FitPose

AI-Powered Exercise Analysis Platform

Transform your workouts with AI-powered form analysis using Computer Vision and GPT-4.

Quick Start

# Clone and setup
git clone https://github.com/Jonnydevp/FitPose.git && cd FitPose

# Backend
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env  # Add your OPENAI_API_KEY
python main.py

# Frontend (new terminal)
cd src/frontend && npm install && npm run dev

Open: http://localhost:5173

Tech Stack

• Frontend: React + Vite + Tailwind CSS
• Backend: FastAPI + OpenAI GPT-4 + MediaPipe
• Deploy: Vercel + Railway

Architecture

User → Frontend (React) → Backend API (FastAPI) → CV Processing → AI Analysis

Workflow:

1. Video Upload - user uploads video through web interface
2. Video Processing - VideoProcessor extracts movement vectors using MediaPipe
3. AI Analysis - vectors are sent to OpenAI for technique analysis
4. Results - user receives detailed feedback

Features

• AI exercise form analysis
• Real-time video processing
• Support for multiple exercise types (squats, push-ups, pull-ups)
• REST API with interactive documentation
• Modern responsive UI

API Endpoints

Health Check

GET /health

Exercise Analysis

POST /api/v1/analyze-exercise
Content-Type: multipart/form-data

file: [video file]

Environment

# .env
OPENAI_API_KEY=your_openai_api_key_here

Development

Backend

source .venv/bin/activate
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend

cd src/frontend
npm run dev

For production builds on Vercel, set:

VITE_API_URL=""

Then add a Vercel Rewrite (Project Settings → Routing → Rewrites):

• Source: /api/(.*)
• Destination: https://your-railway-url.up.railway.app/api/$1

With this setup the frontend calls relative paths (same-origin), and Vercel proxies /api/... to Railway. If you prefer not to use rewrites, set VITE_API_URL to https://your-railway-url.up.railway.app instead.

Deploy

One-Click Deploy:

Manual: See docs/DEPLOYMENT.md

Project Structure

FitPose/
├── main.py                   # FastAPI entry point
├── requirements.txt          # Python dependencies  
├── .env.example             # Environment template
├── src/
│   ├── backend/             # FastAPI application
│   │   ├── api/             # REST endpoints
│   │   ├── core/            # Configuration
│   │   ├── models/          # Data schemas
│   │   └── services/        # Business logic
│   ├── cv/                  # Computer Vision (MediaPipe)
│   ├── ml/                  # AI Analysis (OpenAI)
│   └── frontend/            # React application
├── docs/                    # Documentation
│   ├── README.md            # Detailed setup guide
│   └── DEPLOYMENT.md        # Production deployment
└── .github/workflows/       # CI/CD pipeline

Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/name
3. Commit changes: git commit -m 'Add feature'
4. Push branch: git push origin feature/name
5. Open Pull Request

License

MIT License - see LICENSE