Diabetes Risk Assessment with LLM

A Python application that provides structured diabetes risk assessments using OpenAI's language models. The application processes patient data, generates risk assessments, and validates the output to ensure reliability and consistency.

Features

• Structured diabetes risk assessment using OpenAI's GPT models
• Input validation using Pydantic models
• Comprehensive test suite with unit and integration tests
• Mock-based testing to avoid real API calls during development
• Type hints and static type checking with mypy
• Code formatting with Black and isort

Prerequisites

• Python 3.10 or higher
• OpenAI API key
• pip (Python package manager)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/llm_output_validation.git
   cd llm_output_validation

2. Create and activate a virtual environment:

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

3. Install the required packages:

   pip install -r requirements.txt

4. Set up your environment variables:

   cp .env.example .env
   # Edit .env and add your OpenAI API key

Usage

from diabetes_diagnosis import get_risk

patient_data = {
    "age": 45,
    "bmi": 28.5,
    "glucose_level": 110,
    "family_history": "Father had type 2 diabetes",
    "symptoms": ["increased thirst", "frequent urination"],
    "blood_pressure": "130/85",
    "cholesterol": {"total": 210, "hdl": 45, "ldl": 130},
    "physical_activity": "sedentary"
}

result = get_risk(patient_data)
print(result)

Testing

The test suite includes unit tests and integration tests with mocked API responses.

Running Tests

# Run all tests
pytest tests/

# Run with coverage report
pytest --cov=diabetes_diagnosis tests/

# Generate HTML coverage report
pytest --cov=diabetes_diagnosis --cov-report=html tests/

Test Structure

• tests/test_diabetes.py: Core functionality tests
• tests/test_edge_cases.py: Edge case validation tests
• tests/test_mock_scenarios.py: Mocked API response tests

Development

Code Style

The project uses:

• Black for code formatting
• isort for import sorting
• mypy for static type checking

Development Workflow

1. Create a feature branch
2. Make your changes
3. Run tests and checks:

   black .
   isort .
   mypy .
   pytest

4. Commit your changes with a descriptive message
5. Open a pull request

Requirements

• Python 3.6+
• OpenAI API key