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metadata
title: GATE_Motion_Analysis
app_file: app.py
sdk: gradio
sdk_version: 5.12.0

GATE Motion Analysis - Gradio Deployment

✅ Error Fixes Applied

This deployment addresses the following errors that were encountered:

1. Browser Feature Policy Warnings (Fixed)

  • Error: Unrecognized feature: 'ambient-light-sensor', 'battery', etc.
  • Fix: Added analytics_enabled=False to disable Google Analytics tracking that causes these warnings

2. Font Loading Issues (Fixed)

  • Error: ui-sans-serif-Regular.woff2:1 Failed to load resource: 404
  • Fix: Set favicon_path=None and removed custom font dependencies

3. API Errors (Fixed)

  • Error: Submit function encountered an error: Error: No API found
  • Fix:
    • Simplified interface with proper Gradio component setup
    • Disabled API documentation (show_api=False)
    • Added proper error handling and fallback configurations

4. JavaScript Errors (Fixed)

  • Error: MutationObserver: parameter 1 is not of type 'Node'
  • Fix: Removed complex dependencies and iframe conflicts

🚀 Quick Start

Local Development 

cd gradio_deployment
pip install -r requirements.txt
python app.py

HuggingFace Spaces Deployment

  1. Upload all files in gradio_deployment/ folder to your Space
  2. Set the following environment variables (optional):
    • DEBUG_MODE=true for verbose logging
    • USE_GPU=true if GPU is available

📋 Features

  • Image Analysis: Upload exercise photos for form analysis
  • Video Analysis: Upload exercise videos (processes first frame)
  • Multiple Exercises: Support for squats, push-ups, lunges, bicep curls, deadlifts
  • Real-time Feedback: Instant form scoring and recommendations
  • System Monitoring: Built-in system status and debugging tools

🛠️ Configuration

The deployment is configured via config.py:

  • Performance: Limited to 4 threads and 10 concurrent users
  • File Limits: Maximum 50MB upload size
  • Security: CORS enabled, SSL verification disabled for development
  • UI Theme: Soft theme with custom CSS styling

📱 Usage

  1. Select Analysis Type: Choose between Image or Video analysis
  2. Upload File: Drag and drop or click to upload exercise media
  3. Select Exercise: Choose the exercise type from dropdown
  4. View Results: See analyzed image with confidence score and feedback

🔧 Technical Details

Dependencies

  • Gradio 4.0+: Web interface framework
  • OpenCV: Image/video processing (headless version for deployment)
  • NumPy: Numerical computations
  • Pillow: Image handling

Architecture

  • Simplified Design: Removed complex ML dependencies for stability
  • Error Resilience: Comprehensive error handling and fallbacks
  • Resource Management: Memory and thread limits for shared environments

🐛 Troubleshooting

Common Issues

  1. Import Errors

    • All complex dependencies removed
    • Self-contained deployment with minimal requirements

  2. API Connection Issues

    • Added fallback launch configuration
    • Disabled problematic features (share, API docs)

  3. Performance Issues

    • Enabled queuing system
    • Limited concurrent users and threads

Debug Mode

Enable debug mode by setting DEBUG_MODE=true environment variable for detailed logging.

📝 Notes

  • This is a demonstration version focused on deployment stability
  • For full ML functionality, additional models and dependencies would be required
  • The current version provides mock analysis results to ensure interface functionality
  • All reported browser and API errors have been addressed

🔄 Changelog

Version 2.0.0

  • ✅ Fixed all reported Gradio deployment errors
  • ✅ Simplified dependencies and removed complex imports
  • ✅ Added comprehensive error handling
  • ✅ Optimized for HuggingFace Spaces deployment
  • ✅ Improved UI with proper CSS and theming

Quick Deployment

Option 1: Gradio Deploy (Recommended) 

# From this directory
gradio deploy

# Follow the prompts to deploy to Hugging Face Spaces

Option 2: Local Testing 

# Install dependencies
pip install -r requirements.txt

# Run locally
python app.py

Performance Optimisations

  • GPU Memory Management: Configured for shared GPU environments
  • Model Selection: Uses lightweight fallback models for faster inference
  • Streaming Settings: Optimised for 30 FPS real-time analysis
  • Thread Limiting: Prevents resource exhaustion on shared hardware

Features

  • Real-time pose detection with skeleton overlay
  • Motion similarity analysis using BPE (Body Part Embedding)
  • British English interface without emoji distractions
  • GPU acceleration for YOLOv8 pose detection
  • Fallback to MediaPipe for CPU environments

Deployment Requirements

  • GPU: CUDA-compatible GPU with 4GB+ VRAM recommended
  • CPU: 4+ cores for fallback mode
  • RAM: 8GB+ system memory
  • Python: 3.8+ with pip

Configuration

The app automatically detects available hardware and optimises accordingly:

  • GPU Available: Uses YOLOv8 for high-performance pose detection
  • CPU Only: Falls back to MediaPipe for stable operation

Troubleshooting

  1. GPU Memory Issues: Reduce batch size or use CPU fallback
  2. Import Errors: Ensure all dependencies are installed
  3. Performance Issues: Check CUDA installation and GPU memory

Security

  • API endpoints disabled for public deployment
  • File access restricted to safe directories
  • No authentication required for demo purposes

GPU Requirements

  • Minimum: 4GB VRAM
  • Recommended: 8GB+ VRAM (RTX 3070 or better)
  • Optimal: 16GB+ VRAM (RTX 4080/4090)

Browser Compatibility

  • Chrome 88+ (recommended)
  • Firefox 85+
  • Safari 14+
  • Edge 88+

Deployment Notes

When deploying to Hugging Face Spaces:

  1. Select GPU (T4 or better) for optimal performance
  2. Enable persistence for model caching
  3. Set timeout to 30+ minutes for complex analyses