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| title: Resumescreener V2 | |
| emoji: 🚀 | |
| colorFrom: red | |
| colorTo: red | |
| sdk: docker | |
| app_port: 8501 | |
| tags: | |
| - streamlit | |
| pinned: false | |
| short_description: Streamlit template space | |
| # 🤖 AI Resume Screener | |
| An advanced Streamlit application that automatically ranks candidate resumes against job descriptions using a sophisticated multi-stage AI pipeline. | |
| ## 🚀 Features | |
| ### Multi-Stage AI Pipeline | |
| 1. **FAISS Recall**: Semantic similarity search using BGE embeddings (top 50 candidates) | |
| 2. **Cross-Encoder Reranking**: Deep semantic matching using MS-Marco model (top 20 candidates) | |
| 3. **BM25 Scoring**: Traditional keyword-based relevance scoring | |
| 4. **Intent Analysis**: AI-powered candidate interest assessment using Qwen LLM | |
| 5. **Final Ranking**: Weighted combination of all scores | |
| ### Advanced AI Models | |
| - **Embedding Model**: BAAI/bge-large-en-v1.5 for semantic understanding | |
| - **Cross-Encoder**: cross-encoder/ms-marco-MiniLM-L6-v2 for precise ranking | |
| - **LLM**: Qwen2-1.5B with 4-bit quantization for intent analysis | |
| ### Multiple Input Methods | |
| - **File Upload**: PDF, DOCX, TXT files | |
| - **CSV Upload**: Bulk resume processing | |
| - **Hugging Face Datasets**: Direct integration with HF datasets | |
| ### Comprehensive Analysis | |
| - **Skills Extraction**: Technical skills and job-specific keywords | |
| - **Score Breakdown**: Detailed analysis of each scoring component | |
| - **Interactive Visualizations**: Charts and metrics for insights | |
| - **Export Capabilities**: Download results as CSV | |
| ## 📋 Requirements | |
| ### System Requirements | |
| - Python 3.8+ | |
| - CUDA-compatible GPU (recommended for optimal performance) | |
| - 8GB+ RAM (16GB+ recommended) | |
| - 10GB+ disk space for models | |
| ### Dependencies | |
| All dependencies are listed in `requirements.txt`: | |
| - streamlit | |
| - sentence-transformers | |
| - transformers | |
| - torch | |
| - faiss-cpu | |
| - rank-bm25 | |
| - nltk | |
| - pdfplumber | |
| - PyPDF2 | |
| - python-docx | |
| - datasets | |
| - plotly | |
| - pandas | |
| - numpy | |
| ## 🛠️ Installation | |
| 1. **Clone the repository**: | |
| ```bash | |
| git clone <repository-url> | |
| cd resumescreener_v2 | |
| ``` | |
| 2. **Install dependencies**: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. **Run the application**: | |
| ```bash | |
| streamlit run src/streamlit_app.py | |
| ``` | |
| ## 📖 Usage Guide | |
| ### Step 1: Model Loading | |
| - Models are automatically loaded when the app starts | |
| - First run may take 5-10 minutes to download models | |
| - Check the sidebar for model loading status | |
| ### Step 2: Job Description | |
| - Enter the complete job description in the text area | |
| - Include requirements, responsibilities, and desired skills | |
| - More detailed descriptions yield better matching results | |
| ### Step 3: Load Resumes | |
| Choose from three options: | |
| #### Option A: File Upload | |
| - Upload PDF, DOCX, or TXT files | |
| - Supports multiple file selection | |
| - Automatic text extraction | |
| #### Option B: CSV Upload | |
| - Upload CSV with resume texts | |
| - Select text and name columns | |
| - Bulk processing capability | |
| #### Option C: Hugging Face Dataset | |
| - Load from public datasets | |
| - Specify dataset name and columns | |
| - Limited to 100 resumes for performance | |
| ### Step 4: Run Pipeline | |
| - Click "Run Advanced Ranking Pipeline" | |
| - Monitor progress through 5 stages | |
| - Results appear in three tabs | |
| ### Step 5: Analyze Results | |
| #### Summary Tab | |
| - Top-ranked candidates table | |
| - Key metrics and scores | |
| - CSV download option | |
| #### Detailed Analysis Tab | |
| - Individual candidate breakdowns | |
| - Score components explanation | |
| - Skills and keywords analysis | |
| - Resume excerpts | |
