AI Resume Analyzer & Job Matching Platform

Connecting talent to opportunity, intelligently.

Introduction • Key Features • Live Demo • Tech Stack • Getting Started •

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Introduction

In today's competitive job market, the hiring process is broken for everyone. Recruiters are inundated with an average of 49 applications per job, making manual screening impossible. Meanwhile, job seekers face the "ATS black box," where 93% of employers use systems that often reject qualified candidates based on simple formatting.

This project is an AI-powered, dual-sided platform designed to solve this problem. It provides job seekers with powerful tools for resume analysis and career path prediction, while simultaneously offering employers a curated dashboard of perfectly matched, pre-vetted talent. We transform a chaotic process into an intelligent and efficient ecosystem.

Key Features

For Job Seekers (Empowering Your Career)

• AI-Powered Resume Analysis: Get instant, actionable feedback on your resume to optimize it for both Applicant Tracking Systems (ATS) and human recruiters.
• Career Path Prediction: Our machine learning models analyze your skills and experience to predict the job fields where you'll be most successful.
• Multi-Format & Bulk Upload: Upload a single PDF, TXT, or even a ZIP file containing multiple resumes—perfect for managing different versions.
• Unlimited & Free Access: Our core analysis tools are free and unlimited to ensure every job seeker has the resources to succeed.

For Employers (Streamlining Your Hiring)

• Intuitive Talent Dashboard: Access a centralized dashboard of pre-analyzed and ranked candidates, turning a pile of resumes into a prioritized shortlist.
• Efficient Bulk Processing: Save countless hours by uploading hundreds of resumes at once via a single ZIP file. Our system handles the unpacking, parsing, and analysis automatically.
• Reduced Time-to-Hire: Quickly identify the most relevant, high-quality talent to drastically shorten your recruitment cycle and improve the quality of hires.

Live Demo

Check out the live platform here!

Technical Architecture

Our platform is built on a modern, scalable microservices architecture to ensure high performance, security, and maintainability.

• Frontend: A responsive and interactive web application built with Next.js and Tailwind CSS, providing a seamless user experience.
• Backend: A high-performance API built with FastAPI (Python) handles business logic, user authentication, and data processing.
• AI/ML Service: A dedicated microservice orchestrates our AI pipeline using LangChain. It leverages NLP models (like spaCy) for information extraction, Machine Learning models (Scikit-learn) for prediction, and Generative AI for nuanced analysis.
• Database: A robust PostgreSQL database securely stores all user data, extracted resume information, and predictions.
• Deployment: The entire application is containerized using Docker and deployed on a cloud platform like AWS for scalability and reliability.

Tech Stack

• Frontend: Next.js, React, Tailwind CSS, Framer Motion, Chart.js
• Backend & Database: Python, FastAPI, PostgreSQL, SQLAlchemy
• AI/ML: scikit-learn, spaCy, LangChain
• Deployment: Docker, AWS

Getting Started

To get a local copy up and running, follow these simple steps.

Prerequisites

Make sure you have the following installed on your system:

• Git
• Python 3.9+
• bun.sh
• PostgreSQL
• Docker Desktop (adviced for better DX)

Installation & Setup

1. Clone the repository:

   git clone https://github.com/harleenkaur28/AI-Resume-Parser.git
   cd AI-Resume-Parser

2. Setup the Backend (FastAPI):

   # Navigate to the backend directory
   cd backend
   
   # Create and activate a virtual environment
   python -m venv .venv
   source .venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   
   # Install dependencies
   pip install .
   
   # Create a .env file from the example
   cp .env.example .env

   Now, open the .env file and add your PostgreSQL database URL and other environment variables.

3. Setup the Frontend (Next.js):

   # Navigate to the frontend directory from the root
   cd frontend
   
   # Install dependencies
   npm install

Running the Application

1. Start the Backend Server: From the /backend directory, with your virtual environment activated:

   uvicorn app.main:app --reload

   The backend API will be running on http://127.0.0.1:8000.

2. Start the Frontend Development Server: From the /frontend directory:

   npm run dev

   Open http://localhost:3000 in your browser to see the application.

Team & Acknowledgments

This project was proudly developed by:

• Harleen Kaur - (GitHub) - Lead, Machine Learning, Backend Development
• Tashif Ahmad Khan - (GitHub) - Full-Stack Development, Designer