PyGress 🐍📊

A comprehensive Python competency analysis tool that analyzes GitHub repositories to assess developer competency levels using CEFR (Common European Framework of Reference for Languages) standards.

🌟 Features

• GitHub Repository Analysis: Automatically clones and analyzes Python repositories
• CEFR Competency Assessment: Evaluates code complexity using A1-C2 proficiency levels
• Interactive Visualizations: Generates dynamic charts and graphs using Plotly
• Web Interface: User-friendly Flask-based web application
• Author Tracking: Track individual developer contributions and competency evolution

📋 Prerequisites

• Python 3.7+

🚀 Installation

1. Clone the repository:

   git clone <repository-url>
   cd Greeedhub_PyGress

2. Install required dependencies:

   pip install -r requirements.txt

   Or install manually:

   pip install flask pandas pydriller plotly pytz git

3. Install PyCEFR (automatically handled by the application): The application will automatically clone the PyCEFR repository for competency analysis.

🏗️ Project Structure

Greeedhub_PyGress/
├── backend/                          # Core analysis modules
│   ├── AnalyzeCompetencyScore.py     # Main competency analysis script
│   ├── CalculateCompetencyScore.py   # Score calculation and categorization
│   ├── PyDriller_ExtractData.py      # Git repository data extraction
│   ├── run_analysis.py               # Orchestration script
│   └── DataPyPI.csv                  # Repository URLs database
├── webui/                            # Web interface
│   ├── app.py                        # Flask application
│   ├── templates/                    # HTML templates
│   └── static/                       # Static assets
├── visualization/                    # Visualization scripts
│   └── Visualize_Code               # Plotly visualization generator
└── run.py                           # Main application entry point

🎯 Usage

Web Interface (Recommended)

1. Start the application:

   python run.py

2. Open your browser and navigate to http://localhost:5000

3. Enter a GitHub repository URL and click "Generate Analysis"

4. View results:

   • Competency scores for each developer
   • Interactive visualizations
   • Project-level analysis

Command Line Interface

1. Add repository URLs to backend/DataPyPI.csv:

   github_url
   https://github.com/username/repository
   

2. Run the analysis:

   python backend/run_analysis.py

3. View results in the generated directories:

   • backend/CompetencyScore/ - Raw competency data
   • backend/Classified_Project_CompetencyScore/ - Project-organized results
   • backend/Classified_Author_CompetencyScore/ - Author-organized results
   • visualization/ - Interactive HTML visualizations

📊 Understanding the Results

Competency Levels (CEFR)

• A1 (Beginner): Basic Python syntax and simple operations
• A2 (Elementary): Basic functions and data structures
• B1 (Intermediate): Object-oriented programming concepts
• B2 (Upper Intermediate): Advanced Python features and patterns
• C1 (Advanced): Complex algorithms and design patterns
• C2 (Mastery): Expert-level Python programming

Output Files

1. CSV Files: Raw competency scores for each file
2. JSON Files: Categorized data by project and author
3. HTML Visualizations: Interactive charts showing:
   • Spider charts for overall competency distribution
   • Time-series analysis of competency evolution
   • Individual developer competency profiles

📄 License

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

🙏 Acknowledgments

• PyCEFR: For the competency analysis framework
• PyDriller: For Git repository mining capabilities
• Plotly: For interactive visualizations
• Flask: For the web interface framework

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Note: This tool is designed for educational and research purposes. Competency assessments should be used as one of many indicators of developer skill, not as the sole measure of programming ability.