fashion-rating-web-app

Fashion Rating Web App

A web application that allows users to upload fashion images and receive a rating from a TensorFlow model. Built with Flask for the backend and HTML/CSS/JavaScript for the frontend.

Features

• Upload fashion images via a simple web interface.
• Receive a fashion rating on a scale of 1 to 10 based on the uploaded image.
• Built with TensorFlow for machine learning model integration.
• Simple and clean user interface using HTML, CSS, and JavaScript.

Installation

Prerequisites

• Python 3.x
• TensorFlow
• Flask

Steps

1. Clone the repository:

   git clone https://github.com/AhmedFaizanDev/fashion-rating-web-app.git
   cd fashion-rating-web-app

2. Install the required packages:

   pip install -r requirements.txt

   If you don’t have `requirements.txt` yet, you can create it with:

   pip freeze > requirements.txt

3. Download or create a TensorFlow model file.

   Ensure you have a file named `fashion_rating_model.h5` in the root directory. This is a placeholder for your TensorFlow model.

4. Run the Flask application:

   python app.py

5. Access the web application:

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

File Structure

• `app.py`: The main Flask application file.
• `model.py`: The script used to create and train the TensorFlow model.
• `static/`: Contains static files such as CSS and JavaScript.
  • `styles.css`: The CSS file for styling the web page.
  • `script.js`: The JavaScript file for handling the form submission.
• `templates/`: Contains HTML files.
  • `index.html`: The main HTML page for the web application.
• `fashion_rating_model.h5`: The TensorFlow model file.

Usage

1. Upload an image using the form on the home page.
2. Submit the form to send the image to the backend.
3. Receive a rating from the TensorFlow model displayed on the page.

Contributing

If you want to contribute to this project:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature/YourFeature`).
3. Commit your changes (`git commit -am 'Add new feature'`).
4. Push to the branch (`git push origin feature/YourFeature`).
5. Create a new Pull Request.

License

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

Acknowledgments

• TensorFlow for machine learning.
• Flask for the web framework.
• HTML/CSS/JavaScript for the frontend development.