Skip to content

README.md

Latest commit

 

History

History
97 lines (73 loc) · 1.85 KB

README.md

File metadata and controls

97 lines (73 loc) · 1.85 KB

AgriScan Backend

AI-powered crop disease diagnosis backend using Google Gemini Vision API and IPFS storage.

Features

  • AI Image Analysis - Diagnose crop diseases using Gemini Vision API
  • AI Chatbot - Agricultural advisor powered by Gemini Pro
  • IPFS Storage - Decentralized image storage via NFT.Storage
  • Blockchain Ready - Returns IPFS CIDs for on-chain storage

API Endpoints

Health Check

GET /health

Returns server status.

Analyze Image

POST /analyze
Body: {
  "imageBase64": "data:image/jpeg;base64,...",
  "plantType": "Tomato" (optional)
}

Returns diagnosis, confidence, and IPFS CID.

Chat

POST /chat
Body: {
  "message": "How do I treat early blight?",
  "context": "Tomato plant with brown spots" (optional)
}

Returns AI agricultural advice.

Setup

  1. Install dependencies

    npm install

  2. Configure environment variables

    cp env.example .env

    Edit .env and add:

    • GEMINI_API_KEY - Your Google Gemini API key
    • IPFS_API_KEY - Your NFT.Storage API key (optional)

  3. Run locally

    npm start
# or for development
npm run dev

  4. Test

    curl http://localhost:3000/health

Deployment

Vercel

This backend is configured for Vercel deployment:

  1. Push to GitHub
  2. Import to Vercel
  3. Set environment variables in Vercel dashboard
  4. Deploy!

See ../DEPLOY_TO_VERCEL.md for detailed instructions.

Environment Variables

  • GEMINI_API_KEY - Required for AI analysis
  • IPFS_API_KEY - Optional, for IPFS storage
  • PORT - Server port (default: 3000)
  • NODE_ENV - Environment (production/development)

Tech Stack

  • Express.js - Web framework
  • Google Gemini AI - Vision and chat models
  • NFT.Storage - IPFS storage
  • CORS - Cross-origin support

License

MIT