Argument Mining API

A FastAPI-based service for analyzing argumentative structures in text using state-of-the-art NLP models.

Features

• Identification of argument components (claims, premises)
• Analysis of argumentative relations
• RESTful API with FastAPI
• Powered by BERT and spaCy models
• Comprehensive test suite

Requirements

• Python 3.12.1 or higher
• Hugging Face account and API token
• CUDA-compatible GPU (optional, for faster inference)

Installation

1. Clone the repository:

git clone https://github.com/Horizontal-Labs/pipeline
cd pipeline

2. Create and activate a virtual environment:

python -m venv .venv
# On Windows:
.venv\Scripts\activate
# On Unix or MacOS:
source .venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Download the spaCy model:

python -m spacy download en_core_web_lg

5. Set up environment variables: Create a .env file in the project root with:

HF_TOKEN=your_huggingface_token_here

Usage

1. Start the server:

python main.py

2. The API will be available at http://localhost:8000

3. Access the interactive API documentation:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Example API Request

import requests

response = requests.post(
    "http://localhost:8000/analyze",
    json={
        "text": "Global warming is a serious threat. Since temperatures are rising worldwide, we need to act now."
    }
)

print(response.json())

API Endpoints

POST /analyze

Analyzes text for argument components and relations.

Request body:

{
    "text": "string"
}

Response:

{
    "components": [
        {
            "text": "string",
            "type": "claim|premise|non-argument",
            "confidence": 0.95
        }
    ],
    "relations": [
        {
            "source_idx": 0,
            "target_idx": 1,
            "relation_type": "support|attack",
            "confidence": 0.8
        }
    ]
}

GET /health

Health check endpoint.

Response:

{
    "status": "healthy"
}

Project Structure

ArgumentMining/
├── app/
│   ├── __init__.py
│   ├── models.py      # Pydantic models
│   ├── routes.py      # API endpoints
│   └── services.py    # Core business logic
├── tests/
│   ├── __init__.py
│   ├── conftest.py
│   ├── test_api.py
│   ├── test_argument_mining.py
│   └── test_model.py
├── main.py            # Application entry point
├── requirements.txt
└── README.md

Testing

Run the test suite:

pytest

For test coverage report:

pytest --cov=. --cov-report=term-missing

Current test coverage: 93%

Development

• The project uses FastAPI for the web framework
• Models are loaded using the Hugging Face Transformers library
• Argument component classification uses BERT
• Sentence segmentation uses spaCy
• Testing uses pytest with pytest-cov for coverage reporting

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Acknowledgments

• FastAPI
• Hugging Face Transformers
• spaCy
• BERT