Automated Resume Parser

A Python-based application that automatically extracts and analyzes information from resume documents (PDF and DOCX formats) using natural language processing.

Features

• Multiple Format Support: Parse resumes in PDF and DOCX formats
• Intelligent Information Extraction: Extract key details including:
  • Candidate name
  • Email address
  • Phone number
  • Skills
• Database Storage: Automatically store parsed information in PostgreSQL database
• RESTful API: Simple API endpoint for resume parsing
• Scalable Architecture: Modular design for easy extensions and modifications

Technology Stack

• Backend: Python 3.9+, Flask
• Database: PostgreSQL
• NLP: SpaCy
• Document Processing: PyPDF2, python-docx
• Development Tools: pytest, black, flake8

Installation

1. Clone the repository:

git clone https://github.com/stephenombuya/Automated-Resume-Parser
cd resume-parser

2. Create and activate 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. Download SpaCy model:

python -m spacy download en_core_web_sm

5. Set up environment variables:

cp .env.example .env
# Edit .env with your database credentials

6. Initialize the database:

flask db upgrade

Usage

1. Start the Flask application:

python app.py

2. Send a POST request to parse a resume:

curl -X POST -F "file=@/path/to/resume.pdf" http://localhost:5000/parse

Example Response

{
    "name": "John Doe",
    "email": "john.doe@email.com",
    "phone": "+1 123-456-7890",
    "skills": ["python", "java", "sql"]
}

Project Structure

resume-parser/
├── app/
│   ├── __init__.py
│   ├── config.py
│   ├── models.py
│   ├── parser/
│   │   ├── pdf_parser.py
│   │   ├── docx_parser.py
│   │   └── nlp_processor.py
│   └── utils.py
├── tests/
├── requirements.txt
├── .env.example
└── README.md

Development

• Run tests:

pytest

• Format code:

black .

• Check code style:

flake8

Contributing

1. Fork the repository
2. Create your feature branch: git checkout -b feature/new-feature
3. Commit your changes: git commit -am 'Add new feature'
4. Push to the branch: git push origin feature/new-feature
5. Submit a pull request

License

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

Acknowledgments

• SpaCy for providing excellent NLP capabilities
• PyPDF2 and python-docx for document parsing functionality

Future Improvements

• Add support for more document formats
• Implement machine learning for better information extraction
• Add bulk processing capabilities
• Create a web interface for file uploads
• Enhance skills detection with industry-specific vocabularies
• Add export functionality to various formats