Emotion Classification App

Welcome to the Emotion Classification App! This application uses a pre-trained deep learning model to classify emotions from uploaded images.

Features

• Emotion Detection: Upload an image, and the app will classify the emotion displayed.
• User-Friendly Interface: Built with Streamlit for an intuitive and interactive user experience.

Technologies Used

• Python: Core programming language for development.
• TensorFlow/Keras: For loading and using the pre-trained emotion classification model.
• Streamlit: To create an interactive web interface.
• PIL (Python Imaging Library): For image processing.

Installation

1. Clone the repository:

   git clone https://github.com/HoBaaMa/Emotion-Classification-App.git

2. Navigate to the project directory:

   cd Emotion-Classification-App

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Open your web browser and go to http://localhost:8501 to access the app.
3. Upload an image to classify the emotion.

How It Works

• The app preprocesses the uploaded image by converting it to grayscale, resizing it to 48x48 pixels, and normalizing the pixel values.
• The preprocessed image is then fed into a pre-trained model to predict the emotion.
• The predicted emotion is displayed on the app interface.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

Special thanks to my mentor, Ahmed Hikal, AMIT Learning and ODC for their support and guidance throughout this project.