ATS Resume Expert Using GenAI

Land more job interviews with our resume scanner that helps you optimize your resume for Applicant Tracking Systems (ATS).

Overview

Our ATS Resume Expert helps job seekers improve their resumes by providing detailed analysis against job descriptions. The tool highlights the strengths and weaknesses of a resume, identifies missing keywords, and suggests areas for improvement to increase the chances of getting hired.

Features

• Resume Analysis: Upload a resume and job description to get a detailed analysis of how well they match.
• Keyword Matching: Identify missing keywords in the resume.
• Interactive Visualizations: Visualize the analysis results with interactive charts.
• Downloadable Report: Download a comprehensive evaluation report of your resume.

Live Project Demo

https://ats-resume-expert-using-genai-agssmatmexm6hywaohk22a.streamlit.app/

Technology Stack

This project uses the following technologies:

• python
• StreamLit
• Google Generative AI
• PyMuPDF
• Pandas

How It Works

1. Upload your resume and job description.

2. Get a detailed analysis of your resume's match with the job description.

3. Identify missing keywords and areas for improvement.

Setup and Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/ats-resume-expert.git

2. Navigate to the project directory:

   cd ats-resume-expert

3. Create and activate a virtual environment:

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

4. Install the required dependencies:

   pip install -r requirements.txt
   

5. Create a .env file and add your Google API key:

   GOOGLE_API_KEY=your_google_api_key_here
   

6. Run the Streamlit app:

   streamlit run app.py
   

Development Process

• Ideation: Identified the need for a tool that helps job seekers optimize their resumes.
• Design: Created wireframes and designed the user interface.
• Implementation: Developed the backend logic and integrated the frontend with Streamlit.
• Testing: Conducted thorough testing to ensure the tool works as expected.
• Deployment: Deployed the application for public use.

Contributing

We welcome contributions to improve this project. Please follow these steps to contribute:

Fork the repository.

• Create a new branch: git checkout -b feature/your-feature-name.
• Make your changes and commit them: git commit -m 'Add some feature'.
• Push to the branch: git push origin feature/your-feature-name.
• Open a pull request.

License

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

Acknowledgements

• Streamlit
• Google Generative AI
• PyMuPDF
• Pandas

Contact

If you have any questions, feel free to reach out:

• Name: Atharv Pramod Jangam
• Email: atharvjangam30@gmail.com