Emotion Detector

Emotion Detector is a full-stack AI web application that analyzes facial expressions in images to detect human emotions.
It combines a React frontend, FastAPI backend, and a deep learning emotion recognition model to deliver a smooth and intuitive user experience.

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Features

• User authentication (Sign up / Login / Logout)
• Upload images for emotion analysis
• AI-based facial emotion recognition
• Emotion confidence score
• Emotion history per user
• Secure JWT-based API
• Clean and responsive UI

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Supported Emotions

The model predicts one of the following emotions:

• Angry
• Disgusted
• Fear
• Happy
• Sad
• Surprise
• Neutral

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System Architecture

emotion-detector/
│
├── backend/                # FastAPI backend
│   ├── main.py
│   ├── database.py
│   └── users.db            # SQLite database (auto-created)
│
├── model/
│   └── face_model.h5       # Trained emotion detection model
│
├── src/                    # React frontend
├── public/
│
├── ui-screenshots/         # UI images used in this README
│
├── package.json
└── README.md

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Tech Stack

Frontend

• React (Create React App)
• Axios
• HTML / CSS / JavaScript

Backend

• FastAPI
• Uvicorn
• SQLite
• JWT Authentication
• Passlib (bcrypt)

Machine Learning

• TensorFlow / Keras
• Convolutional Neural Network (CNN)
• Grayscale facial images (48×48)

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Getting Started

Prerequisites

• Python 3.11
• Node.js (LTS)
• npm

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Backend Setup (FastAPI)

# From project root
python -m venv .venv
.venv\Scripts\activate   # Windows

cd backend
pip install -r requirements.txt
uvicorn main:app --reload

Backend runs at:

http://127.0.0.1:8000

API documentation:

http://127.0.0.1:8000/docs

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Frontend Setup (React)

# From project root
npm install
npm start

Frontend runs at:

http://localhost:3000

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Authentication Flow

1. User signs up
2. User logs in
3. Backend returns a JWT token
4. Token is stored client-side
5. Authenticated requests include: Authorization: Bearer

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Application Screenshots

Home & Authentication

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Emotion Detection Flow

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History

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Database

• SQLite
• Database file: users.db
• Automatically created on first backend run
• Stores:
  • Users
  • Encrypted passwords
  • Emotion analysis history
  • Uploaded images (BLOBs)

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Use Cases

• Emotion-aware applications
• Human-computer interaction demos
• AI-powered image analysis
• Portfolio and academic projects

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Notes

• TensorFlow requires Python 3.10 or 3.11
• CPU inference supported (GPU optional)
• Image upload endpoints require authentication

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Future Improvements

• Real-time webcam emotion detection
• Face detection before emotion classification
• Improved confidence visualization
• Dockerized deployment
• Cloud hosting (AWS / GCP)