DeepTruth - AI-Powered News Verification Platform

DeepTruth is a powerful backend service that leverages artificial intelligence to verify news articles and claims, helping users distinguish between factual and false information.

🌟 Features

• False News Detection: Analyzes news articles for potential misinformation
• Claim Verification: Verifies specific claims using AI and fact-checking
• MongoDB Integration: Efficient data storage and retrieval
• RESTful API: Well-structured endpoints for easy integration
• Docker Support: Containerized deployment for consistent environments
• AI-Powered Analysis: Uses Google Generative AI and Transformers for content analysis

🚀 Tech Stack

• Backend Framework: Django
• API Framework: Django REST Framework 3.12.4
• Database: MongoDB with Djongo 1.3.6
• AI/ML:
  • Google Generative AI 0.1.0rc1
  • Transformers 4.44.0
  • Scikit-learn 1.3.2
  • PyTorch 2.4.0
• Containerization: Docker & Docker Compose
• Other Tools:
  • BeautifulSoup4 4.12.3 for web scraping
  • Python-dotenv 1.0.1 for environment management
  • Requests 2.32.3 for HTTP operations
  • Gunicorn 20.1.0 for production server

🛠️ Prerequisites

• Docker and Docker Compose
• Python 3.10 or higher (if running locally)
• MongoDB Atlas account (for cloud database)
• sckit-learn libarary

🔧 Installation

1. Clone the repository:

git clone https://github.com/yourusername/deeptruth-backend.git
cd deeptruth-backend

2. Create a .env file in the root directory with the following variables:

# Google Gemini AI Configuration
GEMINI_API_KEY=your_gemini_api_key
BRAVE_API_KEY=your_brave_api_key

# MongoDB Configuration
MONGODB_NAME=deeptruth_db
MONGODB_URI=your_mongodb_uri
MONGODB_USERNAME=your_username
MONGODB_PASSWORD=your_password

# Django Configuration
DEBUG=True
SECRET_KEY=your_secret_key
ALLOWED_HOSTS=localhost,127.0.0.1

3. Build and start the Docker containers:

docker-compose up --build

📊 Data Models

FalseNews

• article_title: Title of the news article
• veracity: Boolean indicating if the news is false
• confidence_score: AI model's confidence in the verification
• explanation: Detailed explanation of the verification
• category: News category
• key_findings: JSON field containing key findings
• impact_level: Verification status (VERIFIED/MISLEADING/PARTIAL)
• sources: List of source URLs
• created_at: Creation timestamp
• updated_at: Last update timestamp

TrueNews

• Similar structure to FalseNews but with veracity defaulting to True
• Additional fields for storing verified true information

📝 API Endpoints

1. False News Detection

POST /api/false-news/

Request Body:
{
    "url": "https://example.com/news-article",
    "title": "Example News Title",
    "content": "Article content...",
    "source": "News Source",
    "published_date": "2024-03-21T12:00:00Z"
}

2. Claim Verification

POST /api/true-news/

Request Body:
{
    "claim": "The claim to verify",
    "context": "Additional context",
    "source": "Source of the claim",
    "language": "en"
}

🧪 Testing

Run the test suite:

docker-compose exec web python manage.py test

📚 Documentation

For detailed API documentation, visit:

http://localhost:8000/api/docs/

🔒 Security

• All API endpoints are protected with authentication
• Environment variables for sensitive data
• Secure MongoDB connection with authentication

🤝 Contributing

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

🙏 Acknowledgments

• Google Gemini AI for natural language processing capabilities
• MongoDB Atlas for database hosting
• Django and DRF communities for excellent frameworks