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Unpack dict based on pydantic BaseModel into model
#This is free and unencumbered software released into the public domain.
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#software to the public domain. We make this dedication for the benefit
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from pydantic import BaseModel
from typing import Optional, Type, TypeVar
Model = TypeVar("Model", bound=BaseModel)
def _load_model(t: Type[Model], o: dict) -> Model:
populated_keys = o.keys()
required_keys = set(t.schema()['required'])
missing_keys = required_keys.difference(populated_keys)
if missing_keys:
raise ValueError(f'Required keys missing: {missing_keys}')
all_definition_keys = t.schema()['properties'].keys()
return t(**{k: v for k, v in o.items() if k in all_definition_keys})
if __name__ == '__main__':
class Car(BaseModel):
year: Optional[int]
name: str
company: str
car_dict = {'name': 'Uncivic', 'company': 'Fonda'}
car = _load_model(Car, car_dict)
print(car)
@meadsteve

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meadsteve commented Aug 1, 2019

So I forked to https://gist.github.com/meadsteve/2f720bc3a6ca9f1166ec3d1f3ed14ed8 It adds a TypeVar Model which has to be an instance of BaseModel

@henriklindgren

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henriklindgren commented Aug 1, 2019

So I forked to https://gist.github.com/meadsteve/2f720bc3a6ca9f1166ec3d1f3ed14ed8 It adds a TypeVar Model which has to be an instance of BaseModel

Nice, works perfectly, I merged it. Thanks! :)

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