📰 Intelligent News Feed Application

A personalized news application that uses AI-powered content recommendation and transparent user profiling to deliver relevant news articles tailored to individual preferences.

🌟 Features

Core Functionality

Personalized News Feed: AI-driven article recommendations based on user behavior and preferences
Multi-Category Support: Business, Entertainment, Health, Science, Sports, Technology, and General news
Advanced Search: Keyword-based article filtering and discovery
User Authentication: Secure registration and login system with JWT tokens
Article Interaction: Save, like, and track reading activity
Real-time Updates: Scheduled news fetching with background updates

AI & Machine Learning

Transparent User Profiling: Clear visibility into how recommendations are generated
Interest Profile Building: Dynamic learning from user interactions (reads, likes, saves)
TF-IDF Content Analysis: Advanced text analysis for better content matching
Activity-Based Recommendations: Personalization based on reading patterns
Keyword Extraction: NLP-powered content understanding using NLTK

User Experience

Responsive Design: Mobile-friendly interface with modern UI components
Preference Management: Granular control over content categories and keywords
Data Transparency: Optional data sharing with clear consent mechanisms
Saved Articles: Personal article library with easy management
Location-Based Content: Geographic preference settings

🏗️ Architecture

Frontend

Framework: Dash (Python web framework)
Styling: Bootstrap components with custom CSS
Components: Modular UI components for reusability
State Management: Session-based authentication storage

Backend

API: FastAPI with automatic documentation
Authentication: JWT-based secure authentication
Database: MongoDB for user data and preferences
Scheduling: APScheduler for automated news fetching
AI Engine: Custom recommendation system with scikit-learn

External Services

News API: Real-time news data from newsapi.org
MongoDB: Document-based storage for flexibility

📂 Project Structure

news-app/
├── frontend/
│   ├── app.py              # Main Dash application
│   ├── components.py       # UI components (login, news cards, layouts)
│   ├── callbacks.py        # Interactive callbacks and event handlers
│   └── api_client.py       # Frontend API communication layer
├── backend/
│   ├── main.py             # FastAPI server with all endpoints
│   └── ai_model.py         # ML recommendation engine
├── database/
│   └── mongo_handler.py    # Database utilities
├── news/
│   └── news_fetcher.py     # News API integration with caching
├── assets/
│   ├── custom.css          # Custom styling
│   ├── favicon.ico         # Application icon
│   └── cache/              # News data caching
└── requirements.txt        # Python dependencies

🚀 Quick Start

Prerequisites

Python 3.8+
MongoDB (local or cloud instance)
News API key from newsapi.org

Installation

Clone the repository
```
git clone <repository-url>
cd news-app
```
Install dependencies
```
pip install -r requirements.txt
```

Environment Setup Create a .env file in the root directory:

NEWS_API_KEY=your_news_api_key_here
MONGO_URI=mongodb://localhost:27017/
SECRET_KEY=your_secret_key_here
API_BASE_URL=http://localhost:8000/api

Start MongoDB

# For local MongoDB
mongod

# Or use MongoDB Atlas (cloud)
# Update MONGO_URI in .env with your connection string

Run the Backend

cd backend
python main.py
# Backend runs on http://localhost:8000

Run the Frontend

cd frontend
python app.py
# Frontend runs on http://localhost:8050

Access the Application Open your browser and navigate to: http://localhost:8050

🔧 Configuration

Environment Variables

NEWS_API_KEY: Your News API key (required)
MONGO_URI: MongoDB connection string
SECRET_KEY: JWT secret key for authentication
API_BASE_URL: Backend API base URL

News API Setup

Visit newsapi.org
Register for a free API key
Add the key to your .env file

MongoDB Configuration

Local: Install MongoDB and run mongod
Cloud: Use MongoDB Atlas for cloud hosting
Docker: Run MongoDB in a Docker container

🤖 AI & Recommendation System

How It Works

User Profiling: Tracks interests based on article interactions
Content Analysis: Extracts keywords and analyzes article content
Similarity Matching: Uses TF-IDF for content similarity
Preference Learning: Adapts to user behavior over time

Transparency Features

Clear Explanations: Each recommendation shows why it was suggested
Interest Visibility: Users can see their extracted interests
Data Control: Granular privacy settings
Opt-in Features: Experimental features require explicit consent

🔗 API Endpoints

Authentication

POST /api/auth/register - User registration
POST /api/auth/login - User login

News

POST /api/news/fetch - Fetch filtered news
GET /api/news/personalized - Get personalized recommendations
GET /api/news/explore - Browse trending articles
GET /api/news/categories - Available news categories

User Management

GET /api/user/preferences - Get user preferences
PUT /api/user/preferences - Update preferences
POST /api/user/save-article/{id} - Save article
POST /api/user/like-article/{id} - Like article
GET /api/user/saved-articles - Get saved articles

🎨 Customization

Styling

Modify assets/custom.css for visual customization
Update components.py for layout changes
Customize color schemes and responsive breakpoints

AI Behavior

Adjust recommendation weights in ai_model.py
Modify keyword extraction parameters
Fine-tune similarity algorithms

🧪 Testing

Sample Data

The application includes fallback sample data when the News API is unavailable:

Automatically switches to cached content
Provides sample articles for development
Maintains functionality during API outages

Development Mode

Enable debug mode for detailed error messages
Use sample data by setting ALWAYS_USE_SAMPLE = True
Monitor console logs for troubleshooting

🔒 Security Features

JWT Authentication: Secure token-based authentication
Password Hashing: Werkzeug security for password protection
CORS Protection: Configured for frontend-backend communication
Input Validation: Pydantic models for API validation
Privacy Controls: Transparent data usage with user consent

📈 Performance

Caching

News Caching: Reduces API calls with intelligent caching
Session Storage: Efficient frontend state management
Background Updates: Scheduled news fetching reduces load times

Optimization

Pagination: Efficient data loading
Lazy Loading: Articles loaded as needed
Database Indexing: Optimized MongoDB queries

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Common Issues

API Key Issues: Ensure your News API key is valid and not rate-limited
MongoDB Connection: Verify MongoDB is running and connection string is correct
Port Conflicts: Ensure ports 8000 and 8050 are available

Getting Help

Check the console logs for detailed error messages
Verify all environment variables are set correctly
Ensure all dependencies are installed with correct versions

🚀 Future Enhancements

Real-time Notifications: Push notifications for breaking news
Social Features: Share articles and see what friends are reading
Advanced Analytics: Detailed reading statistics and insights
Mobile App: Native iOS and Android applications
Multilingual Support: Content in multiple languages
RSS Integration: Import from custom RSS feeds

📊 Technology Stack

Frontend: Dash, Bootstrap, JavaScript
Backend: FastAPI, Python
Database: MongoDB
AI/ML: scikit-learn, NLTK
Authentication: JWT
Deployment: Docker-ready
APIs: News API, RESTful architecture

Built with ❤️ for transparent and intelligent news consumption

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