Used
-
-- **News Summarization**: Extractive summarization using NLTK and NetworkX
-- **Sentiment Analysis**: VADER for sentiment analysis and Hugging Face Transformers
-- **Translation**: Google Translate API via deep-translator library
-- **Text-to-Speech**: Google Text-to-Speech (gTTS) and pyttsx3 as fallback for Hindi conversion
-
-## API Documentation
-
-### Endpoints
-
-- `POST /api/get_news`: Fetches news articles about a company
-  - Request body: `{"company_name": "Tesla"}`
-  - Returns a list of articles with metadata
-
-- `POST /api/analyze_sentiment`: Performs sentiment analysis on articles
-  - Request body: `{"articles": [article_list]}`
-  - Returns sentiment analysis for each article
-
-- `POST /api/generate_speech`: Converts text to Hindi speech
-  - Request body: `{"text": "summarized_text"}`
-  - Returns a URL to the generated audio file
-  
-- `POST /api/complete_analysis`: Performs complete analysis including fetching news, sentiment analysis, and generating speech
-  - Request body: `{"company_name": "Tesla"}`
-  - Returns complete analysis results
-
-## Assumptions & Limitations
-
-- The application scrapes publicly available news articles that don't require JavaScript rendering
-- Sentiment analysis accuracy depends on the model used and may not capture context-specific nuances
-- Hindi translation and TTS quality may vary based on technical terms
-- The application requires an internet connection to fetch news articles and use cloud-based services
-
-## Troubleshooting
-
-If you encounter any issues:
-
-1. Run the healthcheck script to verify all dependencies are working:
-   ```
-   python healthcheck.py
-   ```
-
-2. Check that you have all the required system dependencies installed (espeak, ffmpeg).
-
-3. If you encounter issues with specific components:
-   - Translation service requires an internet connection
-   - Text-to-speech uses gTTS by default, but falls back to pyttsx3 if needed
-   - Transformer models may take time to download on first run
-
-## Deployment
-
-This application is deployed on Hugging Face Spaces: [Link to deployment]
-
-### Using Docker
-
-You can also run the application using Docker:
-
-```
-docker build -t news-summarization-tts .
-docker run -p 8501:8501 -p 8000:8000 news-summarization-tts
-```
-
-## Future Improvements
-
-- Add support for more languages
-- Implement advanced NLP techniques for better summarization
-- Improve the user interface with more interactive visualizations
-- Add historical data analysis for tracking sentiment over time
-- Enhance TTS quality with dedicated Hindi speech models
-
-## License
-
-MIT 
+---
+title: News Summarization and TTS
+emoji: 📰
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.27.0
+app_file: app_spaces.py
+pinned: false
+---
+
+# News Summarization and Text-to-Speech Application
+
+This application extracts news articles about companies, performs sentiment analysis, conducts comparative analysis, and generates a text-to-speech output in Hindi.