AgriCLIP – Multilingual AI System

Prerequisites:

- Node.js (v18 or later)

- Python 3.10+

- MongoDB

- Git

1. Clone the repository:

git clone https://github.com/shabarigirishmeela/AgriLiv-T5-

cd AgriLiv-T5-

2. Install backend dependencies:

cd crop-cure-chat-backend

npm install

3. Configure environment variables:

Create a .env file in the backend directory and add the required environment variables such as MongoDB connection string, JWT secret, API keys, and FastAPI service URL.

4. Start the backend server:

npm run dev

5. Install and start the AI service:

Install the required Python dependencies using:

pip install -r requirements.txt

Start the FastAPI server:

uvicorn app:app --reload

6. Install frontend dependencies:

Open a new terminal and run:

cd crop-cure-chat-frontend

npm install

7. Start the frontend:

npm run dev

8. Ensure MongoDB is running.

9. Open the application in your browser:

http://localhost:5173

1. Open the application in your browser after starting the frontend and backend services.

2. Register a new account or log in using your credentials.

3. From the dashboard, choose the analysis category:

  - Plant Disease Detection

  - Livestock Analysis

  - Fish Species Analysis

4. Upload a supported image (JPG, JPEG, or PNG).

5. Click the Analyze button to start the AI prediction.

6. The system processes the image using the AgriCLIP model and displays:

  - Predicted class

  - Confidence score

  - AI-generated diagnosis

  - Cause, Prevention, and Cure recommendations

7. Use the integrated AI chatbot to ask questions related to the prediction or seek additional agricultural guidance.

8. View previous analyses from the History section.

9. Download or save the generated report for future reference.

Expected Output:

- Accurate image classification for plants, livestock, and fish species.

- AI-generated diagnostic report with Cause, Prevention, and Cure.

- Confidence score for each prediction.

- Interactive chatbot assistance.

- Stored prediction history for authenticated users.

Operating System:

- Windows 10/11, Ubuntu 20.04+ (Linux), or macOS

Hardware Requirements:

- Processor: Intel Core i5 (or equivalent) or higher

- RAM: Minimum 8 GB (16 GB recommended)

- Storage: Minimum 10 GB free disk space

- GPU: NVIDIA GPU (optional, recommended for faster AI inference)

Software Requirements:

- Node.js v18 or later

- Python 3.10 or later

- MongoDB Community Server or MongoDB Atlas

- Git

- npm (comes with Node.js)

- Visual Studio Code (recommended)

Web Browser:

- Google Chrome (recommended)

- Microsoft Edge

- Mozilla Firefox

Network:

- Internet connection required for API access and downloading dependencies.