Emotion Classification with Deep Learning Models

This project aims to classify emotions from facial images using deep learning techniques implemented in TensorFlow. The trained model is then deployed as a web application using Flask and containerized with Docker for easy deployment and scalability.

Project Overview

The core of this project is a deep learning model built with TensorFlow that can classify facial expressions into various emotion categories such as happiness, sadness, anger, fear, neutral and surprise. The model is trained on a labeled dataset of facial images (FER2013), allowing it to learn the intricate patterns and features associated with different emotions.

To make the model accessible and usable, it is deployed as a web application using Flask, a lightweight Python web framework. This application provides a user-friendly interface where users can upload facial images, and the deep learning model will analyze them and return the predicted emotion.

Furthermore, the entire application is containerized using Docker, ensuring consistent and reproducible deployments across different environments. This containerization approach simplifies the deployment process and allows for easy scaling and management of the application.

Features

• Deep learning model for emotion classification built with TensorFlow
• Trained on a labeled dataset of facial expressions
• Web application interface built with Flask
• User-friendly interface for uploading facial images
• Emotion prediction results displayed on the web page
• Docker containerization for easy deployment and scalability

Getting Started

To run this project locally, follow these steps or paste all of them in Terminal:

# 1. Clone the repository:
git clone https://github.com/quochuy242/Emotions-ImageClassification.git

# 2. Navigate to the project directory: `
cd Emotions-ImageClassification`

# 3. Build the Docker image: 
docker build -t Emotions-ImageClassification .

# 4. Run the Docker container: 
docker run -p 5000:5000 Emotions-ImageClassification

Access the web application at:

http://localhost:5000

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.