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https://github.com/gaurav-singh7092/resumatch

An AI-powered resume and job description matching application using natural language processing and machine learning techniques. This application provides intelligent analysis of resume-job compatibility with detailed scoring and recommendations.
https://github.com/gaurav-singh7092/resumatch

fastapi keyword-extraction nextjs nlp preprocessing-data python similarity-score tailwind

Last synced: about 1 month ago
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An AI-powered resume and job description matching application using natural language processing and machine learning techniques. This application provides intelligent analysis of resume-job compatibility with detailed scoring and recommendations.

Awesome Lists containing this project

README 

          
# ResuMatch 🎯



## Revolutionize Your Job Application Process



ResuMatch is an advanced AI-powered platform that transforms how job seekers and recruiters approach the hiring process. By leveraging cutting-edge natural language processing (NLP) and machine learning algorithms, ResuMatch analyzes the compatibility between resumes and job descriptions with unprecedented accuracy and insight.



### Why ResuMatch?



In today's competitive job market, standing out is essential. ResuMatch provides job seekers with data-driven insights to tailor their applications, while helping recruiters identify ideal candidates efficiently. Our intelligent matching system goes beyond simple keyword matching by understanding context, skills relevance, and experience alignment.



## Screenshots 📸



### Home Page

![ResuMatch Home Page](ResuMatch%20Home.png)



### Analysis Results

![ResuMatch Demo Results](ResuMatch%20Demo.png)



## Features ✨



- **Multi-format Support**: PDF, DOC, DOCX, TXT, and image files with text extraction

- **Text Preprocessing**: NLP pipeline with entity recognition, skill extraction, and text normalization

- **Similarity Analysis**: Semantic similarity calculation using sentence embeddings

- **Component Scoring**: Analysis across multiple dimensions:

  - Semantic similarity

  - Skill matching

  - Experience compatibility

  - Education alignment

  - Keyword overlap

- **Web Interface**: React-based frontend for easy interaction

- **REST API**: Backend API for integration with other applications

- **Batch Processing**: Analyze multiple resumes through API endpoints

- **Open Source**: Complete source code available for customization and extension



## Quick Start 🚀



### Prerequisites



- Python 3.8 or higher

- Node.js 16 or higher (for frontend)

- pip package manager



### Installation



1. **Clone the repository**:

```bash

git clone https://github.com/gaurav-singh7092/ResuMatch.git

cd ResuMatch

```



2. **Backend Setup**:

```bash

cd backend

python -m venv .venv

source .venv/bin/activate  # On Windows: .venv\Scripts\activate

pip install -r requirements.txt

python -m spacy download en_core_web_sm

```



3. **Frontend Setup**:

```bash

cd ../frontend

npm install

```



4. **Install Tesseract OCR** (for image processing):

   - **Ubuntu/Debian**: `sudo apt-get install tesseract-ocr`

   - **macOS**: `brew install tesseract`

   - **Windows**: Download from [GitHub](https://github.com/UB-Mannheim/tesseract/wiki)



### Running the Application



#### Quick Start with Development Script

```bash

# Setup everything

./dev.sh setup-all



# Run both backend and frontend

./dev.sh run-dev

```



#### Manual Setup



**Backend API Server**

```bash

cd backend

python main.py

```

The backend API will be available at `http://localhost:8000`



**Frontend Development Server**

```bash

cd frontend

npm run dev

```

The frontend will be available at `http://localhost:3000`



#### Development Helper Commands

```bash

./dev.sh setup-backend     # Setup Python environment

./dev.sh setup-frontend    # Setup Node.js environment  

./dev.sh setup-all         # Setup both environments

./dev.sh run-backend       # Start only backend

./dev.sh run-frontend      # Start only frontend

./dev.sh run-dev           # Start both services

./dev.sh clean             # Clean up generated files

./dev.sh help              # Show help

