Emotion Detection Web App 🎭

A small Flask-based web application that detects emotions from images using a pretrained model.

This README explains how to set up, run, and deploy the app that is included in this repository.

Repository at a glance

Project structure:

EMOTION_DETECTOR/
├── app.py                        
├── model.py                     
├── simple_emotion_detector.py  
├── emotion_model.joblib        
├── templates/
│   └── index.html               
├── requirements.txt            
├── Procfile                   
├── runtime.txt                 
├── app.json                  
├── link.txt                    
└── README.md          

Key files and folders:

• app.py — Flask application (entry point). The app runs by default on port 8000.
• model.py — wrapper to initialize and use the trained model.
• simple_emotion_detector.py — model helper / implementation used by model.py.
• emotion_model.joblib — pretrained model (joblib format) used by the app.
• templates/ — HTML templates (contains index.html).
• requirements.txt — Python dependencies.
• Procfile — Heroku-compatible process file (web: gunicorn app:app).

Requirements

• Python 3.8+ (recommended)
• Install project dependencies:

pip install -r requirements.txt

If you use a virtual environment (recommended):

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run locally

The application is a Flask app and can be started directly for development or served with Gunicorn for production-like behavior.

• Development (quick run):

python app.py

By default the app listens on port 8000. Open http://localhost:8000 in your browser.

• Production / Heroku-like (uses Procfile):

# start with gunicorn (Procfile uses: web: gunicorn app:app)
gunicorn app:app

If deploying to Heroku, the included Procfile already contains the startup command.

How to use

1. Open the app in your browser (/ route).
2. Fill name (required) and optionally email.
3. Upload an image file (jpg, jpeg, png, bmp, gif).
4. Click the analyze button — the app will return the predicted emotion and confidence, plus a breakdown of emotion percentages.

Notes:

• app.py validates uploads (allowed extensions and a 16 MB max file size configured).
• If the trained model isn't available or initialization fails, the app falls back to a random prediction for demo purposes.

Model

The pretrained model is provided as emotion_model.joblib in the repository root. model.py contains the code that loads this model (via joblib) and exposes a predict_emotion helper used by app.py.

If you retrain or replace the model, keep the same file path or update model.py accordingly.

Database

The app creates a lightweight SQLite database file named emotion_detection.db in the project root. It stores simple user records and prediction results. The DB is created automatically on first run.

Deployment

• Heroku: The Procfile includes web: gunicorn app:app so deployment to Heroku is straightforward. Make sure to set any required environment variables (e.g. SECRET_KEY).

• Docker: You can containerize the app yourself (not included here). Use gunicorn app:app as the container entrypoint.

Troubleshooting

• If the app prints an import/initialization error for the detector, ensure emotion_model.joblib exists and requirements.txt packages are installed.
• File upload errors: ensure the image is of an allowed type and under 16MB.
• Port in use: change the PORT environment variable or edit the port in app.py for local testing.

Contributing

Small improvements and fixes are welcome. If you change model behavior or public APIs, please update this README accordingly.

License

Educational / demo project. No explicit license set in this repository — update if you want to apply one.

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If you'd like, I can add a short example curl command and a minimal .env.example next.