Solution

[jlaourayed]

No description provided.

[mateacademy-ai-mentor]

Review Summary

I am requesting changes because there is a functional gap in the caching behavior and a direct checklist violation that both need to be addressed before this can move forward. In app/main.py, inside the cache decorator where you build the cache key, using tuple(kwargs.items()) means calls like func(a=1, b=2) and func(b=2, a=1) will generate different keys and miss the cache; you should construct the kwargs part of the key with a deterministic ordering, e.g. based on sorted(kwargs.items()), so logically identical calls share the same cached result. In the same file, within the decorator’s closure (near the definition of the cache dictionary and key), variable names such as resultats_executions and cle are not clear English identifiers and violate [CHECKLIST ITEM #4]; please rename them to something descriptive like cache_store (or results_cache) and key so the intent is immediately obvious. These two changes are required because they directly affect correctness (consistent caching for equivalent arguments) and adherence to the explicit checklist you were asked to follow. As minor improvements (not blockers), consider wrapping the inner function with functools.wraps(func) to preserve metadata and adjusting the wrapper’s return type annotation to something like -> Any or omitting it, since -> func is misleading. Once you fix the cache-key construction and variable names, your decorator will align well with the task requirements and be a solid, readable solution.

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[mateacademy-ai-mentor]

This violates checklist item: "Use descriptive and correct variable names." The variables resultats_executions and cle are not descriptive English names; prefer clearer names like cache_store or results_cache and key to improve readability and follow the checklist.

[jlaourayed]

oui bien vu

[mateacademy-ai-mentor]

Constructing the cache key as tuple(kwargs.items()) can cause cache misses if the same keyword arguments are passed in a different order. Use a deterministic ordering such as tuple(sorted(kwargs.items())) so that keyword arguments {a=1, b=2} and {b=2, a=1} produce the same key.

[jlaourayed]

oui bien vu

[jlaourayed]

j'ai corrigé le tri des kwargs

[mateacademy-ai-mentor]

Review Summary

I am requesting changes because one explicit checklist requirement is still violated, even though the core caching behavior is correct and working. In app/main.py, the variables resultats_executions and cle (near the decorator’s cache dictionary and key-construction logic) are not descriptive English names, which breaks “Use descriptive and correct variable names” [CHECKLIST ITEM #1]; please rename them to something clearer like results_cache (or cache_store) and key so their intent is obvious. This change is required before the solution can be considered complete, since checklist items are part of the task’s functional requirements.

Separately, there is a minor improvement you can make: the wrapper’s return annotation -> func is misleading because it refers to the function object, not the return value type; consider removing the annotation or using a more accurate type such as -> Any. The caching logic itself (per-function cache, deterministic key for kwargs, and correct printing of cache vs new computation) looks good. Once you fix the variable names and optionally adjust the return annotation, your decorator will both meet the functional goals and align with the style checklist—nice work getting the behavior right so far.

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[mateacademy-ai-mentor]

This violates the checklist item "Use descriptive and correct variable names." The variables resultats_executions and cle are not descriptive English names. Rename them to something clear like results_cache (or cache_store) and key so the intent is obvious and matches the checklist.

[mateacademy-ai-mentor]

Optional/low-severity: the wrapper's return annotation -> func is misleading (it references the function object, not the return value type). Consider using -> Any (from typing) or omitting the return annotation.

[jlaourayed]

variables renommées en Anglais

[mateacademy-ai-mentor]

Review Summary

I am approving your solution because it correctly implements the caching decorator’s behavior: it keeps a per-function cache, supports both *args and **kwargs, and prints different messages for cached versus newly computed results. Your variable naming and structure are clear and readable (for example, using results_cache and key), which aligns well with [CHECKLIST ITEM #1] about descriptive names. There is one minor issue in app/main.py: on the line where you define the wrapper (def wrapper(... ) -> func:), the return annotation -> func is misleading, since func is the decorated function object, not the return type, and this can confuse readers and type checkers. A good improvement would be to either remove that return annotation or change it to something like -> Any and import Any from typing so that the annotation reflects that the wrapper returns whatever the original function returns. Because this is a small style/type-hint concern and your core logic is correct, this is an educational override approval to help you keep progressing while you refine details like type hints. Overall, you’ve done a solid job on the functionality—treat this type-hint tweak as a chance to polish already strong work.

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[mateacademy-ai-mentor]

The wrapper's return annotation -> func is misleading: func refers to the function object, not the return value type. Consider removing the annotation or using -> Any and importing Any from typing for clarity.

[jlaourayed]

"Type: change wrapper return annotation to Any for clarity