Farmers Support Platform

A comprehensive MERN stack application designed to help farmers access government schemes, get instant support through an AI-powered chatbot, and manage their profiles efficiently.

Table of Contents

• Features
• Tech Stack
• Prerequisites
• Installation
• Configuration
• Running the Application
• Deployment
• Project Structure
• API Documentation
• Contributing
• License

Features

• AI-Powered Chatbot - Get instant answers to farming-related queries
• WhatsApp Bot Integration - Support via WhatsApp messaging
• Government Schemes - Browse and search government schemes for farmers
• Interactive Maps - Location-based services using Google Maps
• User Authentication - Secure JWT-based authentication with email verification
• User Profiles - Manage personal information and preferences
• Multilingual Support - Available in English and Hindi
• Text-to-Speech - Audio support for accessibility
• File Upload - Cloudinary integration for image uploads

Tech Stack

Frontend

• React 19 - UI library
• React Router v7 - Client-side routing
• Tailwind CSS v4 - Utility-first CSS framework
• Flowbite React - UI components
• Vite - Build tool
• i18next - Internationalization

Backend

• Node.js - Runtime environment
• Express 5 - Web framework
• MongoDB - Database
• Mongoose - ODM for MongoDB
• JWT - Authentication
• Cloudinary - File storage
• Google Cloud TTS - Text-to-speech
• SendGrid/Nodemailer - Email service

AI/ML

• LangChain - AI chatbot framework
• ChromaDB - Vector database for chatbot

Prerequisites

Before you begin, ensure you have the following installed:

• Node.js (v18 or higher) - Download
• Python (v3.8 or higher) - Required for AI chatbot
• MongoDB Atlas Account - Sign up
• Git - Download

Installation

1. Clone the Repository

git clone https://github.com/yourusername/farmers-support-platform.git
cd farmers-support-platform

2. Install Server Dependencies

cd server
npm install

3. Install Client Dependencies

cd ../client/Learning
npm install

4. Install Python Dependencies (for AI Chatbot)

cd ../../
pip install -r requirements.txt

⚙️ Configuration

1. Server Environment Variables

Create a .env file in the server directory:

cd server
cp .env.example .env

Edit the .env file and configure:

# Server Configuration
PORT=3000

# MongoDB Configuration
MONGODB_URL=mongodb+srv://your-username:your-password@your-cluster.mongodb.net
DB_NAME=farmers

# CORS Configuration
CORS_ORIGIN=http://localhost:5173

# JWT Secret (generate using: node -e "console.log(require('crypto').randomBytes(32).toString('hex'))")
JWT_SECRET=your-secure-jwt-secret

# Cloudinary Configuration (https://cloudinary.com)
CLOUDINARY_CLOUD_NAME=your-cloud-name
CLOUDINARY_API_KEY=your-api-key
CLOUDINARY_API_SECRET=your-api-secret

Getting Configuration Values:

MongoDB:

1. Go to MongoDB Atlas
2. Create a free cluster
3. Click "Connect" → "Connect your application"
4. Copy the connection string and replace <password> with your database password

Cloudinary:

1. Sign up at Cloudinary
2. Go to Dashboard
3. Copy Cloud Name, API Key, and API Secret

JWT Secret: Generate a secure random string:

node -e "console.log(require('crypto').randomBytes(32).toString('hex'))"

Google Cloud TTS (Optional):

1. Create a project in Google Cloud Console
2. Enable Text-to-Speech API
3. Create a service account and download JSON key
4. Place the key file in server/keys/tts-sa.json

2. Client Environment Variables

Create a .env file in the client/Learning directory:

cd client/Learning
cp .env.example .env

Edit the .env file:

# Google Maps API Key
VITE_API_KEY=your-google-maps-api-key

# Backend API URL
VITE_API_BASE_URL=http://localhost:3000/api/v1

Google Maps API:

1. Go to Google Cloud Console
2. Enable Maps JavaScript API
3. Create credentials (API Key)
4. Copy the API key

Running the Application

Option 1: Run All Services Separately

1. Start MongoDB (if running locally)

mongod

2. Start the Backend Server

cd server
npm run dev
# Server will start on http://localhost:3000

3. Start the Frontend

cd client/Learning
npm run dev
# Frontend will start on http://localhost:5173

4. Start the AI Chatbot (Optional)

python chatbot_service.py

5. Start WhatsApp Bot (Optional)

python whatsapp_bot.py

Option 2: Using Concurrent Processes

You can create a root package.json to run everything together:

{
  "scripts": {
    "dev": "concurrently \"npm run server\" \"npm run client\"",
    "server": "cd server && npm run dev",
    "client": "cd client/Learning && npm run dev"
  }
}

Then run:

npm install -g concurrently
npm run dev

Deployment

Ready to deploy your application to production? We've prepared a comprehensive deployment guide.

