py_cache_decorator solution

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[mateacademy-ai-mentor]

Review Summary

Your solution meets the core task requirements: the cache decorator correctly stores results per decorated function, avoids repeated computations for the same positional arguments, and prints exactly "Calculating new result" and "Getting from cache" in app/main.py. The logic using a per-function results dictionary is a solid design choice that keeps caches isolated and ensures correct behavior for multiple decorated functions. One improvement to consider in the wrapper definition (near the def wrapper(...): in app/main.py) is to support keyword arguments by changing the signature to def wrapper(*args, **kwargs) and building a cache key that combines args with a deterministic representation of kwargs, such as (args, tuple(sorted(kwargs.items()))). This would prevent logically equivalent calls that differ only in keyword usage or ordering from bypassing the cache. Another non-blocking enhancement is to preserve the original function’s metadata by importing wraps from functools and decorating the wrapper with @wraps(func), so attributes like __name__ and __doc__ remain accurate. Overall, your implementation is clear, avoids duplication, and satisfies the functional requirements, with these suggestions serving as good next steps for robustness and best practices.

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[mateacademy-ai-mentor]

The wrapper only accepts positional arguments. To correctly cache calls that use keyword arguments (and to avoid treating equivalent calls differently), change the signature to def wrapper(*args, **kwargs) and incorporate a hashable representation of kwargs into the cache key (for example, key = (args, tuple(sorted(kwargs.items())))).

[mateacademy-ai-mentor]

The code uses args alone as the dictionary key. If you add **kwargs support, update key construction here and where you store/lookup results so both positional and keyword arguments influence the cache key consistently and deterministically.

[mateacademy-ai-mentor]

Consider preserving the decorated function's metadata by using functools.wraps. Add from functools import wraps at the top and decorate wrapper with @wraps(func) so __name__, __doc__, etc. are preserved.