Your Personal Trainer, Reimagined.
Gymbro is a next-generation full-stack fitness application that leverages computer vision to provide professional-grade form analysis. It combines a sleek React frontend with a powerful Python AI backend to democratize fitness coaching.
Upload your workout videos to get instant, frame-by-frame biomechanical analysis.
- Squat Analyzer: Checks for NSCA-standard depth (Hip crease below knee top) and torso alignment.
- Deadlift Analyzer: Tracks "Setup → Pull → Lockout" phases, ensuring back neutrality (40-50°) and full hip extension.
- Push-Up Analyzer: Monitors elbow angle (90°), shoulder position (45°), and hip sag.
- Visual Overlays: See your skeleton, joint angles, and error flags directly on the video.
- Actionable Corrections: "Sit back deeper," "Don't round your back," "Engage core."
- Privacy First: All processing happens locally on your machine.
- Kinetic Interface: Smooth
framer-motionanimations. - Dark Mode Aesthetic: A premium Neon Green/Pink design system.
For a deep dive into how the Front-End, Back-End, and Computer Vision pipeline interact, read our detailed System Architecture Documentation.
Tech Stack:
- Frontend: React, Vite, Framer Motion
- Backend: Python, FastAPI, Uvicorn
- AI Engine: MediaPipe, OpenCV, MoviePy
You will need two terminal windows to run the full application.
cd client
npm install
npm run dev👉 Open Browser: http://localhost:5173
cd server # Important: Run from inside /server directory
pip install -r requirements.txt
uvicorn main:app --reload --port 8000👉 API Status: http://localhost:8000
VISION-47-PoseCorrect/
├── client/ # React Application
│ └── src/ # UI Components & Pages
├── server/ # Python Backend
│ ├── main.py # API Entry Point
│ ├── core/ # AI Analyzers (Squat, Deadlift, Pushup)
│ ├── uploads/ # Temporary storage for inputs
│ ├── outputs/ # Processed videos
│ └── requirements.txt # Python dependencies
└── system_architecture.md # Detailed Technical Docs- Start Both Servers (see above).
- Go to the Dashboard in the web app.
- Select "Video Analysis".
- Choose your exercise (e.g., Squat).
- Drag & Drop your video file.
- Watch the magic: The AI will process your video and return a highlighted version with a detailed report card.
Built by students from RV College of Engineering, Bangalore.
- Arya Wadhwa
- Dilraj Singh
- Shlokk Sikka
- Anirudh M
- Ashwin Acharya
We welcome contributions! Please feel free to submit a Pull Request.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Built with ❤️ by the Gymbro Team