AI Resume Parser

Abstract

1. Introduction

A sophisticated web application designed to revolutionize the resume screening process by harnessing the power of multiple state-of-the-art AI models. This application provides comprehensive resume analysis, intelligent scoring, and detailed information extraction capabilities, making it an invaluable tool for HR professionals and recruiters.

The system leverages advanced natural language processing and machine learning techniques through integration with premium AI models via Sree Shop's API. By combining the strengths of multiple AI models including Google Gemini, GPT-4, DeepSeek, and Llama-3-70B, the application delivers highly accurate and nuanced analysis of candidate profiles.

The application is built on a modern tech stack featuring React and TypeScript for the frontend, ensuring a responsive and intuitive user experience, while the Node.js backend handles complex document processing and AI model orchestration. The architecture is designed for scalability and performance, capable of handling high-volume resume processing while maintaining quick response times.

2. Objective

A sophisticated web application designed to revolutionize the resume screening process by harnessing the power of multiple state-of-the-art AI models. This application provides comprehensive resume analysis, intelligent scoring, and detailed information extraction capabilities, making it an invaluable tool for HR professionals and recruiters.

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

3. Key Features / Modules

• Multi-format Support:
• Handles PDF, DOCX, PNG, and JPG files
• Intelligent text extraction from all supported formats
• AI-Powered Analysis:
• Multiple AI model support:
• Google Gemini
• DeepSeek
• Llama-3-70B
• Smart resume scoring against job descriptions
• Position matching validation

4. Technology Stack

5. System Requirements

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

• Node.js (LTS) and npm / yarn / pnpm
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/jerry-619/AI-Resume-Parser.git
cd AI-Resume-Parser

1. Clone the repository
2. Backend Setup
3. Frontend Setup

git clone https://github.com/jerry-619/AI-Resume-Parser.git
   cd AI-Resume-Parser

cd backend
   npm install

PORT=3000
   GEMINI_API_KEY=your_gemini_api_key
   OPENAI_API_KEY=your_openai_api_key
   OPENAI_BASE_URL=https://beta.sree.shop/v1
   DEEPSEEK_MODEL=your_deepseek_model
   CHATGPT_MODEL=your_chatgpt_model
   LLAMA_MODEL=your_llama_model

mkdir uploads

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 jerry-619 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.