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2162b17 38d6a05 2162b17 f0b009f 2162b17 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | # 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.
## π Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Technology Stack](#technology-stack)
- [Installation](#installation)
- [Usage](#usage)
- [How It Works](#how-it-works)
- [API Endpoints](#api-endpoints)
- [Deployment](#deployment)
- [Author](#author)
## π― 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.
## β¨ 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
## π 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
## π 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).
## π¨βπ» Author
**Pradyumn Tendulkar**
Data Science Graduate Student | ML Engineer
- GitHub: [@pradyten](https://github.com/pradyten)
- LinkedIn: [Pradyumn Tendulkar](https://www.linkedin.com/in/p-tendulkar/)
- Email: pktendulkar@wpi.edu
---
β If you found this project helpful, please consider giving it a star!
π **License:** MIT
π‘ **Contributing:** Pull requests are welcome! For major changes, please open an issue first to discuss proposed changes.
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