Project Description

The system uses a trained machine learning model to classify fruits into fresh or rotten categories.
Users can upload images of fruits through the web interface, and the model will return predictions instantly.

Currently, the model is trained to recognize and classify the following fruits:

• Apple
• Banana
• Orange

The backend is powered by Flask, handling routes, predictions, and template rendering.
The frontend provides a clean, responsive design to make the platform user-friendly and accessible.

This project demonstrates how machine learning can be integrated into real-world web applications to promote food safety, efficiency, and sustainability.

Getting Started

To get started with this project, you need to have the following installed on your system:

• Python 3.10.
• pip (Python package manager).
• VS Code with the Jupyter extension or Anaconda (or any editor of your choice).

Once installed, you can clone the repository and run the application on your local machine.

Prerequisites

Before running the application, make sure you have:

• Installed all required dependencies (requirements.txt is provided).
• Downloaded a fruit dataset and divided it into train/ and test/ folders (these are excluded from the repository).
• Train the machine learning model using the provided Fruits Vision.ipynb notebook.
• Save the trained model file inside the project directory (the model file is excluded from this repo due to size).
• A system with RAM: 8GB or higher (recommended for model training).

The model is specifically trained for apples, bananas, and oranges (both fresh and rotten).

Running the Application

To run the application, follow these steps:

1. Clone the repository to your local machine.
2. Create a virtual environment and activate it (if using one).
3. Install the required packages: pip install -r requirements.txt
4. Start the Flask server: python app.py
5. Open a web browser and navigate to: http://localhost:5000/

Features

The application offers the following features:

• Upload fruit images for prediction
• Machine learning model for fruit classification
• Simple and responsive interface
• Real-time results
• Helps reduce food waste by detecting rotten fruits

Built With

• Python Flask – Backend framework
• Machine Learning (TensorFlow / Keras) – Model training and prediction
• HTML, CSS, JavaScript – Frontend design
• Jupyter Notebook – Model training and experimentation

License

This project is licensed under the MIT License - see theLicense file for details.