Smart Resume Screener

A smart resume screening application using Node.js, React, and the Gemini API.

AI & Machine LearningJavaScriptMIT

Abstract

Smart Resume Screener is an open-source AI & Machine Learning project. A smart resume screening application using Node.js, React, and the Gemini API. The Smart Resume Screener is a sophisticated, full-stack web application designed to revolutionize the initial stages of the hiring process. This tool empowers recruiters and hiring managers to move beyond manual, time-consuming resume reviews by leveraging advanced AI to intelligently parse, score, and rank candidates in bulk. It is built using JavaScript. Key capabilities include: Secure Multi-User Authentication; AI-Powered Multi-Criteria Scoring; Experience. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

The Smart Resume Screener is a sophisticated, full-stack web application designed to revolutionize the initial stages of the hiring process. This tool empowers recruiters and hiring managers to move beyond manual, time-consuming resume reviews by leveraging advanced AI to intelligently parse, score, and rank candidates in bulk.

Built with the modern MERN stack and powered by the Gemini API, this application provides a secure, multi-user environment where users can manage screening history, analyze candidates with a detailed, multi-criteria scoring system, and generate professional PDF reports for offline sharing.

An AI-powered resume screener that automates candidate evaluation and generates professional PDF reports.

2. Objective

A smart resume screening application using Node.js, React, and the Gemini API.

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

3. Key Features / Modules

  • Secure Multi-User Authentication
  • AI-Powered Multi-Criteria Scoring
  • Experience
  • Project Quality
  • Education
  • Batch Resume Processing
  • Interactive Analytics Dashboard
  • Number of top-tier candidates
  • Score distribution donut chart for at-a-glance insights.
  • Professional PDF Report Generation

4. Technology Stack

JavaScript

5. System Requirements

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

  • Node.js (LTS) and npm
  • A modern web browser
  • VS Code or any code editor
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/divyanshu02raj/Smart-Resume-Screener.git
cd Smart-Resume-Screener
cd backend
npm install
cd frontend
npm install

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 divyanshu02raj 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.

View Source on GitHubDownload ZIP

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