🏃‍♂️ Fitness Data Analyzer

An advanced Streamlit application for analyzing fitness data from FIT and TCX files using AI-powered insights.

✨ Features

📊 Data Visualization

• Heart Rate Analysis: Interactive charts with training zone overlays
• Elevation Profiles: Detailed elevation changes throughout your workout
• GPS Route Mapping: Interactive maps showing your workout route
• Performance Metrics: Comprehensive workout statistics

🤖 AI-Powered Insights

• Detailed workout analysis using Google Vertex AI
• Training zone recommendations
• Performance improvement suggestions
• Recovery guidance
• Personalized coaching tips

📱 User Experience

• Modern, responsive interface
• Tabbed navigation for different data views
• Interactive charts and visualizations
• Chat interface for follow-up questions
• Export capabilities for data and chat history

📁 File Support

• FIT Files: From Garmin, Polar, Suunto devices
• TCX Files: Training Center XML format
• Automatic file type detection
• Comprehensive data extraction

🚀 Quick Start

Prerequisites

• Python 3.8+
• Google Cloud Account with Vertex AI enabled
• Valid fitness data files (FIT or TCX)

Installation

1. Clone the repository

   git clone <your-repo-url>
   cd FIT_Chatbot_Streamlit

2. Create virtual environment

   python -m venv 313
   source 313/bin/activate  # On Windows: 313\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment

   cp .env.example .env
   # Edit .env with your Google Cloud credentials

5. Run the application

   streamlit run main.py

⚙️ Configuration

Environment Variables

Create a .env file with the following variables:

GOOGLE_CLOUD_PROJECT_ID=your-project-id
GOOGLE_CLOUD_REGION=us-central1
MODEL_NAME=gemini-pro

Google Cloud Setup

1. Create a Google Cloud Project
2. Enable Vertex AI API
3. Set up authentication (service account or application default credentials)
4. Configure your project ID and region in the .env file

📈 Data Analysis Features

Heart Rate Analysis

• Training zone visualization
• Average, maximum, and minimum heart rate
• Heart rate variability insights
• Zone distribution analysis

Performance Metrics

• Distance and duration tracking
• Pace and speed analysis
• Calorie burn calculations
• Power data analysis (if available)

GPS and Route Analysis

• Interactive route mapping
• Elevation profile visualization
• Ascent and descent tracking
• Geographic insights

🛠️ Technical Details

Dependencies

• Streamlit: Web application framework
• Plotly: Interactive data visualization
• Folium: GPS mapping and route visualization
• FitDecode: FIT file parsing
• TCXParser: TCX file parsing
• Google Vertex AI: AI-powered analysis
• Pandas/NumPy: Data processing

File Processing

• Secure temporary file handling
• Automatic cleanup after processing
• Error handling for corrupted files
• Support for large file uploads

🔧 Customization

Adding New Visualizations

1. Create visualization functions in the utility section
2. Add new tabs in the main interface
3. Include data validation and error handling

Extending AI Analysis

1. Modify the AI prompt templates
2. Add new analysis categories
3. Implement custom metrics calculation

UI Enhancements

1. Customize the Streamlit theme
2. Add new sidebar options
3. Implement additional export formats

🐛 Troubleshooting

Common Issues

ImportError: No module named 'X'

• Ensure all dependencies are installed: pip install -r requirements.txt

Google Cloud Authentication Error

• Verify your credentials are properly configured
• Check your project ID and region settings

File Parsing Errors

• Ensure your files are valid FIT or TCX format
• Try with different files to isolate the issue

Visualization Not Loading

• Check browser console for JavaScript errors
• Try refreshing the page or restarting the app

📝 Usage Tips

1. File Upload: Drag and drop or browse for FIT/TCX files
2. Navigation: Use tabs to explore different aspects of your data
3. AI Chat: Ask specific questions about your workout performance
4. Export: Download analysis reports and chat history
5. Settings: Use sidebar options to customize the display

🔮 Future Enhancements

• Multiple file comparison
• Historical trend analysis
• Training plan generation
• Social sharing features
• Mobile app version
• Integration with fitness APIs
• Advanced machine learning insights

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📞 Support

For questions and support, please open an issue in the GitHub repository.