Resume Skill Extractor

A web-based application that extracts structured information from PDF resumes using Python and machine learning techniques.

Features

• Extracts key information from PDF resumes:
  • Name
  • Email
  • Phone number
  • Skills
  • Work experience
• Web-based interface
• Database storage
• Search functionality by skills
• Docker support for easy deployment

UI Preview

Below are screenshots of the application's user interface:

1. File Upload

2. Resume Summary

3. Search by Skill

4. Search Result

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Tech Stack

• Backend: Python, Flask
• Database: SQLite
• PDF Processing: pdfplumber
• Frontend: HTML, Bootstrap
• Containerization: Docker

Installation

Prerequisites

• Python 3.11+
• Docker (optional)

Using Docker (Recommended)

# Build the Docker image
docker build -t resume-parser .

# Run the container
docker run -it --rm -p 5000:5000 resume-parser

Access the application at: http://localhost:5000

Local Installation

1. Clone the repository:

git clone https://github.com/kumarBisho/Resume-Skill-Extractor.git
cd Resume-Skill-Extractor

2. Create and activate virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Run the application:

python resume_parser.py

Access the application at: http://localhost:5000

Usage

1. Upload a PDF resume using the file upload button
2. The system will automatically extract:
   • Basic information (name, email, phone)
   • Skills from predefined categories
   • Work experience
3. Use the search functionality to find resumes based on specific skills

Skill Categories

The system currently supports extracting skills from the following categories:

• Programming languages
• Web development
• Databases
• Cloud technologies
• Tools
• Machine Learning/Artificial Intelligence
• Data Structures and Algorithms

Project Structure

.
├── resume_parser.py         # Main application file
├── templates/               # HTML templates
│   └── index.html          # Main web interface
├── static/                  # Static files (CSS, JS)
├── uploads/                 # Directory for uploaded resumes
├── requirements.txt         # Python dependencies
├── Dockerfile              # Docker configuration
├── .gitignore              # Git ignore file
└── README.md               # This file

Contributing

1. Fork the repository
2. Create your 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

• Thanks to the developers of pdfplumber and Flask for their excellent libraries
• Special thanks to the open-source community for their contributions