AI-Powered Resume Optimizer

🚀 Transform your resume with AI-powered optimization for better ATS compatibility and job matching.

Features

• 🎯 ATS Optimization: Improve compatibility with Applicant Tracking Systems
• 🤖 AI-Powered Analysis: Leverage Perplexity AI and Google Gemini for intelligent recommendations
• 📊 Job Matching: Analyze resume against specific job descriptions
• 🔑 Keyword Optimization: Identify and suggest missing keywords
• 📄 PDF Generation: Create professionally formatted, ATS-compliant resumes
• 🎨 Streamlit UI: User-friendly web interface

Architecture

The application follows a modular, object-oriented architecture with clear separation of concerns:

src/
├── resume_optimizer/
│   ├── config/          # Configuration management
│   ├── core/            # Core business logic
│   │   ├── resume_parser/     # Resume parsing and extraction
│   │   ├── job_analyzer/      # Job description analysis
│   │   ├── ai_integration/    # AI service clients
│   │   ├── ats_optimizer/     # ATS optimization engine
│   │   └── pdf_generator/     # PDF generation
│   ├── streamlit_ui/    # Streamlit web interface
│   └── utils/           # Utilities and helpers

Installation

1. Clone the repository:

git clone https://github.com/yourusername/resume-optimizer.git
cd resume-optimizer

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Install spaCy model:

python -m spacy download en_core_web_sm

5. Configure environment variables:

cp .env.example .env
# Edit .env with your API keys

Usage

Streamlit Web Interface (Recommended)

python main.py

Then open http://localhost:8501 in your browser.

Command Line Interface

python main.py --cli \
    --resume path/to/resume.pdf \
    --job path/to/job_description.txt \
    --output optimized_resume.pdf \
    --name "John Doe" \
    --company "TechCorp"

Configuration

Required API Keys

• Perplexity AI: Get from https://perplexity.ai/
• Google Gemini: Get from https://makersuite.google.com/

Environment Variables

# AI Service API Keys
PERPLEXITY_API_KEY=your_key_here
GOOGLE_API_KEY=your_key_here

# Application Settings
DATA_DIR=data
TEMP_DIR=data/temp
DEBUG=false

Development

Project Structure

The project follows PEP 8 standards and modern Python best practices:

• Modular Design: Clear separation of concerns with dedicated modules
• Object-Oriented: Uses design patterns like Factory, Strategy, and Observer
• Type Hints: Full type annotation support
• Error Handling: Comprehensive exception handling
• Logging: Structured logging throughout the application
• Configuration: Centralized configuration management

Key Components

1. Resume Parser: Extracts structured data from PDF/DOCX/TXT files
2. Job Analyzer: Analyzes job descriptions for requirements and keywords
3. AI Integration: Interfaces with Perplexity AI and Google Gemini
4. ATS Optimizer: Improves resume for ATS compatibility
5. PDF Generator: Creates professional, ATS-friendly PDFs

Running Tests

pytest tests/

Code Quality

# Format code
black src/

# Lint code
flake8 src/

# Type checking
mypy src/

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass
6. Submit a pull request

License

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

Support

For support and questions:

• 📧 Email: [email protected]
• 🐛 Issues: GitHub Issues
• 📚 Documentation: See docs/ directory