Upnava - Elevate Your Career to Top 1%

Up (elevation) + Nava (new)

Upnava is an AI-powered career analysis platform that helps professionals identify skill gaps and create personalized learning paths to compete with the top 1% in the job market.

Features

• Resume Analysis: OCR-powered resume parsing and skill extraction
• Market Intelligence: Real-time comparison with top market demands
• Skill Gap Detection: Identifies missing skills for elite positions
• GitHub Portfolio Analysis: Evaluates code quality and project portfolio
• Personalized Learning Plans: Custom roadmaps with prioritized skills
• Career Recommendations: Actionable insights for career growth

Architecture

The system uses a multi-agent architecture powered by LangChain and LangGraph:

User Upload → OCR Agent → Resume Parser → Skills Extractor
                                              ↓
                                    Market Analyzer Agent
                                              ↓
                                      Gap Detector Agent
                                              ↓
                                    GitHub Analyzer Agent
                                              ↓
                                    Learning Plan Generator
                                              ↓
                                    Personalized Recommendations

Tech Stack

• Backend: FastAPI, LangChain, LangGraph
• AI/ML: Google Gemini, LangChain Agents
• Frontend: HTML, CSS, JavaScript
• OCR: Pytesseract, PyPDF2
• GitHub Integration: PyGithub

Installation

1. Clone the repository:

git clone https://github.com/yourusername/upnava.git
cd upnava

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env

Edit .env and add your API keys:

• GOOGLE_API_KEY: Your Google Gemini API key
• GITHUB_TOKEN: GitHub personal access token (optional)

4. Run the application:

cd backend
python main.py

5. Open your browser and navigate to:

http://localhost:8000

Usage

1. Upload Resume: Upload your resume in PDF or text format
2. GitHub URL (Optional): Provide your GitHub profile URL
3. Analyze: Click "Analyze My Profile" to start the analysis
4. Review Results: Get detailed insights including:
   • Current skill assessment
   • Market gap analysis
   • GitHub portfolio feedback
   • Personalized learning plan
   • Career recommendations

API Endpoints

• GET /: Serve frontend application
• POST /api/analyze: Analyze resume and GitHub profile
• POST /api/upload-resume: Upload resume file
• GET /api/sample-analysis: Get sample analysis results
• GET /health: Health check endpoint

Project Structure

upnava/
├── backend/
│   ├── agents/
│   │   ├── base_agent.py
│   │   ├── resume_parser_agent.py
│   │   ├── skills_extractor_agent.py
│   │   ├── market_analyzer_agent.py
│   │   ├── gap_detector_agent.py
│   │   └── github_analyzer_agent.py
│   ├── models/
│   │   └── schemas.py
│   ├── orchestrator.py
│   └── main.py
├── frontend/
│   ├── index.html
│   ├── css/
│   │   └── styles.css
│   └── js/
│       └── app.js
├── static/
│   └── uploads/
├── requirements.txt
├── .env.example
└── README.md

Agent System

Resume Parser Agent

Extracts text from PDF/images using OCR and structures resume data.

Skills Extractor Agent

Identifies technical and soft skills, experience level, and certifications.

Market Analyzer Agent

Analyzes current job market trends and top 1% skill requirements.

Gap Detector Agent

Identifies missing skills and creates priority-based improvement areas.

GitHub Analyzer Agent

Evaluates GitHub repositories for code quality and best practices.

Orchestrator

LangGraph-based workflow orchestrator that manages agent interactions and memory.

Premium Features (Coming Soon)

• Curated video lectures from top instructors
• AI-generated study materials
• Real-world project templates
• Weekly progress tracking
• 1-on-1 AI mentorship

Contributing

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

License

This project is licensed under the MIT License.

Support

For issues and questions, please open an issue on GitHub.

Acknowledgments

• Built with LangChain and LangGraph
• Powered by Google Gemini
• FastAPI for backend services