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πŸ₯¦ Smart Bridge - AI Powered Produce Classifier 🍎

Smart Bridge is an AI-powered web application that detects whether fruits and vegetables are fresh or rotten using deep learning (VGG16). Built with a modern UI and deployed on Render, this tool provides instant quality analysis with a single image upload.


πŸš€ Live Demo

πŸ”— Smart Bridge Live App Click the link and wait a minute to load the application and then refresh or open in a new tab


πŸ“Έ Demo Visuals

🏠 Home Page

Screenshot (583) Screenshot (584)

πŸ” Predict Page

Predict

πŸ“ˆ Result Output

Result

About

About

Contact

Screenshot (590) Screenshot (591)

πŸŽ₯Demo Video:

https://drive.google.com/file/d/1W26DcKPSnwxO8gVYwLsN2Tkug1zpECqo/view?usp=sharing

πŸ’‘ Features

  • βœ… Upload an image of a fruit or vegetable
  • βœ… Predict whether it’s Healthy or Rotten
  • βœ… Stunning modern UI with dark theme and glassmorphism
  • βœ… Fully responsive for all devices
  • βœ… Backend powered by Flask + TensorFlow (VGG16)
  • βœ… Hosted & Live on Render

βš™οΈ Technologies Used

  • Frontend: HTML, CSS, JavaScript
  • Backend: Python Flask
  • Deep Learning: TensorFlow, Keras, VGG16
  • Deployment: Render (Backend), GitHub Hosting

πŸ—‚οΈ Project Structure

smart-sorting/
β”‚
β”œβ”€β”€ app.py # Flask backend
β”œβ”€β”€ healthy_vs_rotten.h5 # Trained model
β”œβ”€β”€ requirements.txt
β”‚
β”œβ”€β”€ templates/ # HTML files
β”‚ β”œβ”€β”€ index.html
β”‚ β”œβ”€β”€ about.html
β”‚ β”œβ”€β”€ contact.html
β”‚ β”œβ”€β”€ predict.html
β”‚ └── output.html
β”‚
β”œβ”€β”€ static/
β”‚ β”œβ”€β”€ css/
β”‚ β”‚ └── style.css
β”‚ └── img/
β”‚ β”œβ”€β”€ banner.jpg
β”‚ β”œβ”€β”€ email.png
β”‚ β”œβ”€β”€ github.png
β”‚ └── linkedin.png
β”‚
└── screenshots/
β”œβ”€β”€ home.png
β”œβ”€β”€ predict.png
└── contact.png


πŸ“¦ How to Run Locally

# Clone the repository
git clone https://github.com/RAFIROCK/smart-bridge.git
cd smart-bridge

# Optional: create virtual environment
python -m venv venv
venv\Scripts\activate     # For Windows
# source venv/bin/activate  # For macOS/Linux

# Install dependencies
pip install -r requirements.txt

# Run the Flask application
python app.py


Then open http://127.0.0.1:5000 in your browser.


πŸ‘₯ Development Team

Name Role GitHub Profile
V MAHAMMAD RAFI Project Lead @RAFIROCK

🎯 Use Cases

🏭 Factories: Automated sorting of fresh vs rotten produce

πŸ›’ Supermarkets: Quality check at delivery docks

🏠 Smart Homes: Alert users to use produce before it spoils

πŸ™Œ Acknowledgements Kaggle Dataset – for the fruit and vegetable dataset

TensorFlow – deep learning framework

VGG16 – pre-trained model for transfer learning

Flask – lightweight Python web framework

✍️ Author

πŸ§‘πŸ»β€πŸ’» V MAHAMMAD RAFI

πŸ”— Portfolio: https://github.com/RAFIROCK

πŸ“œ License

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

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