Backend

• FastAPI: High-performance API framework
• Pydantic: Data validation and settings management
• Python 3.9+: Core programming language

Frontend

• Gradio: Interactive web interface
• Plotly: Data visualization

MLOps

• GitHub Actions: CI/CD pipeline
• HuggingFace Hub: Model versioning and storage
• HuggingFace Spaces: Deployment platform
• Docker: Containerization (optional)
• Pytest: Testing framework
• Black & Flake8: Code quality tools

📦 Installation

Local Setup

# Clone the repository
git clone https://github.com/yourusername/Resume_Analyzer_MLOps.git
cd Resume_Analyzer_MLOps

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

# Install dependencies
pip install -r requirements.txt

# Download spaCy model
python -m spacy download en_core_web_sm

# Run the application
python app.py

Docker Setup

# Build the image
docker build -t resume-analyzer .

# Run the container
docker run -p 7860:7860 resume-analyzer

🚀 Usage

Web Interface

1. Launch the application: python app.py
2. Open your browser at http://localhost:7860
3. Upload a PDF resume
4. Optionally paste a job description
5. Click "Analyze Resume"
6. View results including:
   • ATS Score
   • Extracted Skills
   • Job Matches
   • Skill Gap Analysis
   • Improvement Recommendations

API Usage

import requests

# Upload resume
with open("resume.pdf", "rb") as f:
    files = {"file": f}
    data = {"job_description": "Python developer with ML experience"}
    response = requests.post(
        "http://localhost:8000/api/analyze",
        files=files,
        data=data
    )
    
print(response.json())

📂 Project Structure

Resume_Analyzer_MLOps/
├── src/
│   ├── __init__.py
│   ├── resume_parser.py       # PDF parsing and text extraction
│   ├── nlp_processor.py       # NLP models and skill extraction
│   ├── job_matcher.py         # Embedding-based job matching
│   ├── ats_scorer.py          # ATS score calculation
│   └── utils.py               # Helper functions
├── models/
│   ├── __init__.py
│   └── model_loader.py        # Model loading and caching
├── api/
│   ├── __init__.py
│   └── main.py                # FastAPI endpoints
├── tests/
│   ├── __init__.py
│   ├── test_parser.py
│   ├── test_nlp.py
│   └── test_api.py
├── data/
│   ├── sample_resumes/        # Sample resumes for testing
│   └── job_database.json      # Sample job descriptions
├── logs/
│   └── app.log                # Application logs
├── .github/
│   └── workflows/
│       └── ci.yml             # GitHub Actions CI/CD
├── app.py                     # Main Gradio application
├── requirements.txt           # Python dependencies
├── Dockerfile                 # Docker configuration
├── .gitignore                 # Git ignore rules
├── README.md                  # This file
└── LICENSE                    # MIT License

🧪 Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=src --cov-report=html

# Run specific test
pytest tests/test_parser.py -v

📊 MLOps Features

1. Model Versioning

• Models stored on HuggingFace Hub
• Version-controlled model artifacts
• Easy rollback capabilities

2. CI/CD Pipeline

• Automated testing on push
• Code quality checks (Black, Flake8)
• Automatic deployment to HF Spaces

3. Monitoring & Logging

• Request/response logging
• Performance metrics tracking
• Error monitoring and alerting

4. Reproducibility

• Pinned dependencies
• Seed setting for deterministic results
• Documented model versions

🎯 How It Works

1. Resume Parsing

# Extract text from PDF
resume_text = parse_resume("resume.pdf")

2. Skill Extraction

# Use NER and pattern matching
skills = extract_skills(resume_text)
experience = extract_experience(resume_text)
education = extract_education(resume_text)

3. ATS Scoring

# Calculate ATS compatibility
ats_score = calculate_ats_score(
    resume_text,
    formatting_score,
    keyword_match,
    section_completeness
)

4. Job Matching

# Generate embeddings and find similar jobs
resume_embedding = model.encode(resume_text)
job_embeddings = model.encode(job_descriptions)
similarities = cosine_similarity(resume_embedding, job_embeddings)

5. Skill Gap Analysis

# Compare resume skills with job requirements
missing_skills = required_skills - resume_skills
recommendations = generate_recommendations(missing_skills)

🌐 Deployment

HuggingFace Spaces

1. Create a new Space on HuggingFace
2. Connect your GitHub repository
3. Configure Space settings:
   • SDK: Gradio
   • Python version: 3.9
4. Push to main branch - auto-deployment triggers!

Environment Variables

HF_TOKEN=your_huggingface_token  # For model access
LOG_LEVEL=INFO                    # Logging level
MAX_FILE_SIZE=10                  # Max upload size (MB)

📈 Performance Metrics

• Resume Processing: ~2-3 seconds per resume
• ATS Score Calculation: <1 second
• Job Matching: ~1-2 seconds for 100 jobs
• API Response Time: <5 seconds average

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• HuggingFace for model hosting and deployment
• Sentence-Transformers team for embedding models
• FastAPI team for the excellent framework
• Gradio team for the UI framework

📧 Contact

Your Name - [Your Email]

Project Link: https://github.com/yourusername/Resume_Analyzer_MLOps

Made with ❤️ and 🤖 by [Your Name]