Fruit Freshness Classifier Streamlit App

https://fruitclassifierbeta.streamlit.app/

A simple local Streamlit application that uses a Keras MobileNetV2 model to classify the freshness of fruits into fresh, mild, or rotten.

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Features

• Upload an image of a fruit and get a real-time classification.

• Detailed UI showing:

  • Freshness label (fresh, mild, rotten)
  • Confidence score
  • Icon and recommendation text

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Installation & Setup

1. Clone the repository

   git clone https://github.com/your-username/fruit-freshness-streamlit.git
   cd fruit-freshness-streamlit

2. Create and activate a virtual environment (recommended)

   python3 -m venv venv
   source venv/bin/activate   # macOS/Linux
   venv\\Scripts\\activate  # Windows

3. Install dependencies

   pip install -r requirements.txt

4. Add your trained model

   • Place your model.h5 file in the project root.

   • Ensure classify.py points to the correct filename:

     model = load_model("model.h5")

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Running Locally

1. Start the app

   streamlit run app.py

2. Open in browser

   • Visit http://localhost:8501 to interact with the UI.

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Project Structure

fruit-freshness-streamlit/
├── app.py           # Main Streamlit script
├── classify.py      # Loads model and defines classify_fruit()
├── model.keras         # Keras MobileNetV2 weights
├── requirements.txt # Project dependencies
├── styles.css          # CSS
└── README.md        # This file

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Customization

• CSS: Edit styles.css to tweak card and icon styles.

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License

This project is licensed under the MIT License. See LICENSE for details.