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Resume Analyzer & Job Match System

AI-powered resume analysis tool using NLP and deep learning to compare resumes with job descriptions and provide detailed matching scores.

📋 Table of Contents

🎯 Overview

This application analyzes resumes against job descriptions using state-of-the-art NLP models to provide:

  • Overall compatibility scores
  • Section-by-section analysis
  • Keyword matching
  • Skill gap identification
  • Improvement suggestions

Built with Gradio for an interactive web interface and optimized for deployment on Hugging Face Spaces.

✨ Features

  • Multi-Model Analysis: Uses BERT, Sentence Transformers, and TF-IDF for comprehensive matching
  • Document Support: Accepts PDF and DOCX formats for both resumes and job descriptions
  • Detailed Scoring: Provides scores for:
    • Overall match percentage
    • Skills alignment
    • Experience relevance
    • Education compatibility
    • Keyword density
  • Visual Feedback: Generates word clouds and similarity visualizations
  • API Support: FastAPI endpoints for programmatic access
  • Cloud-Ready: Optimized for Hugging Face Spaces deployment

🛠 Technology Stack

Core ML/NLP

  • PyTorch - Deep learning framework
  • Transformers (Hugging Face) - BERT models for contextual understanding
  • Sentence Transformers - Semantic similarity with `all-MiniLM-L6-v2`
  • Scikit-learn - TF-IDF vectorization and cosine similarity

Document Processing

  • PyMuPDF (fitz) - PDF text extraction
  • python-docx - Word document processing

Web Framework

  • Gradio - Interactive web UI
  • FastAPI - REST API endpoints
  • Uvicorn - ASGI server

Visualization

  • Matplotlib - Plotting and charts
  • WordCloud - Visual keyword representation

🚀 Installation

Prerequisites

  • Python 3.8 or higher
  • pip package manager
  • 4GB+ RAM (for transformer models)

Setup

  1. Clone the repository: ```bash git clone https://github.com/pradyten/Resume-Comparator.git cd Resume-Comparator ```

  2. Create a virtual environment (recommended): ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ```

  3. Install dependencies: ```bash pip install -r requirements.txt ```

Note: The installation may take several minutes as it downloads pre-trained transformer models (~400MB).

👨‍💻 Author

Pradyumn Tendulkar

Data Science Graduate Student | ML Engineer

⭐ If you found this project helpful, please consider giving it a star!

📝 License: MIT

💡 Contributing: Pull requests are welcome! For major changes, please open an issue first to discuss proposed changes.