📄 Resume Matcher - AI-Powered Resume Analysis Tool

A Streamlit web app that uses Natural Language Processing (NLP) to match resumes with job descriptions. It supports single or batch analysis, identifies skill gaps, and provides visual insights to help job seekers and recruiters make informed decisions.

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🚀 Features

• ✅ Semantic Similarity using TF-IDF + Cosine Similarity
• 📄 Supports PDF & TXT resumes
• 🧠 Skill Extraction using spaCy and pattern matching
• 📊 Interactive Visualizations with Plotly
• 📈 Improvement Suggestions for resume optimization
• 👥 Bulk Resume Comparison with ranking and breakdown

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🛠 Tech Stack

Component	Technology
Frontend	Streamlit
Backend/NLP	Python, spaCy
ML & Vectors	scikit-learn (TF-IDF)
PDF Processing	PyMuPDF
Visualization	Plotly

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📁 Project Structure

resume-matcher/
├── app.py                          # Main app
├── utils/                          # Modular utilities
│   ├── text_extractor.py          # File parsing
│   ├── similarity_analyzer.py     # Matching logic
│   ├── skill_extractor.py         # Skill identification
│   ├── visualizations.py          # Charting
│   └── improvement_suggestions.py # Resume tips

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⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/your-username/resume-matcher.git
cd resume-matcher

2. Create & Activate Virtual Environment

python -m venv venv
venv\Scripts\activate         # For Windows
# OR
source venv/bin/activate      # For macOS/Linux

3. Install Requirements

pip install -r requirements.txt
python -m spacy download en_core_web_sm

4. Run the Application

streamlit run app.py

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🎯 How to Use

• Single Resume Analysis Upload a resume and job description → Click Analyze → Get similarity score, skill gaps, and visualizations.

• Multi-Resume Comparison Upload multiple resumes + job description → Click Compare → See ranking, individual reports, and matched skills.

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❗ Troubleshooting

• Missing spaCy Model python -m spacy download en_core_web_sm

• Port Conflict streamlit run app.py --server.port 8502

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📄 License

Licensed under MIT License

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🙌 Acknowledgments

• Streamlit
• spaCy
• scikit-learn
• PyMuPDF

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Built with ❤️ for better hiring experiences ⭐ Star this repo

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