AgriClassify

Project Overview

This project is a web-based image classification application built using Flask and TensorFlow. The application leverages a custom-trained model, which I personally developed using a huge database. Users can upload images of fruits or vegetables, and the app will predict the category of the uploaded image, displaying the result along with the prediction confidence.

Features

• Image Upload: Users can upload images directly from their local directory through a web interface.

• Custom Model Prediction: The application processes the uploaded image and predicts its category using a TensorFlow model that I personally trained with a large dataset.

• Result Display: The predicted category and confidence level are displayed on a results page alongside the uploaded image.

Project Structure

    project-directory/
    │
    ├── app.py                    
    ├── templates/
    │   ├── result.html            
    │   └── upload.html           
    ├── uploads/                  
    └── README.md                 

Tech Stack

Language: Python

Backend: Flask

Machine Learning: Pandas, Matplotlib, TensorFlow, Keras

Frontend: HTML, CSS

Deployment: Flask development server

Installation

Prerequisites

Python 3.7+ Pip (Python package installer)

Setup Instructions

Clone the repository:

git clone https://github.com/Roy025/AgriClassify.git

cd AgriClassify

Create a virtual environment:

python -m venv venv

venv\Scripts\activate

Install the required packages:

pip install -r requirements.txt

Run the application:

python app.py

Access the application:

Open your web browser and navigate to http://127.0.0.1:5000/.

Screenshots:

Upload

Result

Data

Model Structure

Accuracy