https://github.com/programming-sai/fertilizer-recommendation-system-client-server
The Fertilizer Recommendation System is a web-based application designed to provide tailored fertilizer recommendations for different crops based on various factors like growth stage, soil properties, climate, and more. It consists of a frontend built with React and a backend powered by Flask, using Prolog for inference.
https://github.com/programming-sai/fertilizer-recommendation-system-client-server
agric backend docker docker-compose expert-system fertilizers frontend fullstack prolog react
Last synced: 6 months ago
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The Fertilizer Recommendation System is a web-based application designed to provide tailored fertilizer recommendations for different crops based on various factors like growth stage, soil properties, climate, and more. It consists of a frontend built with React and a backend powered by Flask, using Prolog for inference.
- Host: GitHub
- URL: https://github.com/programming-sai/fertilizer-recommendation-system-client-server
- Owner: Programming-Sai
- Created: 2025-01-31T10:05:33.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-05T10:54:26.000Z (8 months ago)
- Last Synced: 2025-02-13T10:47:06.296Z (8 months ago)
- Topics: agric, backend, docker, docker-compose, expert-system, fertilizers, frontend, fullstack, prolog, react
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Fertilizer Recommendation System (Client & Server)
## Overview
The **Fertilizer Recommendation System** is a web-based application designed to provide tailored fertilizer recommendations for different crops based on various factors like growth stage, soil properties, climate, and more. It consists of a **frontend** built with React and a **backend** powered by Flask, using Prolog for inference.
### Features
- **Real-time Fertilizer Recommendations** based on multiple parameters.
- **Interactive UI** allowing users to input their crop and environmental data.
- **Backend Inference Engine** using Prolog to calculate optimal fertilizer recommendations.
## Tech Stack
- **Frontend**: React, JSX, Vite (with `fetch` for API requests)
- **Backend**: Flask, Python, SWI-Prolog
- **Docker**: For containerization and deployment
## Installation
To get started with the project locally, follow the steps below:
### 1. Clone the Repository
```bash
git clone --recurse-submodules https://github.com/Programming-Sai/Fertilizer-Recommendation-System-Client-Server.git
cd Fertilizer-Recommendation-System-Client-Server
```
> [!NOTE]
> If you have already cloned the repository without --recurse-submodules, you can initialize the submodules separately with:
```bash
git submodule update --init --recursive
```
## Repositories
- **Frontend**: [Fertilizer Recommendation System Client](https://github.com/Programming-Sai/Fertilizer-Recommendation-System.git)
- **Backend**: [Fertilizer Recommendation System Server](https://github.com/Programming-Sai/Fertilizer-Recommendation-Engine-PROLOG.git)
### 2. Set Up the Backend
1. Navigate to the `server` directory.
```bash
cd server
```
2. Install the necessary dependencies:
```bash
pip install -r requirements.txt
```
3. Start the backend server:
```bash
python scripts/server.py
```
The backend should now be running at `http://localhost:5000`.
### 3. Set Up the Frontend
1. Navigate to the `client` directory.
```bash
cd client
```
2. Install dependencies:
```bash
npm install
```
3. Start the frontend:
```bash
npm run dev
```
The frontend should now be accessible at `http://localhost:3000`.
### 4. Running the Full Application Using Docker
1. From the root directory, run:
```bash
docker-compose up --build
```
- To stop it you can run
```bash
# Ctrl + C # If running in the foreground
docker-compose down # To stop and remove containers
```
This will build and run the frontend and backend containers.
## Usage
Once both the frontend and backend are running, you can navigate to `http://localhost:3000` in your browser and start using the application.
### How to Use
- Navigate to `Predict` from the Navbar to get to the parameters form
- Input the required crop, environmental and soil parameters.
- Click **Get Recommendations** to receive fertilizer recommendations tailored to your inputs.