• analysis.html: The HTML structure for the dashboard.
• analysis.js: JavaScript for processing data and generating visualizations.
• app.py: Python script to start/stop tracking and serve the dashboard.
• getusage_win.py: Python script to track active window usage on Windows.
• requirements.txt: Lists the Python dependencies required for the project.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/track-it.git
   cd track-it

2. Install Python dependencies:

   pip install -r requirements.txt

Usage

1. Start the application:

   python app.py

2. Tracking:

   • Click "Start Tracking" to begin monitoring your activity.
   • Click "Stop Tracking" to stop monitoring.

3. Dashboard:

   • Click "Show Dashboard" to start the local server and open the dashboard in your web browser.
   • The dashboard will be accessible at http://localhost:8000/analysis.html.

Data Visualization

The dashboard provides various visualizations:

• Activity Timeline: Displays a timeline of your application usage.
• Application Usage: Donut chart showing the distribution of time spent on different applications.
• Usage Patterns: Line chart showing hourly usage patterns.
• Multi-dimensional Analysis: Bubble chart comparing CPU and memory usage.
• Weekly Trends: Bar chart showing daily usage trends.

Insights and Recommendations

The dashboard also provides smart insights and recommendations based on your usage patterns:

• Productivity Score: Based on application usage patterns.
• Focus Score: Percentage of long, focused sessions.
• Work-Life Balance: Analysis of work and personal time.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

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

Acknowledgements

• D3.js
• Chart.js
• Moment.js
• Tailwind CSS
• psutil
• pywin32

Contact

For any questions or suggestions, please contact your-email@example.com.