Bell Pepper Bacterial Infection Detection Model

Welcome to the Bell Pepper Bacterial Infection Detection project! This project utilizes a TensorFlow model trained on a Kaggle dataset to classify images of bell peppers as either having a bacterial infection or being healthy. The project consists of a FastAPI backend for serving the model and a simple HTML, CSS, and JavaScript frontend for user interaction.

Table of Contents

• Introduction
• Requirements
• Installation
• Usage
• Project Structure

Introduction

This project aims to provide a user-friendly interface for detecting bacterial infections in bell peppers using a pre-trained TensorFlow model. Users can upload images of bell peppers, and the model will predict whether the peppers are healthy or infected.

Requirements

Before running the project, ensure that you have the following prerequisites installed:

• Python (3.6+)
• TensorFlow
• FastAPI
• HTML, CSS, and JavaScript-enabled web browser

You can find the specific Python dependencies in the api/requirements.txt file.

Installation

Follow these steps to set up and run the Bell Pepper Bacterial Infection Detection project:

1. Clone the repository to your local machine using the following command:

git clone [email protected]:DavidCarG/BellPepperDisease.git

2. Install the Python dependencies by running the following command:

pip install -r api/requirements.txt

Usage

To use the Bell Pepper Bacterial Infection Detection project, follow these steps:

1. Start the FastAPI server by running the main.py file

2. Open your web browser and navigate to frontend/index.html. This will load the user interface for uploading images and receiving predictions.

3. Upload an image of a bell pepper using the provided interface, and the model will predict whether it is healthy or infected with bacteria.

4. View the prediction results on the web interface.

Project Structure

The project is organized into the following directories:

• api/: Contains the FastAPI backend code, including the pre-trained TensorFlow model and API routes.
• frontend/: Contains the HTML, CSS, and JavaScript files for the user interface.
• model/: Includes the pre-trained TensorFlow model and associated files.