Avengers Classifier

A web application that classifies images of Avengers characters using a machine learning model. Upload an image, and the app will predict which Avenger it is, displaying probability scores for each character.

Features

• Upload an image and classify it as one of the Avengers (Bruce Banner, Clint Barton, Natasha Romanoff, Steve Rogers, Thor Odinson, Tony Stark).
• Displays probability scores for each character.
• Clean, modern UI with drag-and-drop image upload.
• Model success rate of ~70%

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Project Structure

.
├── model/                # Model training, saved model, and requirements
│   ├── Avengers_Classifier_Model.ipynb
│   ├── saved_model.pkl
│   ├── class_dictionary.json
│   ├── requirements.txt
│   └── ...
├── server/               # Backend Flask server
│   ├── server.py
│   ├── util.py
│   └── ...
├── UI/                   # Frontend files
│   ├── app.html
│   ├── app.js
│   ├── app.css
│   ├── dropzone.min.js
│   ├── dropzone.min.css
│   └── images/
├── GIF_Demo.gif          # Demo GIF
├── ui_snapshot.png       # UI Screenshot
└── ...

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Setup Instructions

1. Clone the Repository

git clone https://github.com/Abhay-Mmmm/Avengers-Classifier.git
cd Abhay's AvengerClassifier

2. Backend Setup (Flask + Model)

1. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

   pip install -r model/requirements.txt
   pip install flask flask-cors

3. Run the Flask server:

   cd server
   python server.py

   The server will start at http://localhost:5000.

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3. Frontend Setup

1. Open the UI:

   • Open UI/app.html directly in your browser.
   • Make sure the Flask server is running in the background.

2. (Optional) Serve the UI with a local server:

   • You can use any static server (like http-server for Node.js or Python's http.server).
   • Example with Python:

     cd UI
     python -m http.server 8000

     Then visit http://localhost:8000/app.html.

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4. Usage

• Drag and drop an image of an Avenger or click to upload.
• Click the "Classify" button.
• The predicted character and probability scores will be displayed.

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Notes

• Ensure all images in UI/images/ are present for the UI to display correctly.
• The backend uses a pre-trained model (model/saved_model.pkl). If you wish to retrain, use the Jupyter notebook in model/Avengers_Classifier_Model.ipynb.
• The server expects images in base64 format from the frontend.

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Dependencies

• Python 3.x
• Flask
• Flask-CORS
• PyWavelets
• OpenCV (opencv-python, opencv-python-headless)
• Seaborn

Install all Python dependencies with:

pip install -r model/requirements.txt
pip install flask flask-cors

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Licensed by MIT.

Made with ❤️ by Abhay