Resume Evaluator and ATS Score Analyzer

Overview

The Resume Evaluator and ATS Score Analyzer is a Streamlit web application that leverages Google’s Gemini-1.5-Pro model to analyze resumes against job descriptions. It helps job seekers evaluate how well their resumes match a given job description and provides insights into areas for improvement. The app also calculates an ATS (Applicant Tracking System) score to help users understand how their resumes would perform in an automated screening process.

Features

• AI-Powered Resume Evaluation – Leverages Gemini-1.5-Pro for intelligent evaluation of resumes against job descriptions.
• ATS Score Calculation – Provides a percentage match between the resume and job description, including missing/extra skills.
• PDF Resume Upload – Upload resumes in PDF format for processing.
• Resume Improvement Insights – Offers recommendations on how to improve the resume based on job requirements.
• User-Friendly Interface – Built using Streamlit for easy interaction.

Demo

Experience the live application: Resume Evaluator and ATS Score Analyzer

Tech Stack

• Python – Backend logic
• Streamlit – Web-based user interface
• Google Gemini-1.5-Pro API – AI-based resume analysis and ATS scoring
• PyMuPDF (fitz) – For extracting text from PDF resumes
• dotenv – Secure API key management

Installation & Setup

1. Clone the repository:

git clone https://github.com/PriyanshuLathi/Resume-Evaluator-and-ATS-Score-Analyzer.git
cd Resume-Evaluator-and-ATS-Score-Analyzer

2. Create and Activate a Virtual Environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Set up Gemini API key:

Create a .streamlit/secrets.toml file and add your Gemini API key:

[secrets]
API_KEY = "your_api_key_here"

Alternatively, you can create a .env file in the project root and add your Gemini API key:

API_KEY=your_api_key_here

5. Run the application:

streamlit run app.py

Usage

• Open the web interface.
• Enter the Job Description in the provided text area.
• Upload your resume (PDF format) using the uploader.
• Click "Tell Me About the Resume" to get a detailed resume evaluation.
• Click "Get ATS Score" to calculate the ATS score and match percentage.

Project Structure

📂 Resume-Evaluator-and-ATS-Score-Analyzer
│── app.py                # Main Streamlit app
│── requirements.txt      # Dependencies
│── .streamlit/secrets.toml  # API key storage (ignored in .gitignore)
│── README.md             # Documentation

Example Output

Input:

Job Description: We are looking for a Data Scientist with expertise in Python, machine learning, and NLP.
Resume: (Uploaded as PDF)

Output:

Resume Analysis:

- The candidate demonstrates strong Python and machine learning skills but lacks experience in NLP-related projects.
- The resume could benefit from showcasing more specific NLP expertise.

ATS Score:

- The candidate demonstrates strong Python and machine learning skills but lacks experience in NLP-related projects.
- The resume could benefit from showcasing more specific NLP expertise.

Conclusion

The Resume Evaluator and ATS Score Analyzer helps job seekers tailor their resumes to match job descriptions, providing valuable insights for both human and automated screening. By leveraging Google's Gemini-1.5-Pro model, this tool helps users optimize their resumes for higher chances of success.

With an intuitive Streamlit interface and secure API key management via dotenv, the application is easy to use and deploy. Future enhancements could include multi-language support, deeper integration with job boards, and automated resume formatting tips.

Contributing

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.

2. Create a new branch:

git checkout -b feature-branch-name

3. Make your changes and commit them:

git commit -m "Add new feature"

4. Push to the branch:

git push origin feature-branch-name

5. Submit a pull request detailing your changes.

License

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

Contact

For questions or feedback:

• LinkedIn: Priyanshu Lathi
• GitHub: Priyanshu Lathi

Authors

• Priyanshu Lathi