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The project is built using Python, Flask, and scikit-learn. It can also be enhanced using machine learning models for better efficiency and accuracy — work on this is in progress.\n\n---\n\n## Features\n\n- Supports multiple resume formats: `.pdf`, `.docx`, `.txt`\n- Uses TF-IDF vectorization and cosine similarity for scoring\n- Ranks resumes based on relevance to the provided job description\n- Allows users to upload and store resumes in a custom local path\n- HTML/CSS templates can be customized as per your needs\n- Can be deployed on the web using services like PythonAnywhere\n\n---\n\n## APP Interface\n\n![App Interface](r2.png)\n\n![App Interface](r1.png)\n\n---\n\n## How It Works\n\n1. Users input a job description and upload up to 10 resumes.\n2. The application extracts text from all resumes:\n   - PDF: extracted using PyPDF2\n   - DOCX: extracted using docx2txt\n   - TXT: extracted via standard file read\n3. Text data is vectorized using `TfidfVectorizer` from scikit-learn.\n4. Cosine similarity is calculated between the job description and each resume.\n5. The top 3 matching resumes are displayed along with their similarity scores.\n\n---\n\n## Running the App Locally\n\n### Requirements\n\n- Python 3.7 or higher\n- Required Python packages (Check Requirements.txt)\n\n### Installation\n\n```bash\ngit clone https://github.com/ankitsharma-tech/resume-analyzer.git\ncd resume-analyzer\npip install -r requirements.txt\n```\n\n### Launch the Application\n\n```bash\npython main.py\n```\n\nResume files will be stored in the `uploads/` directory. You can change this path in the `main.py` file as needed.\n\n---\n\n## Deployment\n\nThis app can be deployed to web platforms such as:\n\n- PythonAnywhere\n- Render\n- Railway\n- Heroku (with appropriate setup like a `Procfile`)\n\n---\n\n## Collaboration and Development\n\nWork is ongoing to enhance the application using machine learning for smarter resume-job matching. Planned features include:\n\n- Deep learning-based similarity scoring\n- Better handling of resume formatting and structure\n- Filter options for specific qualifications, skills, or keywords\n- User accounts and dashboard\n\nContributions are welcome.\n\n---\n\n## Tech Stack\n\n- **Frontend**: HTML, CSS (with customization options)\n- **Backend**: Python Flask\n- **Text Processing**: TF-IDF, Cosine Similarity\n- **File Parsing**: PyPDF2, docx2txt\n\n---\n\n## Notes\n\nThis is a prototype and may not handle every edge case. It serves as a proof-of-concept for resume screening and ranking based on textual similarity. For production use, improvements in model robustness and user interface are recommended.\n\n---\n\n## License\n\n[Apache-2.0 License](LICENSE)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankitsharma-tech%2Fresume-analyzer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fankitsharma-tech%2Fresume-analyzer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankitsharma-tech%2Fresume-analyzer/lists"}