99 lines
3.6 KiB
Python
99 lines
3.6 KiB
Python
from inflection import underscore
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, Field, create_model
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from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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import inspect
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class ModelDef(BaseModel):
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"""Assistance Class for Pydantic Dynamic Model Generation"""
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field: str
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field_alias: str
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field_type: Any
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field_value: Any
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class pydanticModelGenerator:
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"""
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Takes source_data:Dict ( a single instance example of something like a JSON node) and self generates a pythonic data model with Alias to original source field names. This makes it easy to popuate or export to other systems yet handle the data in a pythonic way.
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Being a pydantic datamodel all the richness of pydantic data validation is available and these models can easily be used in FastAPI and or a ORM
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It does not process full JSON data structures but takes simple JSON document with basic elements
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Provide a model_name, an example of JSON data and a dict of type overrides
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Example:
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source_data = {'Name': '48 Rainbow Rd',
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'GroupAddressStyle': 'ThreeLevel',
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'LastModified': '2020-12-21T07:02:51.2400232Z',
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'ProjectStart': '2020-12-03T07:36:03.324856Z',
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'Comment': '',
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'CompletionStatus': 'Editing',
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'LastUsedPuid': '955',
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'Guid': '0c85957b-c2ae-4985-9752-b300ab385b36'}
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source_overrides = {'Guid':{'type':uuid.UUID},
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'LastModified':{'type':datetime },
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'ProjectStart':{'type':datetime },
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}
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source_optionals = {"Comment":True}
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#create Model
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model_Project=pydanticModelGenerator(
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model_name="Project",
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source_data=source_data,
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overrides=source_overrides,
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optionals=source_optionals).generate_model()
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#create instance using DynamicModel
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project_instance=model_Project(**project_info)
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"""
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def __init__(
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self,
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model_name: str = None,
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source_data: str = None,
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params: Dict = {},
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overrides: Dict = {},
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optionals: Dict = {},
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):
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def field_type_generator(k, v, overrides, optionals):
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print(k, v)
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field_type = str if not overrides.get(k) else overrides[k]["type"]
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if v is None:
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field_type = Any
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else:
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field_type = type(v)
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return Optional[field_type]
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self._model_name = model_name
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self._json_data = source_data
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self._model_def = [
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ModelDef(
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field=underscore(k),
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field_alias=k,
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field_type=field_type_generator(k, v, overrides, optionals),
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field_value=v
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)
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for (k,v) in source_data.items() if k in params
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]
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def generate_model(self):
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"""
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Creates a pydantic BaseModel
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from the json and overrides provided at initialization
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"""
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fields = {
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d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
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}
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DynamicModel = create_model(self._model_name, **fields)
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DynamicModel.__config__.allow_population_by_field_name = True
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return DynamicModel
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StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing",
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StableDiffusionProcessing().__dict__,
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inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model() |