t1d-food-module-scaffold

Build a food data processing module by creating a Python file in your app directory, defining dataclasses for type-safe contracts, and writing comprehensive tests to validate nutrient extraction and serving normalization logic.

The best way to ensure type safety in food data modules is to use dataclass-based contracts for your Python structures, providing clarity and robust type checking for processes like nutrient extraction and provenance tracking.

Provenance tracking in a food data processing module works by utilizing dataclass-based contracts to maintain type-safe records of data origin and transformations, ensuring reliable traceability when developing within the T1D Companion project structure.

You can use this scaffold to build serving normalization modules without extra libraries by leveraging native Python dataclasses for type safety and implementing comprehensive testing protocols within your existing project structure.

A limitation of using dataclass-based contracts for food module testing is that while they provide strong type safety for nutrient extraction and serving normalization, they require manual implementation of comprehensive test protocols in the tests directory to validate complex data transformations.