OncoAI Survival Prediction API

Overview

The OncoAI Survival Prediction API is a web service designed to predict patient survival rates in oncology using machine learning models. This API provides endpoints for user authentication, survival prediction, and health checks.

Features

• User Authentication: Secure user registration and login using JWT tokens.
• Survival Prediction: Predict patient survival rates based on input features.
• Health Checks: Verify the status of the API, database connection, and model loading.

Project Structure

The project is structured as follows:

• app/: Contains the main application code.
  • main.py: The entry point of the application.
  • api/: Contains the API endpoints.
    • auth.py: Authentication-related API endpoints.
    • survival.py: Survival analysis-related API endpoints.
  • core/: Contains core application logic.
    • config.py: Configuration settings.
    • model.py: Data models.
    • security.py: Security-related functions.
    • utils.py: Utility functions.
    • models/: Contains additional data models.
  • db/: Contains database-related code.
    • crud.py: CRUD operations.
    • models.py: Database models.
    • session.py: Database session management.
  • schemas/: Contains Pydantic schemas.
    • auth.py: Authentication-related schemas.
    • survival.py: Survival analysis-related schemas.
• entorno/: Contains environment-related files.
• static/: Contains static files.
  • neon-app.js: JavaScript file for the Neon app.
  • neon-dark.css: CSS file for the Neon app.
  • oncoai-dashboard.html: HTML file for the OncoAI dashboard.
• test.db: SQLite database file.

Dependencies

The application uses the following dependencies:

• FastAPI: A modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.
• SQLAlchemy: The Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL.
• Pydantic: Data validation and settings management using Python type annotations.
• Uvicorn: An ASGI server for serving FastAPI applications.
• Passlib: Password hashing library for Python.
• PyJWT: JSON Web Token implementation in Python.
• Python-dotenv: Reads key-value pairs from a .env file and sets them as environment variables.

Setup

To set up the application, follow these steps:

1. Create a virtual environment and activate it.
2. Install the dependencies using pip install -r requirements.txt.
3. Create a .env file with the necessary environment variables.
4. Run the application using uvicorn app.main:app --reload.

Testing

To run the tests, use the following command:

pytest

API Endpoints

Authentication

• POST /auth/register: Register a new user.
• POST /auth/login: Log in an existing user.
• POST /token: Obtain an access token for authentication.

Survival Prediction

• POST /api/predict: Predict patient survival rates based on input features.

Health Check

• GET /health: Verify the status of the API, database connection, and model loading.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License.