🔍 Factos API - Fake News Detection

A robust REST API and interactive web interface for fake news detection using state-of-the-art machine learning models.

🌟 Features

• Multiple ML Models: Choose from various pre-trained models:
  • Logistic Regression (with text readability and sentiment features)
  • Random Forest (using TF-IDF features)
  • Naive Bayes (using TF-IDF features)
• REST API: Built with FastAPI for high performance and easy integration
• Interactive UI: User-friendly Streamlit interface for testing models
• Real-time Analysis: Get instant predictions with confidence scores
• Model Insights: View detailed model information and performance metrics
• Docker Support: Easy deployment with Docker containers

🚀 Getting Started

You can run the application either using Docker or directly on your machine.

🐳 Using Docker (Recommended)

1. Prerequisites

   • Docker
   • Docker Compose

2. Running the Application

   # Clone the repository
   git clone https://github.com/ramcav/factos_api.git
   cd factos_api
   
   # Start the services
   docker-compose up -d
   
   # View logs (optional)
   docker-compose logs -f

   The services will be available at:

   • API: http://localhost:8002
   • Streamlit UI: http://localhost:8501

3. Stopping the Application

   docker-compose down

💻 Local Installation

There are two ways to run the application locally: using command-line instructions or using the provided shell scripts.

Option 1: Command Line Installation

1. Clone the repository

   git clone https://github.com/ramcav/factos_api.git
   cd factos_api

2. Set up and activate virtual environment

   # Create virtual environments for both services
   
   # For API
   cd api
   python -m venv venv
   
   # Activate virtual environment
   
   # On Unix/macOS:
   source venv/bin/activate
   
   # On Windows:
   .\venv\Scripts\activate
   
   # Install API dependencies
   pip install -r requirements.txt
   
   # Start the API server
   uvicorn src.main:app --host 0.0.0.0 --port 8002 --reload

   In a new terminal:

   # For Streamlit app
   cd demo
   python -m venv venv
   
   # Activate virtual environment
   # On Unix/macOS:
   source venv/bin/activate
   
   # On Windows:
   .\venv\Scripts\activate
   
   # Install Streamlit app dependencies
   pip install -r requirements.txt
   
   # Start the Streamlit app
   streamlit run app.py

The services will be available at:

• API: http://localhost:8002
• Streamlit UI: http://localhost:8501

To deactivate the virtual environments when done:

deactivate

Option 2: Using Shell Scripts

This will create a virtual environment, install dependencies and start the services.

1. Clone the repository

   git clone https://github.com/ramcav/factos_api.git
   cd factos_api

2. Make the scripts executable

   chmod +x api/run.sh
   chmod +x demo/run.sh

3. Start the services

   In the first terminal:

   cd api
   ./run.sh

   In a second terminal:

   cd demo
   ./run.sh

The services will be available at:

• API: http://localhost:8002
• Streamlit UI: http://localhost:8501

Stopping the Application

For both methods:

1. Press Ctrl+C in each terminal to stop the services
2. If using virtual environments, run deactivate to exit them

🎯 Usage

Via Web Interface

1. Open the Streamlit interface in your browser
2. Select a model from the sidebar
3. Enter or paste the text you want to analyze
4. Click "Analyze Text" to get the prediction
5. View results including:
   • Prediction (Fake/Real News)
   • Confidence Score
   • Model Details
   • Full Analysis Results

Via API

The API provides the following endpoints:

GET /api/models
# List all available models

GET /api/models/{model_id}
# Get detailed information about a specific model

POST /api/models/{model_id}/predict
# Get prediction for input text

Example API request:

curl -X POST "http://localhost:8002/api/models/1/predict" \
     -H "Content-Type: application/json" \
     -d '{"text": "Your news article text here"}'

🛠️ Architecture

• API Layer: FastAPI for robust REST endpoints
• ML Models: Scikit-learn based models for text classification
• Frontend: Streamlit for interactive user interface
• Data Processing: NLTK for text preprocessing and feature extraction

🔄 Environment Variables

The application supports the following environment variables:

• API_URL: API endpoint URL (default: "http://localhost:8002")

📊 Model Performance

Model	Accuracy	Features
Logistic Regression	73%	Text readability, sentiment
Random Forest	99%	TF-IDF
Naive Bayes	92%	TF-IDF

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

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

🙏 Acknowledgments

• FastAPI for the amazing web framework
• Streamlit for the intuitive UI components
• Scikit-learn for machine learning tools
• NLTK for natural language processing capabilities