```



## Project Structure 📁



```

ResuMatch/

├── README.md                    # Main project documentation

├── backend/                     # Python backend application

│   ├── README.md               # Backend-specific documentation

│   ├── main.py                 # Flask/FastAPI application

│   ├── config.py               # Configuration settings

│   ├── similarity_engine.py    # Core matching algorithms

│   ├── text_extractor.py       # Document text extraction

│   ├── text_preprocessor.py    # NLP preprocessing

│   ├── examples.py             # Example scripts

│   ├── requirements.txt        # Python dependencies

│   ├── examples/               # Sample files and results

│   ├── results/                # Analysis output storage

│   ├── static/                 # Web assets

│   ├── templates/              # HTML templates

│   └── uploads/                # File upload storage

├── frontend/                   # Next.js frontend application

│   ├── README.md               # Frontend documentation

│   ├── package.json            # Node.js dependencies

│   ├── next.config.js          # Next.js configuration

│   ├── tailwind.config.js      # Tailwind CSS config

│   ├── components/             # React components

│   ├── pages/                  # Next.js pages

│   ├── services/               # API services

│   ├── hooks/                  # Custom React hooks

│   └── styles/                 # CSS styles

└── .venv/                      # Python virtual environment

```



## Usage Examples 📋



### Web Interface



1. **Single Resume Analysis**:

   - Upload your resume (PDF, DOC, DOCX, or TXT)

   - Paste the job description

   - Click "Analyze Match"

   - View detailed results with scores and recommendations



2. **Batch Analysis**:

   - Use the API endpoint `/batch-analyze` to process multiple resumes



### API Usage Examples



The project now focuses on web-based interaction through the frontend interface and REST API endpoints. For automated processing, you can use the API endpoints programmatically:



```bash

# Navigate to backend directory

cd backend



# Example API usage with curl

curl -X POST http://localhost:8000/api/analyze \

  -F "resume=@path/to/resume.pdf" \

  -F "job_description=Your job description text here"



# Or use the web interface at http://localhost:3000

```



## Architecture 🏗️



### Core Components



1. **Frontend (Next.js + TypeScript)**

   - Modern React-based web interface

   - Responsive design with Tailwind CSS

   - Real-time analysis results

   - File upload and management



2. **Backend API (Python)**

   - RESTful API server

   - File processing and analysis

   - Machine learning models

   - Database integration



3. **Text Extractor** (`backend/text_extractor.py`)

   - Supports multiple file formats

   - OCR for scanned documents

   - Fallback mechanisms for better reliability



4. **Text Preprocessor** (`backend/text_preprocessor.py`)

   - Advanced NLP pipeline

   - Skill and entity extraction

   - Statistical analysis



5. **Similarity Engine** (`backend/similarity_engine.py`)

   - Transformer-based embeddings

   - Multi-component scoring

   - Configurable weights



### Data Flow



```

Resume File → Text Extraction → Preprocessing → Feature Extraction

                                      ↓

Job Description → Preprocessing → Feature Extraction

                                      ↓

                              Similarity Analysis

                                      ↓

                            Detailed Results & Recommendations

```



## API Documentation 📚



### REST Endpoints



#### `POST /analyze`

Analyze a single resume against a job description.



**Parameters**:

- `resume`: File upload (multipart/form-data)

- `job_description`: Text (form field)



**Response**:

```json

{

  "analysis_id": "uuid",

  "similarity_analysis": {

    "overall_score": 75.5,

    "component_scores": {

      "semantic_similarity": 0.78,

      "skill_match": 0.65,

      "experience_match": 0.80,

      "education_match": 0.90,

      "keyword_match": 0.72

    },

    "matched_skills": ["python", "django", "rest api"],

    "missing_skills": ["react", "aws", "docker"],

    "recommendations": ["Add React experience", "Include cloud technologies"]

  }

}

```



#### `POST /batch-analyze`

Analyze multiple resumes against a job description.



#### `GET /analysis/{analysis_id}`

Retrieve detailed analysis results by ID.



#### `GET /health`

Health check endpoint.



#### `GET /api/stats`

Application statistics.



## Configuration ⚙️



### Environment Variables



```bash

# Server settings

HOST=0.0.0.0

PORT=8000

DEBUG=False



# Model settings

SENTENCE_MODEL=all-MiniLM-L6-v2

USE_GPU=False



# Processing settings

MAX_FILE_SIZE=52428800  # 50MB

MAX_BATCH_SIZE=10



# Component weights

WEIGHT_SEMANTIC=0.35

WEIGHT_SKILL=0.25

WEIGHT_EXPERIENCE=0.15

WEIGHT_EDUCATION=0.10

WEIGHT_KEYWORD=0.15

```



### Configuration Files



The application uses `config.py` for centralized configuration management with support for different environments (development, production, testing).



