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Emotion Classifier – CNN & RNN

This repository contains two deep learning–based emotion classification systems:

  1. CNN-based Facial Emotion Recognition (Image-based)
  2. RNN-based Emotion Classification (Text-based)

The CNN model is production-ready, Dockerized, and deployable on Docker & Kubernetes.

📁 Project Structure

Emotion_Clasifier-RNN-CNN-
│
├── CNN/
│   ├── app.py                  # Flask API for CNN inference
│   ├── model.h5                # Trained CNN model (emotion classification)
│   ├── Dockerfile              # Docker configuration for CNN service
│   ├── requirements.txt        # Python dependencies
│   ├── cnn-deployment.yaml     # Kubernetes Deployment
│   ├── cnn-service.yaml        # Kubernetes Service (ClusterIP)
│   ├── test.py                 # Client-side test script
│   └── Image Classifier.ipynb  # CNN training & experimentation notebook
│
├── RNN/
│   ├── model.h5                # Trained RNN model (text emotion classification)
│   ├── Predection_UI.py        # Local UI / script for prediction
│   ├── TextClassifier1.ipynb   # RNN training notebook
│   ├── training.csv            # Training dataset
│   └── README.md               # RNN-specific documentation

🧠 CNN – Facial Emotion Recognition

A Convolutional Neural Network (CNN) trained to classify facial emotions from images. The model is exposed as a Flask REST API and packaged as a Docker container.

🎭 Supported Emotions

  • Angry
  • Disgusted
  • Fearful
  • Happy
  • Neutral
  • Sad
  • Surprised

🐳 Docker Image (CNN)

The CNN service is available as a public Docker image:

🔗 Docker Hub Repository https://hub.docker.com/repository/docker/brijesh944/emotion_cnn/general

▶️ Run with Docker

docker run -p 5000:5000 brijesh944/emotion_cnn

Access:

http://localhost:5000

☸️ Run CNN with Kubernetes (Docker Desktop / Minikube)

Deploy

kubectl apply -f cnn-deployment.yaml
kubectl apply -f cnn-service.yaml

Verify

kubectl get pods
kubectl get svc

Inside the cluster, the service is accessible via:

http://cnn:5000

From local machine:

kubectl port-forward svc/cnn 5000:5000

📡 CNN API Endpoint

POST /predict

Request (JSON):

[[[[...image pixel values...]]]]

Response:

{
  "predicted_class": 3
}

🧪 Testing the CNN API

Use the provided script:

python test.py

Or send a request using requests:

requests.post("http://localhost:5000/predict", json=data)

🧠 RNN – Text Emotion Classification

The RNN module focuses on text-based emotion detection using sequential models.

Features

  • Trained RNN model (model.h5)
  • Notebook for training & evaluation
  • Local prediction UI / script
  • CSV-based training dataset

Note: The RNN module is currently designed for local execution, not containerized.

🛠️ Tech Stack

  • Python 3
  • TensorFlow / Keras
  • Flask
  • NumPy
  • Docker
  • Kubernetes
  • Jupyter Notebook

🎯 Use Cases

  • Facial emotion recognition demos
  • Text-based emotion analysis
  • ML inference microservices
  • Docker & Kubernetes learning
  • MLOps and deployment practice

⚠️ Notes

  • The CNN service binds to 0.0.0.0:5000 for container compatibility
  • Models are intended for educational and prototype purposes
  • Not optimized for large-scale production inference

👤 Author

Brijesh Computer Science Engineer | AI & Deep Learning Enthusiast

If you plan to extend this project:

  • Add RNN Docker support
  • Introduce model versioning
  • Add Ingress / LoadBalancer
  • Implement CI/CD for ML pipelines

🚀