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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. | |
| ## 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. | |