Transform your GitHub profile data and resume into a professional markdown CV automatically!

Generate a comprehensive, professional CV by combining your GitHub repository statistics, project analysis, and resume information using OCR and local LLM processing.

• 📊 GitHub Analytics: Automatically analyzes your repositories for stars, commits, lines of code, and language distribution
• 📄 OCR Resume Processing: Extracts text from PDF and image resumes using Tesseract OCR
• 🤖 AI-Powered Parsing: Uses local Ollama LLM to intelligently parse resume content
• 🎯 Smart Skills Detection: Combines resume skills with programming languages detected from GitHub projects
• 📝 Professional Template: Generates a well-structured markdown CV with multiple sections
• 🔒 Privacy-First: All processing happens locally - no data sent to external services
• ⚡ Fast & Efficient: Processes data quickly with minimal dependencies

• Python 3.8 or higher
• Tesseract OCR installed on your system
• Ollama running locally with a language model

1. Clone the repository

   git clone https://github.com/PriyavKaneria/profile-generator.git
   cd profile-generator

2. Install Python dependencies

   pip install -r requirements.txt

3. Install Tesseract OCR (only for image based resume)

   Ubuntu/Debian:

   sudo apt-get install tesseract-ocr

   macOS:

   brew install tesseract

   Windows: Download from UB-Mannheim/tesseract

4. Setup Ollama

   # Install Ollama (visit https://ollama.ai/ for installation instructions)
   
   # Pull a model (choose one)
   ollama pull llama2          # General purpose
   ollama pull codellama       # Code-focused
   ollama pull mistral         # Alternative option or use any which you feel like

Basic usage:

python generate_cv_profile.py --github-data github_data.json --resume resume.pdf --output my_cv.md

With custom model:

python generate_cv_profile.py \
    --github-data github_profile_data.json \
    --resume resume.png \
    --output professional_cv.md \
    --ollama-model codellama

See https://github.com/PriyavKaneria/CodeStats

Your GitHub data should be in JSON format with the following structure:

{
  "repository_name": {
    "featuredLevel": 3,
    "total_files": 16,
    "total_lines": 1466,
    "total_lines_of_code": 1030,
    "actual_code_lines": 1018,
    "language_distribution": {
      ".py": 1432,
      ".js": 123,
      ".html": 164
    },
    "description": "Project description",
    "stars": 5,
    "topics": ["python", "web"],
    "private": false,
    "contributions": {
      "total_commits": 39,
      "total_lines_changed": {
        "additions": 3173,
        "deletions": 1694
      },
      "first_commit_date": "2024-07-21 15:18:05",
      "last_commit_date": "2024-08-13 15:06:48"
    }
  }
}

The generated CV includes:

• Contact Information - Extracted from resume
• Professional Summary - Parsed from resume using LLM
• GitHub Statistics - Calculated from repository data
  • Total public repositories
  • Total stars received
  • Total commits made
  • Lines of code written
• Technical Skills - Combined from resume and GitHub language analysis
• Featured Projects - Top 5 repositories with details
• Professional Experience - Extracted from resume
• Education - Academic background from resume
• Certifications - Professional certifications listed

1. GitHub Analysis: Processes repository data to extract meaningful statistics and identify primary programming languages
2. OCR Processing: Uses Tesseract to extract text from PDF or image resumes
3. LLM Parsing: Employs local Ollama model to structure resume text into organized data
4. Smart Categorization: Automatically categorizes skills and projects based on content analysis
5. Template Generation: Combines all data into a professional markdown format

• PDF files (.pdf) - Text extraction via PyMuPDF
• Image files (.png, .jpg, .jpeg) - OCR via Tesseract

• llama2 - General purpose, good balance
• codellama - Optimized for code and technical content
• mistral - Fast and efficient
• llama3 - Latest version with improved capabilities

The script automatically detects programming languages from file extensions:

• Python, JavaScript, TypeScript, Java, C++, C, C#
• PHP, Ruby, Go, Rust, Swift, Kotlin
• HTML, CSS, Svelte, Vue.js, React

Contributions are welcome! Here are some ways you can help:

• 🐛 Report bugs and issues
• 💡 Suggest new features
• 📝 Improve documentation
• 🔧 Submit pull requests

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and test thoroughly
4. Submit a pull request with a clear description

Tesseract not found:

# Make sure Tesseract is in your PATH
tesseract --version

Ollama connection error:

# Check if Ollama is running
curl http://localhost:11434/api/tags

Poor OCR results:

• Ensure resume image is high quality (300+ DPI)
• Try preprocessing image (contrast, brightness)
• Use PDF format when possible

LLM parsing issues:

• Try different Ollama models
• Ensure resume has clear structure
• Check if resume text was extracted correctly

• Processing Time: ~30-60 seconds for typical resume + GitHub data
• Memory Usage: ~200-500MB depending on Ollama model
• Accuracy: 85-95% for well-structured resumes

• Local Processing: All data processing happens on your machine
• No External APIs: No data sent to third-party services
• Open Source: Full transparency of data handling

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

• Tesseract OCR for optical character recognition
• Ollama for local LLM capabilities
• PyMuPDF for PDF processing
• Pillow for image processing

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed information
3. Include error messages and system information

Made with ❤️ for developers who want to showcase their GitHub portfolio professionally