🔍 TruthLens

AI-Powered Fake News Detection with Multi-Source Verification

Cut through the noise. Verify the truth. TruthLens uses advanced AI and multi-source cross-referencing to help you identify misinformation in real-time.

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✨ Features

🤖 AI-Powered Analysis - Leverages GPT-4/Gemini with RAG (Retrieval Augmented Generation) for intelligent fact-checking

📊 Multi-Source Verification - Cross-references claims against Reddit discussions, Wikipedia, and multiple news APIs

🎯 Credibility Scoring - Provides transparent scoring breakdown: source credibility, content consistency, and fact verification

📈 Interactive Dashboard - Beautiful Streamlit interface with real-time visualizations and detailed breakdowns

💾 Persistent History - SQLite database tracks all analyzed articles and user feedback

🐳 Docker Ready - Full containerization support for easy deployment

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🏗️ Architecture

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   Streamlit     │────▶│    FastAPI      │────▶│  Fake News      │
│   Frontend      │     │    Backend      │     │  Agent          │
└─────────────────┘     └─────────────────┘     └────────┬────────┘
                                                         │
                    ┌────────────────────────────────────┼────────────────────────────────────┐
                    │                                    │                                    │
            ┌───────▼───────┐                   ┌───────▼───────┐                   ┌───────▼───────┐
            │  AI Service   │                   │ Reddit Service│                   │ News Service  │
            │  (LLM + RAG)  │                   │               │                   │               │
            └───────────────┘                   └───────────────┘                   └───────────────┘
                    │                                                                        │
            ┌───────▼───────┐                                                       ┌───────▼───────┐
            │  Wikipedia    │                                                       │   Database    │
            │  Service      │                                                       │   (SQLite)    │
            └───────────────┘                                                       └───────────────┘

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🚀 Quick Start

Prerequisites

• Python 3.10+
• API Keys (at least one AI provider required):
  • OpenAI API Key or Google Gemini API Key
  • Reddit API credentials (optional, for Reddit analysis)
  • NewsAPI / GNews / NewsData API keys (optional, for news verification)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/truthlens.git
   cd truthlens

2. Set up environment (Windows PowerShell)

   ./quick_setup.ps1

   Or manually:

   python -m venv venv
   source venv/bin/activate  # Windows: .\venv\Scripts\Activate
   pip install -r requirements.txt
   python -m spacy download en_core_web_sm

3. Configure API keys

   Create a .env file in the root directory:

   # AI Provider (choose one: "openai" or "gemini")
   AI_PROVIDER=gemini
   
   # API Keys
   GOOGLE_API_KEY=your_gemini_api_key
   OPENAI_API_KEY=your_openai_api_key
   
   # Reddit API (optional)
   REDDIT_CLIENT_ID=your_reddit_client_id
   REDDIT_CLIENT_SECRET=your_reddit_secret
   REDDIT_USERNAME=your_reddit_username
   REDDIT_USER_AGENT=TruthLens/1.0
   
   # News APIs (optional - at least one recommended)
   NEWSAPI_KEY=your_newsapi_key
   GNEWS_API_KEY=your_gnews_key
   NEWS_DATA_API_KEY=your_newsdata_key
   
   # Database
   DATABASE_URL=sqlite:///./app/data/truthlens.db

4. Run the application

   python run.py

   Access the application:

   • 🖥️ Frontend: http://localhost:8501
   • 📡 API Docs: http://localhost:8000/docs

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🐳 Docker Deployment

# Build and run with Docker Compose
docker-compose up --build

# Or build manually
docker build -t truthlens .
docker run -p 8000:8000 -p 8501:8501 --env-file .env truthlens

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📡 API Reference

Verify News Article

POST /verify
Content-Type: application/json

{
  "title": "Breaking News Headline",
  "content": "Full article content here..."
}

Response:

{
  "verdict": "REAL",
  "is_fake": false,
  "score": 78,
  "confidence": 85,
  "processing_time": 3.42,
  "details": {
    "source_credibility": 25,
    "content_consistency": 28,
    "fact_verification": 25
  }
}

Submit Feedback

POST /feedback
Content-Type: application/json

{
  "article_id": 1,
  "is_correct": true,
  "feedback_text": "Accurate analysis"
}

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🔧 Configuration

Variable	Description	Default
AI_PROVIDER	AI backend: openai or gemini	gemini
USE_GPU	Enable GPU acceleration	False
WIKIPEDIA_LANGUAGE	Wikipedia language code	en
MAX_WIKIPEDIA_RETRIES	Retry attempts for Wikipedia	3

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📁 Project Structure

truthlens/
├── app/
│   ├── agents/
│   │   └── fake_news_agent.py    # Orchestration logic
│   ├── api/
│   │   └── app.py                # FastAPI endpoints
│   ├── frontend/
│   │   └── streamlit_app.py      # Streamlit UI
│   ├── models/
│   │   └── database.py           # SQLAlchemy models
│   ├── services/
│   │   ├── ai_service.py         # LLM & RAG analysis
│   │   ├── news_service.py       # News API integration
│   │   ├── reddit_service.py     # Reddit analysis
│   │   └── wikipedia_service.py  # Wikipedia lookup
│   └── utils/
│       └── config.py             # Environment configuration
├── docs/                         # Documentation
├── tests/                        # Test suite
├── docker-compose.yml
├── Dockerfile
├── requirements.txt
└── run.py                        # Application launcher

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🧪 Testing

# Run all tests
pytest tests/

# Run with coverage
pytest tests/ --cov=app --cov-report=html

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🤝 Contributing

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

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📜 License

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

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🙏 Acknowledgments

• LangChain for the powerful LLM orchestration framework
• Hugging Face for transformer models
• Streamlit for the elegant frontend framework
• FastAPI for the blazing-fast API framework

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Built with ❤️ to fight misinformation