Resume-ATS-system

Automated Resume Parser Description: Create a system to extract and categorize information from resumes (PDFs/Docs). Technologies: Python (spaCy, PDFPlumber), Flask, PostgreSQL Outcome: Extract candidate details (name, skills, education) and store them in a searchable database.

Resume Parser API

A Flask-based web application that provides an API for parsing resumes and extracting useful information using the pyresparser library.

Features

• Parses resumes in PDF format to extract key details such as name, email, phone, skills, experience, education, etc.
• Provides a simple API endpoint for uploading and processing resumes.
• Includes a health check endpoint to verify the application's status.

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Requirements

• Python 3.7 or later
• Virtual Environment (optional but recommended)
• Libraries specified in the requirements.txt file

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Installation

1. Clone this repository:

   git clone https://github.com/<your-username>/resume-parser-api.git
   cd resume-parser-api

2. Set up a virtual environment (optional but recommended):

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Download NLTK stopwords:

   python -c "import nltk; nltk.download('stopwords')"

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Usage

1. Start the application:

   python app.py

   The application will run on http://0.0.0.0:8030.

2. Test the health check endpoint:

   curl http://localhost:8030/ping

3. Parse a resume:

   • Use a tool like Postman or curl to send a POST request to /resume-parser with a resume file.

   curl -X POST -F "resume=@path/to/resume.pdf" http://localhost:8030/resume-parser

   • Replace path/to/resume.pdf with the actual file path of your resume.

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API Endpoints

1. Health Check

• Endpoint: /ping
• Method: GET
• Response:

  {
      "status": "Healthy"
  }

2. Resume Parsing

• Endpoint: /resume-parser
• Method: POST
• Parameters:
  • resume: A PDF file of the resume.
• Response: Extracted data in JSON format.
  Example:

  {
      "name": "John Doe",
      "email": "john.doe@example.com",
      "phone": "1234567890",
      "skills": ["Python", "Flask", "Machine Learning"],
      ...
  }

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Logging

The application uses Python's logging module to log debugging information and errors to the console.

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Warning Handling

Warnings are suppressed to avoid unnecessary clutter in the output. This can be adjusted by modifying the warnings.filterwarnings setting in the code.

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Deployment

This application can be deployed using platforms like Heroku, AWS, Google Cloud, or any containerized service like Docker.

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Contributing

1. Fork this repository.
2. Create a feature branch.
3. Commit your changes and push.
4. Open a pull request.

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License

This project is licensed under the MIT License.

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Author

Twisha
Feel free to connect for questions or collaborations!