## Supported File Formats 📄



| Format | Extensions | Notes |

|--------|------------|-------|

| PDF | `.pdf` | Text-based and scanned (OCR) |

| Word | `.doc`, `.docx` | Microsoft Word documents |

| Text | `.txt` | Plain text files |

| Images | `.png`, `.jpg`, `.jpeg`, `.tiff`, `.bmp` | OCR processing |



## Scoring System 📊



### Component Weights (Default)



- **Semantic Similarity** (35%): Overall content alignment

- **Skill Match** (25%): Technical and soft skills overlap

- **Experience Match** (15%): Years and type of experience

- **Education Match** (10%): Educational background alignment

- **Keyword Match** (15%): Important keyword overlap



### Score Interpretation



- **75-100%**: Excellent match

- **60-74%**: Good match

- **45-59%**: Fair match

- **0-44%**: Poor match



## Advanced Features 🔧



### Skill Extraction



The system automatically extracts:

- Programming languages

- Frameworks and libraries

- Databases

- Cloud platforms

- Tools and technologies

- Soft skills

- Certifications



### Entity Recognition



Identifies:

- Contact information

- Company names

- Locations

- Dates

- Educational institutions



### Quality Assessment



Evaluates:

- Document completeness

- Text clarity

- Professional formatting

- Technical depth



## Performance Optimization 🚀



### Model Loading



- Lazy loading of heavy models

- Fallback to lighter models

- GPU acceleration support



### Processing



- Async file processing

- Batch optimization

- Caching mechanisms



### Memory Management



- Streaming file processing

- Garbage collection optimization

- Resource cleanup



## Development 👨‍💻



### Project Structure



```

ResuMatch/

├── main.py              # Web application

├── config.py            # Configuration management

├── text_extractor.py    # Text extraction engine

├── text_preprocessor.py # NLP preprocessing pipeline

├── similarity_engine.py # AI similarity analysis

├── requirements.txt     # Python dependencies

├── README.md           # Documentation

├── uploads/            # Uploaded files

├── results/            # Analysis results

└── static/             # Static web assets

```



### Running Tests



```bash

# Install test dependencies

pip install pytest pytest-asyncio



# Run tests

pytest tests/



# Run with coverage

pytest --cov=. tests/

```



### Adding New Features



1. **New File Format Support**:

   - Add extraction method to `TextExtractor`

   - Update supported formats list

   - Add file type detection



2. **New Skill Categories**:

   - Update skill patterns in `TextPreprocessor`

   - Add new regex patterns

   - Update feature extraction



3. **Custom Scoring**:

   - Modify weights in `SimilarityEngine`

   - Add new scoring components

   - Update analysis logic



## Troubleshooting 🔧



### Common Issues



1. **Model Loading Errors**:

   ```bash

   # Download models manually

   python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2')"

   ```



2. **OCR Not Working**:

   - Ensure Tesseract is installed and in PATH

   - Check image quality and format



3. **Memory Issues**:

   - Reduce batch size

   - Use lighter models

   - Enable garbage collection



4. **Performance Issues**:

   - Enable GPU acceleration

   - Use caching

   - Optimize preprocessing options



### Logging



```bash

# Enable debug logging

export LOG_LEVEL=DEBUG

python main.py

```



## Contributing 🤝



1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests

5. Submit a pull request



### Code Style



- Follow PEP 8

- Use type hints

- Add docstrings

- Write unit tests



## License 📝



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.



## Support 💬



For support, please:

1. Check the troubleshooting section

2. Review existing issues on GitHub

3. Create a new issue with detailed information



## Acknowledgments 🙏



- **Sentence Transformers**: For semantic similarity models

- **spaCy**: For NLP preprocessing

- **FastAPI**: For the web framework

- **scikit-learn**: For machine learning utilities

- **Tesseract**: For OCR capabilities



## Roadmap 🗺️



### Planned Features



- [ ] Database integration for result storage

- [ ] User authentication and profiles

- [ ] Advanced analytics dashboard

- [ ] Multi-language support

- [ ] Resume optimization suggestions

- [ ] Integration with job boards

- [ ] Machine learning model fine-tuning

- [ ] Mobile application

- [ ] Enterprise features



---



**ResuMatch** - Making resume screening intelligent and efficient! 🎯