AI HR Assistant - Streamlit App

A modern, AI-powered HR application built with Streamlit that uses Google's Gemini AI to analyze and rank CVs based on job requirements.

🚀 Quick Start

Option 1: Automated Setup

python setup.py

Option 2: Manual Setup

# Install dependencies
pip install -r requirements.txt

# Create .env file with your Gemini API key
echo "GOOGLE_API_KEY=your_api_key_here" > .env

# Run the app
streamlit run streamlit_app.py

✨ Features

• 📄 PDF CV Upload: Upload multiple PDF CVs for analysis
• 🤖 AI-Powered Analysis: Uses Gemini AI to analyze CVs against job requirements
• 📊 Comprehensive Scoring: Scores CVs across 9 key dimensions
• 🏆 Smart Ranking: Automatically ranks candidates by total score
• 📈 Detailed Reports: View detailed analysis for each CV
• 📤 Export Results: Download results as CSV for further analysis

🏗️ Architecture

• Frontend: Streamlit (Python-based web framework)
• AI Engine: Google Gemini Pro API
• PDF Processing: PyPDF2 for text extraction
• Data Analysis: Pandas for data manipulation and export
• Evaluation Criteria: Based on industry-standard CV elements from cv_elements.json

📁 Project Structure

├── streamlit_app.py          # Main Streamlit application
├── demo_cv_analysis.py       # Demo script for testing
├── setup.py                  # Automated setup script
├── run_app.py                # Quick start runner
├── requirements.txt          # Python dependencies
├── README_STREAMLIT.md      # Detailed documentation
├── .streamlit/config.toml   # Streamlit configuration
└── src/server/py_utils/json_components/
    ├── cv_elements.json     # CV evaluation criteria
    └── jd_elements.json    # Job description elements

🔧 Configuration

1. Get Gemini API Key: Visit Google AI Studio
2. Set Environment Variable: Add your API key to .env file
3. Customize Evaluation: Modify cv_elements.json to adjust scoring criteria

📖 Documentation

• Setup Guide: See README_STREAMLIT.md for detailed instructions
• Demo: Run python demo_cv_analysis.py to test functionality
• Configuration: Check .streamlit/config.toml for app settings

🔄 Migration from Previous Version

This Streamlit app replaces the previous TypeScript/Next.js + FastAPI setup:

• ✅ Removed: TypeScript/Next.js frontend
• ✅ Removed: FastAPI backend
• ✅ Removed: OpenAI API dependency
• ✅ Added: Streamlit frontend
• ✅ Added: Google Gemini API integration
• ✅ Maintained: CV evaluation logic and criteria
• ✅ Enhanced: Better UI/UX with Streamlit components

🤝 Contributing

Feel free to submit issues and enhancement requests!

📄 License

This project is open source and available under the MIT License.