sentinelAI

sentinelAI is a full-stack cyber safety platform that combines AI threat analysis with community-driven fraud intelligence.

It helps users:

• Analyze suspicious URLs, messages, prompts, and screenshots
• Report confirmed threats to a shared intelligence pool
• See if others encountered the same attack pattern
• View Scams/Frauds Happening In Your Area on a dedicated page

Live App Links

• Frontend (Public App): https://sentinelai-2.onrender.com
• Backend API Health: https://sentinelai-backend-server.onrender.com/api/health
• AI Service Health: https://sentinelai-1-ehft.onrender.com/health

App Screenshots

Desktop View

Mobile View

Project Structure

• Frontend/app : React + Vite dashboard
• Backend : Node.js + Express API + MongoDB
• Ai-Services : FastAPI AI analysis service (Gemini-powered)

Core Features

• Text and URL threat scanner
• Screenshot fake-login detection
• User authentication with JWT
• Threat reporting workflow
• Threat History and Community Intelligence
• Dedicated page for area-level scam/fraud intelligence
• User segment + location-aware aggregation

Local Development

1. Prerequisites

• Node.js 20+
• Python 3.10+
• MongoDB Atlas (or compatible MongoDB)
• Google Gemini API key

2. Configure Environment Variables

Copy each example file and fill real values:

• Backend/.env.example -> Backend/.env
• Ai-Services/.env.example -> Ai-Services/.env
• Frontend/app/.env.example -> Frontend/app/.env

Backend env (Backend/.env)

• PORT=5000
• MONGO_URI=...
• JWT_SECRET=...
• AI_SERVICE_URL=http://127.0.0.1:8000
• CORS_ORIGINS=http://localhost:5173,http://127.0.0.1:5173,https://*.onrender.com

AI Service env (Ai-Services/.env)

• GEMINI_API_KEY=...
• GEMINI_MODEL=gemini-2.5-flash
• ALLOWED_ORIGINS=http://localhost:5000

Frontend env (Frontend/app/.env)

• VITE_API_URL=http://localhost:5000/api

3. Install Dependencies

Backend

cd Backend
npm install

Frontend

cd Frontend/app
npm install

AI Service

cd Ai-Services
python -m venv .venv
# Windows
.\.venv\Scripts\activate
pip install -r requirements.txt

4. Run Services

Run each service in its own terminal.

AI Service

cd Ai-Services
.\.venv\Scripts\activate
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Backend

cd Backend
npm run dev

Frontend

cd Frontend/app
npm run dev

Open http://localhost:5173.

API Health Checks

• Backend: GET /api/health
• AI Service: GET /health and GET /ping

Deployment Guide (Render + Railway)

Yes, this setup works:

• Frontend on Render (Static Site)
• Backend on Railway (Node service)
• AI Service on Railway (Python service)

Recommended deployment order:

1. AI Service (Railway)
2. Backend API (Railway)
3. Frontend (Render)

1) Railway: Deploy AI Service

• Service root directory: Ai-Services
• Start command: uvicorn main:app --host 0.0.0.0 --port $PORT
• Procfile included: Ai-Services/Procfile

Set Railway environment variables:

• GEMINI_API_KEY
• GEMINI_MODEL=gemini-2.5-flash
• ALLOWED_ORIGINS=<your-backend-railway-url>

Health checks:

• GET /health
• GET /ping

2) Railway: Deploy Backend Service

• Service root directory: Backend
• Start command: npm run start
• Procfile included: Backend/Procfile

Set Railway environment variables:

• PORT=5000 (or Railway default)
• MONGO_URI=<your-mongodb-uri>
• JWT_SECRET=<strong-secret>
• AI_SERVICE_URL=<your-ai-railway-url>
• CORS_ORIGINS=<your-render-frontend-url>

Health check:

• GET /api/health

3) Render: Deploy Frontend Static Site

• Root directory: Frontend/app
• Build command: npm run build
• Publish directory: dist

Set Render environment variables:

• VITE_API_URL=<your-backend-railway-url>/api

Important for React Router routes like /area-intelligence:

• Add a rewrite rule in Render static site settings:
• Source: /*
• Destination: /index.html
• Action: Rewrite

Without this rewrite, direct URL refresh on /area-intelligence can return 404.

One-Command Local Run (Docker Compose)

Docker support is included for all 3 services.

Files added:

• docker-compose.yml
• Backend/Dockerfile
• Ai-Services/Dockerfile
• Frontend/app/Dockerfile

Setup:

1. Copy .env.compose.example to .env at repo root.
2. Fill required values (MONGO_URI, JWT_SECRET, GEMINI_API_KEY, etc.).
3. Run:

docker compose up --build

Local URLs:

• Frontend: http://localhost:5173
• Backend: http://localhost:5000/api/health
• AI Service: http://localhost:8000/health

Production Readiness Checklist

• Environment variables configured in all 3 services
• CORS locked to trusted origins only
• MongoDB network access and credentials verified
• Health endpoints responding after deploy
• Login/Register flow working in production
• Threat scan, report, history, community intel, and area intel verified
• Render rewrite rule added for SPA routes (/* -> /index.html)

Frontend Branding

• Project name updated to sentinelAI
• Logo source from Frontend/app/public/logo.png
• Browser tab icon and page branding updated
• Dedicated route for area intelligence: /area-intelligence

Scripts

Backend

• npm run dev
• npm run start

Frontend

• npm run dev
• npm run build
• npm run preview

AI Service

• uvicorn main:app --reload