Fraud Detection in Voting

This project is a full-stack application that detects fraudulent voting activities using machine learning and biometric verification. The system consists of a frontend (Streamlit) and a backend (FastAPI) with a SQLite database for secure and efficient fraud detection.

Live Demo

• Frontend (Streamlit UI): Live App
• Backend (FastAPI API): API Documentation

Available Scripts

npm start

Runs the FastAPI backend in development mode.
Open http://127.0.0.1:8000 to access the API locally.

streamlit run app.py

Runs the Streamlit frontend in development mode.
It opens automatically in your default web browser.

pip install -r requirements.txt

Installs all necessary dependencies for both frontend and backend.
Ensure you run this command in both src/frontend and src/backend directories before starting the project.

uvicorn main:app --reload

Starts the FastAPI backend server with live reloading.
Use this when developing and testing API endpoints.

Features

• ✅ Secure vote submission with biometric authentication.
• ✅ Fraud detection using machine learning models.
• ✅ Interactive UI built with Streamlit.
• ✅ FastAPI-based backend for handling vote data.
• ✅ SQLite database for storing votes.
• ✅ CORS support for frontend-backend communication.

Technologies Used

Frontend

• Streamlit (for UI)
• Requests (for API communication)

Backend

• FastAPI (for API development)
• SQLite (for database storage)
• SQLAlchemy (ORM for database interactions)
• Pydantic (for data validation)
• CORS Middleware (for cross-origin access)

How It Works

1. Users enter vote details (Voter ID, Candidate, Location, Biometric Hash, Timestamp) in the Streamlit frontend.
2. Frontend sends the vote data to the FastAPI backend, which processes and stores it in the SQLite database.
3. Fraud detection algorithms analyze voting patterns and flag suspicious activities.
4. Results are displayed in the frontend for easy visualization.

Screenshots

Frontend (Streamlit UI)

Backend (FastAPI with Database)

Deployment

The project is deployed using:

• Frontend: Streamlit Cloud
• Backend: Render

Steps to Deploy

1. Frontend Deployment:

   • Push frontend code to GitHub.
   • Deploy using Streamlit Sharing.
   • Update the API URL in the frontend code.

2. Backend Deployment:

   • Push backend code to GitHub.
   • Deploy using Render.
   • Set up the database and API endpoints.

Future Enhancements

• 🔹 Real-time fraud detection.
• 🔹 Advanced ML models (BERT, RoBERTa, etc.).
• 🔹 Blockchain-based secure voting.
• 🔹 Cloud-based authentication & storage.

Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

License

This project is licensed under the MIT License.

Contact

For any inquiries, reach out via [[email protected]] or open an issue in the repository.