ATS (Applicant Tracking System) with Generative AI

📄 Project Overview

This project demonstrates the creation of an Applicant Tracking System (ATS) powered by Generative AI (GenAI) models. The system leverages AI capabilities to streamline recruitment processes by:

• Matching candidates to job roles.
• Automating candidate communication.
• Advising on the chances that the resume provided fits the job description.
• Provides strengths and weakness of a resume in relation to a job description.

By integrating GenAI, the ATS provides smarter, faster, and more accurate talent acquisition workflows.

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🎯 Key Features

• Resume Parsing: Extracts key details such as name, skills, work history, and education from resumes in PDF or DOCX format.
• Job Description Generation: Leverages Generative AI to craft precise and engaging job descriptions for any role. Candidate Matching: Analyzes candidate profiles and job descriptions to compute compatibility scores.
• Fit Assessment: Provides a detailed analysis of how well a resume matches a job description and advises on overall fit chances. Strengths and Weaknesses Analysis: Highlights the strengths and areas for improvement in a candidate's resume relative to a job description.
• Automated Communication: Drafts customized emails for candidate updates, interview invitations, or feedback.

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⚙️ Tech Stack

• Programming Language: Python
• Framework: Streamlit (for the UI)
• AI Models: GenAI GPT models (e.g., gemini-1.5)
• Libraries:
  • pandas - Data manipulation.
  • streamlit - User interface.
  • genai - API integration for GenAI.
  • pdf2image - Load in a pdf and convert to image

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🚀 Setup Instructions

Prerequisites

• Python 3.10+
• GenAI API key (for GPT model usage)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/ATS-system.git
   cd ATS-system

2. Load in the environment:

   conda create -f environment.yml

3. Set up the GenAI API key:
   • Create a .env file in the project directory.
   • How to create a GenAI API key
   • Add your API key:

     OPENAI_API_KEY=your_openai_api_key
     

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🖥️ Usage

1. Run the application:

   streamlit run app.py

2. Navigate to the URL provided by Streamlit (usually http://localhost:8501).
3. Use the following features:
   • Upload resumes in PDF or DOCX format for parsing.
   • Input job titles to generate descriptions.
   • View candidate-job matches based on AI scores.

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📚 Project Structure

ATS-GenAI/
│
├── app.py                # Streamlit app
├── resume_parser.py      # Resume parsing logic
├── job_description.py    # Job description generator
├── candidate_matcher.py  # Candidate ranking and matching
├── .env                  # API key file
├── requirements.txt      # Python dependencies
├── README.md             # Project documentation
└── data/
    ├── sample_resumes/   # Sample resumes for testing
    ├── output/           # Parsed and matched results

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🧪 Example Scenarios

Scenario 1: Job Description Generation

• Input: "Software Engineer"
• Output: A detailed job description including roles, responsibilities, and qualifications.

Scenario 2: Resume Parsing and Matching

• Input: A resume file and a job description.
• Output: Fit score (e.g., "85% match"). Strengths and weaknesses analysis.

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🛠️ Future Enhancements

• Integration with LinkedIn for candidate sourcing.
• Advanced analytics dashboard for recruiters.
• Support for multi-language parsing and matching.
• Integration with third-party email platforms for automated scheduling.

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👨‍💻 Author

• Nanyaemuny Savins - GitHub Profile

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📄 License

This project is licensed under the MIT License. See LICENSE for more details.

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🙌 Acknowledgments

• GenAI for providing the Generative AI models.
• Streamlit for the interactive UI framework.
• Community contributors for testing and feedback.

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