| #### Visualizations Tab | |
| - Score distribution charts | |
| - Comparative analysis | |
| - Intent distribution | |
| - Average metrics | |
| ## 🧮 Scoring Formula | |
| **Final Score = 0.5 × Cross-Encoder + 0.3 × BM25 + 0.2 × Intent** | |
| ### Score Components | |
| 1. **Cross-Encoder Score (50%)** | |
| - Deep semantic matching between job and resume | |
| - Considers context and meaning | |
| - Range: 0-1 (normalized) | |
| 2. **BM25 Score (30%)** | |
| - Traditional keyword-based relevance | |
| - Term frequency and document frequency | |
| - Range: 0-1 (normalized) | |
| 3. **Intent Score (20%)** | |
| - AI-assessed candidate interest level | |
| - Based on experience-job alignment | |
| - Categories: Yes (0.9), Maybe (0.5), No (0.1) | |
| ## 🎯 Best Practices | |
| ### For Optimal Results | |
| 1. **Detailed Job Descriptions**: Include specific requirements, technologies, and responsibilities | |
| 2. **Quality Resume Data**: Ensure resumes contain relevant information | |
| 3. **Appropriate Batch Size**: Process 20-100 resumes for best performance | |
| 4. **Clear Requirements**: Specify must-have vs. nice-to-have skills | |
| ### Performance Tips | |
| 1. **GPU Usage**: Enable CUDA for faster processing | |
| 2. **Memory Management**: Use cleanup controls for large batches | |
| 3. **Model Caching**: Models are cached after first load | |
| 4. **Batch Processing**: Process resumes in smaller batches if memory limited | |
| ## 🔧 Configuration | |
| ### Model Configuration | |
| Models can be customized by modifying the `load_models()` function: | |
| - Change model names for different embeddings | |
| - Adjust quantization settings | |
| - Modify device mapping | |
| ### Scoring Weights | |
| Adjust weights in `calculate_final_scores()`: | |
| ```python | |
| final_scores = 0.5 * ce_scores + 0.3 * bm25_scores + 0.2 * intent_scores | |
| ``` | |
| ### Skills List | |
| Customize the predefined skills list in the `ResumeScreener` class: | |
| ```python | |
| self.skills_list = [ | |
| 'python', 'java', 'javascript', | |
| # Add your specific skills | |
| ] | |
| ``` | |
| ## 🐛 Troubleshooting | |
| ### Common Issues | |
| 1. **Model Loading Errors** | |
| - Check internet connection for model downloads | |
| - Ensure sufficient disk space | |
| - Verify CUDA compatibility | |
| 2. **Memory Issues** | |
| - Reduce batch size | |
| - Use CPU-only mode | |
| - Clear cache between runs | |
| 3. **File Processing Errors** | |
| - Check file formats (PDF, DOCX, TXT) | |
| - Ensure files are not corrupted | |
| - Verify text extraction quality | |
| 4. **Performance Issues** | |
| - Enable GPU acceleration | |
| - Process smaller batches | |
| - Use model quantization | |
| ### Error Messages | |
| - **"Models not loaded"**: Wait for model loading to complete | |
| - **"ML libraries not available"**: Install missing dependencies | |
| - **"CUDA out of memory"**: Reduce batch size or use CPU | |
| ## 📊 Sample Data | |
| Use the included `sample_resumes.csv` for testing: | |
| - 5 sample resumes with different roles | |
| - Realistic job experience and skills | |
| - Good for testing all features | |
| ## 🤝 Contributing | |
| 1. Fork the repository | |
| 2. Create a feature branch | |
| 3. Make your changes | |
| 4. Add tests if applicable | |
| 5. Submit a pull request | |
| ## 📄 License | |
| This project is licensed under the MIT License - see the LICENSE file for details. | |
| ## 🙏 Acknowledgments | |
| - **BAAI** for the BGE embedding model | |
| - **Microsoft** for the MS-Marco cross-encoder | |
| - **Alibaba** for the Qwen language model | |
| - **Streamlit** for the web framework | |
| - **Hugging Face** for model hosting and transformers library | |
| ## 📞 Support | |
| For issues and questions: | |
| 1. Check the troubleshooting section | |
| 2. Review error messages in the sidebar | |
| 3. Open an issue on GitHub | |
| 4. Check model compatibility | |
| --- | |
| **Built with ❤️ using Streamlit and state-of-the-art AI models** | |