🛡️ FraudRadar AI

AI-Powered Scam Detection Platform

Detect • Analyze • Prevent Digital Fraud

🚀 Live Demo

https://fraudradar-v2-yash.up.railway.app

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📖 Overview

FraudRadar AI is a production-ready scam detection platform designed to identify fraudulent digital content using Artificial Intelligence.

The application allows users to detect scams in:

• 📩 Messages
• 🌐 URLs
• 📱 QR Codes
• 🖼 Screenshots
• 🤖 AI Chat Assistance

The project is built using Python, Reflex, Supabase, Docker and Railway with a modern cloud-native deployment workflow.

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✨ Features

• 🔐 Email Authentication
• 🤖 AI Scam Detection
• 🌐 URL Analysis
• 📱 QR Code Scanner
• 🖼 OCR Screenshot Analysis
• 💬 AI Assistant
• 📊 Dashboard Analytics
• 📜 Scan History
• 👤 User Profile
• ⚙ Settings
• 📱 Responsive Design
• ☁ Cloud Deployment

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🛠 Tech Stack

Category	Technologies
Frontend	Reflex, Python, Tailwind CSS
Backend	Python, Reflex
AI	Groq API
Database	Supabase, PostgreSQL
OCR	Tesseract OCR, Pyzbar
Deployment	Docker, Railway
Version Control	Git, GitHub

🏗 Architecture

                User
                  │
                  ▼
        FraudRadar AI Website
                  │
                  ▼
         Reflex Frontend (Python)
                  │
                  ▼
          Reflex Backend Server
             │             │
             ▼             ▼
        Groq AI API    Supabase Auth
                            │
                            ▼
                      PostgreSQL Database

⚙️ Installation

Clone the repository

git clone https://github.com/Yashr4635/FraudRadar-v2.git

Move into the project

cd FraudRadar_v2

Create a virtual environment

python -m venv .venv

Activate it

macOS/Linux

source .venv/bin/activate

Windows

.venv\Scripts\activate

Install dependencies

pip install -r requirements.txt

Run the application

reflex run

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🐳 Docker

Build

docker compose build

Run

docker compose up

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☁️ Deployment

FraudRadar AI is deployed using Railway and containerized with Docker.

Deployment workflow:

GitHub
    │
    ▼
Docker Image
    │
    ▼
Railway
    │
    ▼
Live Application

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🔐 Authentication

Authentication is powered by Supabase Authentication.

Supported features:

• Email Registration
• Secure Login
• Session Management
• Protected Dashboard

📁 Project Structure

FraudRadar_v2/
│
├── assets/
├── screenshots/
├── fraudradar_ai_scam_detection_v2/
│   ├── components/
│   ├── states/
│   └── ...
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
├── rxconfig.py
└── README.md

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🧠 Challenges Solved

During development, several real-world engineering challenges were addressed:

• Integrated Supabase Authentication
• Debugged Google OAuth callback issues
• Configured Railway environment variables
• Dockerized the application
• Connected Groq AI API
• Implemented OCR using Tesseract
• Built a responsive dashboard
• Fixed deployment and production issues

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🚀 Future Improvements

• Browser Extension
• Android Application
• iOS Application
• Admin Dashboard
• Community Scam Reporting
• Multi-language Support
• AI Threat Intelligence
• Advanced ML Fraud Detection

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👨‍💻 Author

**DS Yashaswi **

B.Tech Computer Science & Data Science

• AI
• Full Stack Development
• Data Science
• Cybersecurity

GitHub:

https://github.com/Yashr4635

LinkedIn:

www.linkedin.com/in/ds-yashaswi-662533318

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⭐ Support

If you found this project interesting, consider giving it a ⭐ on GitHub.

Your support motivates future development.