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Welcome to the Plant Disease Classifier Deployment repository! This project integrates cutting-edge technologies to develop a sophisticated plant disease classifier. Leveraging TensorFlow for model creation and ReactJS for the web interface, this project offers an innovative solution for identifying plant diseases swiftly and accurately.

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Plant Disease Classifier Frontend

Welcome to the Plant Disease Classifier Frontend repository! This repository contains the frontend code for the Plant Disease Classifier project. It is built entirely in ReactJS and deployed using Netlify.

Project Overview

The Plant Disease Classifier Frontend is a web application developed to identify plant diseases swiftly and accurately. Utilizing ReactJS for the frontend, this project offers a user-friendly interface for uploading plant images and receiving disease classification results.

Key Components

1. ReactJS Web Interface

The repository includes the ReactJS-based web interface, providing users with an intuitive platform to upload images of plants. The interface seamlessly integrates with the backend classifier (not included) to process uploaded images and display disease classification results.

2. Deployment

Experience the Plant Disease Classifier in action! The web interface is deployed and accessible online at Plant Disease Classifier. You can visit the provided URL to utilize the classifier immediately without any local setup.

3.Website Images

Check out some screenshots of our web interface:

Screenshot 2024-03-16 205033

Screenshot 2024-03-16 205119

Getting Started

  1. Visit the deployed web interface at Plant Disease Classifier.
  2. Upload an image of a plant using the user-friendly interface.
  3. Wait for the classifier to process the image.
  4. Explore the classification results to gain insights into the health of the uploaded plant.

Usage

The Plant Disease Classifier Frontend is designed to be simple and intuitive. Users can upload images of plants through the web interface and receive immediate disease classification results. Here's how to use it:

  1. Upload Image: Click on the "Upload" button to select an image of a plant from your device.
  2. View Results: Once the image is uploaded, the classifier will process it, and the results will be displayed on the screen.
  3. Explore Insights: Gain valuable insights into the health of the plant based on the disease classification provided by the classifier.

Project Structure

The frontend codebase is organized as follows:

plant-disease-classifier-frontend/
│
├── public/              # Public assets and index.html
│
├── src/                 # Source code directory
│   ├── components/      # React components
│   ├── styles/          # CSS stylesheets
│   ├── App.js           # Main application component
│   └── index.js         # Entry point
│
├── .gitignore           # Specifies intentionally untracked files to ignore
├── LICENSE              # MIT License file
└── README.md            # Project README file

Dependencies

The Plant Disease Classifier Frontend relies on the following major dependencies:

  • React: A JavaScript library for building user interfaces.
  • Axios: A promise-based HTTP client for making API requests.
  • React Router: Declarative routing for React applications.
  • Netlify: Hosting platform for deploying web applications.

Contributions

We welcome contributions from the community to improve this project. Whether you have ideas for enhancing the frontend interface, adding new features, or fixing bugs, your contributions are valuable. Feel free to open an issue or submit a pull request—we appreciate your input!

License

This project is licensed under the MIT License, allowing you to use and modify the code in accordance with the terms of the license.

Contact

If you have any questions or feedback, please don't hesitate to contact us at Sharmaalok02gwl@gmail.com.

About

Welcome to the Plant Disease Classifier Deployment repository! This project integrates cutting-edge technologies to develop a sophisticated plant disease classifier. Leveraging TensorFlow for model creation and ReactJS for the web interface, this project offers an innovative solution for identifying plant diseases swiftly and accurately.

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