Quick Deployment Guide

Recommended Stack:

• Frontend: Vercel (Free)
• Backend: Render (Free)
• Database: MongoDB Atlas (Already set up)

Deployment Steps:

1. Deploy Backend to Render

   • Sign up at render.com
   • Connect your GitHub repository
   • Set root directory to server
   • Add environment variables
   • Deploy!

2. Deploy Frontend to Vercel

   • Sign up at vercel.com
   • Import your GitHub repository
   • Set root directory to client/Learning
   • Add environment variables
   • Deploy!

3. Update CORS Settings

   • Update backend CORS_ORIGIN with your Vercel URL

Complete Deployment Guide →

The detailed guide includes:

• Step-by-step instructions with screenshots
• Environment variable configuration
• Troubleshooting tips
• Alternative deployment options
• Free tier optimization

Project Structure

farmers-support-platform/
├── server/                      # Backend application
│   ├── controllers/             # Request handlers
│   ├── models/                  # Mongoose models
│   ├── routes/                  # API routes
│   ├── middleware/              # Custom middleware
│   ├── utils/                   # Utility functions
│   ├── keys/                    # Service account keys (git-ignored)
│   ├── uploads/                 # Temporary file uploads (git-ignored)
│   ├── .env                     # Environment variables (git-ignored)
│   └── package.json             # Server dependencies
│
├── client/Learning/             # Frontend application
│   ├── src/
│   │   ├── components/          # React components
│   │   │   ├── Auth/            # Authentication components
│   │   │   ├── Chatbot/         # Chatbot interface
│   │   │   ├── Dashboard/       # Dashboard components
│   │   │   ├── Maps/            # Google Maps components
│   │   │   └── SchemesList/     # Government schemes
│   │   ├── context/             # React Context (Auth, Toast)
│   │   ├── hooks/               # Custom hooks
│   │   ├── config/              # Configuration files
│   │   └── utils/               # Utility functions
│   ├── public/locales/          # i18n translations
│   ├── .env                     # Environment variables (git-ignored)
│   └── package.json             # Client dependencies
│
├── chatbot_service.py           # AI chatbot service
├── whatsapp_bot.py              # WhatsApp bot integration
├── vector.py                    # Vector database operations
├── process_schemes.py           # Scheme processing utilities
├── requirements.txt             # Python dependencies
├── .gitignore                   # Git ignore rules
└── README.md                    # This file

API Documentation

Authentication Endpoints

Register User

POST /api/v1/auth/register
Content-Type: application/json

{
  "email": "user@example.com",
  "password": "password123",
  "name": "John Doe"
}

Login

POST /api/v1/auth/login
Content-Type: application/json

{
  "email": "user@example.com",
  "password": "password123"
}

Forgot Password

POST /api/v1/auth/forgot-password
Content-Type: application/json

{
  "email": "user@example.com"
}

User Endpoints

Get Profile

GET /api/v1/users/profile
Authorization: Bearer <token>

Update Profile

PATCH /api/v1/users/profile
Authorization: Bearer <token>
Content-Type: multipart/form-data

{
  "name": "John Doe Updated",
  "avatar": <file>
}

Schemes Endpoints

Get All Schemes

GET /api/v1/schemes

Search Schemes

GET /api/v1/schemes/search?q=farming

Chatbot Endpoints

Send Message

POST /api/v1/chatbot/message
Content-Type: application/json
Authorization: Bearer <token>

{
  "message": "What are the benefits of organic farming?"
}

TTS Endpoints

Convert Text to Speech

POST /api/v1/tts/synthesize
Content-Type: application/json

{
  "text": "Hello farmer",
  "language": "en-US"
}

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the ISC License.

Authors

• Keshav - Initial work

Acknowledgments

• Government of India for scheme data
• MongoDB for database services
• Cloudinary for file storage
• Google Cloud for Maps and TTS services
• All contributors who help improve this project

Contact

For questions or support, please open an issue on GitHub.

---

Made with ❤️ for farmers