README.md

🎭 End-to-End Facial Emotion Recognition

This repository contains a complete, end-to-end MLOps pipeline and a production-ready web application for real-time facial emotion recognition. The project leverages a state-of-the-art Vision Transformer model and is deployed as a user-friendly Gradio application on Hugging Face Spaces.

Live Demo: 🚀 Click here to try the application on Hugging Face Spaces!

---

✨ Features

• Real-time Emotion Detection: Analyzes your webcam feed to predict emotions in real-time.
• High Accuracy: Powered by a pre-trained Swin Transformer model fine-tuned on the massive AffectNet dataset for superior performance on "in the wild" faces.
• Static Image & Video Analysis: Upload your own images or videos for emotion prediction.
• Polished UI: A professional and responsive user interface with an animated background, built with Gradio.
• Reproducible MLOps Pipeline: The entire model training and data processing workflow is managed by DVC, ensuring 100% reproducibility.
• Containerized for Deployment: The application is packaged with Docker for easy and consistent deployment anywhere.

🛠️ Tech Stack

• Model: Swin Transformer (PangPang/affectnet-swin-tiny-patch4-window7-224)
• ML/Ops: Python, TensorFlow/Keras, DVC, MLflow, Hugging Face transformers
• Backend & UI: Gradio
• Face Detection: MTCNN
• Deployment: Hugging Face Spaces, Docker

🚀 Getting Started

Follow these steps to run the project locally.

Prerequisites

• Python 3.10+
• Git and Git LFS (installation guide)
• An NVIDIA GPU with CUDA drivers is recommended for the training pipeline, but the deployed app runs on CPU.

1. Clone the Repository

git clone https://github.com/YOUR-USERNAME/Emotion-Recognition-MLOps.git
cd Emotion-Recognition-MLOps