Skip to main content

This code simplifies the conversion of Pydantic schemas into Aiogram handler groups, making it easy to create form-filling handlers.

Project description

Pydantic-handler-converter

This code simplifies the conversion of Pydantic schemas into Aiogram handler groups, making it easy to create form-filling handlers.

Installation

    pip install pydantic_handler_converter

Usage:

>>> from enum import Enum
>>> from typing import Union
>>> from pydantic import BaseModel
>>> from pydantic_handler_converter import BasePydanticFormHandlers

# ----------------------------------------Simple datatypes schema--------------------------------------

>>> class PersonPydanticFormSchema(BaseModel):
...     name: str
...     age: int
...     height: float 
... 

>>> class PersonFormHanlders(BasePydanticFormHandlers[PersonPydanticFormSchema]):
...     pass
...
...
>>> dirs = dir(PersonFormHanlders)
>>> assert len(tuple(filter(lambda x: not x in dirs, ['name_view', 'age_view', 'height_view']))) == 0
>>> assert PersonFormHanlders(finish_call=None)

# ----------------------------------------Enum datatype schema-----------------------------------------

>>> class Mood(Enum):
...     HAPPY = "😄 Happy"
...     SAD = "😢 Sad"
...     EXCITED = "🤩 Excited"
...     RELAXED = "😌 Relaxed"
...
>>>
>>>
>>> class PersonMoodPydanticFormSchema(BaseModel):
...     name: str
...     current_mood: Mood
...
>>> class PersonMoodFormHanlders(BasePydanticFormHandlers[PersonMoodPydanticFormSchema]): 
...     pass
...
...
>>> dirs = dir(PersonMoodFormHanlders)
>>> assert len(tuple(filter(lambda x: not x in dirs, ['name_view', 'current_mood_view']))) == 0
>>> assert PersonMoodFormHanlders(finish_call=None)

# ----------------------------------------Complex schema-----------------------------------------------

>>> class Address(BaseModel):
...     street: str
...     city: str
...     postal_code: str
...
>>> class Person(BaseModel):
...      name: str
...      age: int
...      address: Address
...
...
>>> class PersonFormHanlders(BasePydanticFormHandlers[Person]): 
...     pass
...
...
>>> dirs = dir(PersonFormHanlders)
>>> assert len(tuple(filter(lambda x: not x in dirs, 
...     ['name_view', 'address_street_view', 'address_city_view', 'address_postal_code_view']
... ))) == 0
...
>>> assert PersonFormHanlders(finish_call=None)

# ------------------------------------Combined Enum datatype schema------------------------------------

>>> class HappyMood(Enum):
...     HAPPY = "😄 Happy"
...
>>> class SadMood(Enum):
...     SAD = "😢 Sad"
...
>>> class ExcitedMood(Enum):
...     EXCITED = "🤩 Excited"
...
>>> class RelaxedMood(Enum):
...     RELAXED = "😌 Relaxed"
...
>>>
>>> class PersonMoodPydanticFormSchema(BaseModel):
...     name: str
...     current_mood: Union[HappyMood, SadMood, ExcitedMood, RelaxedMood]
...     future_mood: HappyMood | SadMood | ExcitedMood | RelaxedMood
...
...
>>> class PersonMoodFormHanlders(BasePydanticFormHandlers[PersonMoodPydanticFormSchema]): 
...     pass
...
...
>>> dirs = dir(PersonMoodFormHanlders)
>>> assert len(tuple(filter(lambda x: not x in dirs, ['name_view', 'current_mood_view']))) == 0
>>> assert PersonMoodFormHanlders(finish_call=None)

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pydantic_handler_converter-0.1.102.tar.gz (14.2 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file pydantic_handler_converter-0.1.102.tar.gz.

File metadata

File hashes

Hashes for pydantic_handler_converter-0.1.102.tar.gz
Algorithm Hash digest
SHA256 574a51922b362d19b2b7bd3e8728b1a7af9df95fc337211a418dfd826b791ce0
MD5 483263b366a99ae621f31e6aa4a6439a
BLAKE2b-256 88b769cf7f32c82a04434043eb5031cc6b9d7a4143a955dcc7a8a47d0656ef7f

See more details on using hashes here.

File details

Details for the file pydantic_handler_converter-0.1.102-py3-none-any.whl.

File metadata

File hashes

Hashes for pydantic_handler_converter-0.1.102-py3-none-any.whl
Algorithm Hash digest
SHA256 45d81363e3db939fae9fac01fa8339170cd3203316e1dec5b137e591c0b03f6b
MD5 74314e617bd63dd845ed53ea4b72d865
BLAKE2b-256 0687329981c4ab0a9fdce63205f5b9ae9b8c2787e36f56d773f4d44fffb7890f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page