README.md

metadata

title: AI Content Summariser API
emoji: 📝
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
license: mit

AI Content Summariser API (Backend)

This is the backend API for the AI Content Summariser, a tool that automatically generates concise summaries of articles, documents, and web content using natural language processing.

The frontend application is available in a separate repository: ai-content-summariser.

Features

• Text summarization using state-of-the-art NLP models (BART-large-CNN)
• URL content extraction and summarization
• Adjustable parameters for summary length and style
• Efficient API endpoints with proper error handling

API Endpoints

• POST /api/summarise - Summarize text content
• POST /api/summarise-url - Extract and summarize content from a URL

Technology Stack

• Framework: FastAPI for efficient API endpoints
• NLP Models: Transformer-based models (BART) for summarisation
• Web Scraping: BeautifulSoup4 for extracting content from URLs
• HTTP Client: HTTPX for asynchronous web requests
• Deployment: Hugging Face Spaces or Docker containers

Getting Started

Prerequisites

• Python (v3.8+)
• pip

Installation

# Clone the repository
git clone https://github.com/dang-w/ai-content-summariser-api.git
cd ai-content-summariser-api

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running Locally

# Start the backend server
uvicorn main:app --reload

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

Testing

The project includes a comprehensive test suite covering both unit and integration tests.

Installing Test Dependencies

pip install pytest pytest-cov httpx

Running Tests

# Run all tests
pytest

# Run tests with verbose output
pytest -v

# Run tests and generate coverage report
pytest --cov=app tests/

# Run tests and generate detailed coverage report
pytest --cov=app --cov-report=term-missing tests/

# Run specific test file
pytest tests/test_api.py

# Run tests without warnings
pytest -W ignore::FutureWarning -W ignore::UserWarning

Test Structure

• Unit Tests: Test individual components in isolation

  • tests/test_summariser.py: Tests for the summarization service

• Integration Tests: Test API endpoints and component interactions

  • tests/test_api.py: Tests for API endpoints

Mocking Strategy

For faster and more reliable tests, we use mocking to avoid loading large ML models during testing:

# Example of mocked test
def test_summariser_with_mock():
    with patch('app.services.summariser.AutoTokenizer') as mock_tokenizer_class, \
         patch('app.services.summariser.AutoModelForSeq2SeqLM') as mock_model_class:
        # Test implementation...

Continuous Integration

Tests are automatically run on pull requests and pushes to the main branch using GitHub Actions.

Running with Docker

# Build and run with Docker
docker build -t ai-content-summariser-api .
docker run -p 8000:8000 ai-content-summariser-api

Deployment

See the deployment guide in the frontend repository for detailed instructions on deploying both the frontend and backend components.

Deploying to Hugging Face Spaces

1. Create a new Space on Hugging Face
2. Choose Docker as the SDK
3. Upload your backend code
4. Configure the environment variables:
   • CORS_ORIGINS: Your frontend URL

Performance Optimizations

The API includes several performance optimizations:

1. Model Caching: Models are loaded once and cached for subsequent requests
2. Result Caching: Frequently requested summaries are cached to avoid redundant processing
3. Asynchronous Processing: Long-running tasks are processed asynchronously

Development

Testing the API

You can test the API endpoints using the built-in Swagger documentation at /docs when running locally.

Checking Transformers Installation

To verify that the transformers library is installed correctly:

python -m app.check_transformers

License

This project is licensed under the MIT License.