TextRefine Score Engine

TextRefine Score Engine is a comprehensive text analysis and scoring system that evaluates writing quality across multiple dimensions. The engine provides quantitative scores and qualitative feedback to help identify areas for improvement in written content.

Key Features

• Multi-dimensional Analysis: Evaluates text across four key dimensions
• Detailed Feedback: Provides specific, actionable suggestions for improvement
• RESTful API: Easy integration with other applications
• Docker Support: Simple deployment with containerization
• Extensible Architecture: Modular design for adding new analysis components

Scoring Components

TextRefine's scoring engine evaluates text across four key dimensions:

1. Correctness (35%)

• Grammar accuracy and syntax
• Spelling and typo detection
• Punctuation and mechanics
• Word usage and style
• Semantic and logical coherence
• Contextual appropriateness

2. Vocabulary (25%)

• Lexical diversity and richness
• Word choice precision
• Domain-specific terminology
• Register and tone consistency
• Sophistication of language

3. Readability (15%)

• Sentence structure complexity
• Readability metrics (Flesch-Kincaid, etc.)
• Information density
• Text organization
• Visual presentation

4. Coherence (25%)

• Paragraph structure
• Topic flow and transitions
• Logical progression of ideas
• Overall text cohesion
• Contextual relevance

Project Structure

scoreEngine/
├── api/                    # FastAPI application and endpoints
├── coherence/              # Coherence analysis module
├── commons/                # Shared utilities and models
├── correctness/            # Grammar and correctness scoring
├── language_tool/          # LanguageTool integration
├── readability/            # Readability analysis
├── vocabulary/             # Vocabulary analysis
├── .github/               # GitHub workflows and templates
├── tests/                  # Test suite
├── Dockerfile             # Container configuration
├── main.py                # Application entry point
├── models.py              # Core data models
├── requirements.txt       # Python dependencies
└── logging_config.py      # Logging configuration

Getting Started

Prerequisites

• Python 3.8+
• Docker (for containerized deployment)
• LanguageTool server (can be run in a container)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/TextRefine.git
   cd TextRefine/scoreEngine

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Running with Docker

1. Build the Docker image:

   docker build -t textrefine-scoreengine .

2. Run the container:

   docker run -p 8000:8000 textrefine-scoreengine

API Documentation

Once the service is running, you can access:

• API documentation: http://localhost:8000/docs
• Alternative documentation: http://localhost:8000/redoc

Running Tests

pytest -v

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions or feedback, please open an issue in the repository.