Resume GPT

Abstract

1. Introduction

Resume GPT is an efficient recruitment assistant powered by OpenAI's GPT-3.5. The tool allows recruiters to upload multiple resumes along with a job description. You can further specify mandatory keywords that should be present in a candidate's resume. As a result, the tool will provide an evaluation of each resume against the provided job description and give insights into the suitability of the candidate for the role.

2. Objective

Resume GPT is a cutting-edge recruitment tool tailored to simplify and enhance the resume screening process for recruiters and hiring managers. By leveraging the power of OpenAI's GPT-3.5 model, it aids in automating the evaluation of multiple resumes in relation to a specific job description.

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Bulk upload of resumes in PDF format.
• Specification of a job description to compare the resumes against.
• Option to add mandatory keywords for the desired candidate profile.
• Analysis of resumes using OpenAI GPT-3.5 to generate suitability remarks.
• Downloadable results in a CSV format with columns for resume name, comments, and suitability.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/aliaryan/ResumeGPT.git
cd ResumeGPT

1. Clone the repository.
2. Change directory to the cloned repository.
3. Install the required packages.
4. Update the OpenAI API key in the openai.api_key = "OPEN AI KEY" line with your own key.
5. Run the application.
6. Access the tool by visiting http://localhost:5000 in your browser.
7. Upload the desired resumes.
8. Provide a detailed job description.

git clone https://github.com/aliaryan/ResumeGPT.git

cd resume-gpt

pip install Flask openai pdfplumber

python screening_tool.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by aliaryan and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.