Update processing.py
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@ -16,46 +16,15 @@ class ModelDef(BaseModel):
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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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Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
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source_data is a snapshot of the default values produced by the class
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params are the names of the actual keys required by __init__
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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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source_data: {} = {},
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params: Dict = {},
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overrides: Dict = {},
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optionals: Dict = {},
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@ -96,4 +65,4 @@ class pydanticModelGenerator:
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StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing",
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StableDiffusionProcessing().__dict__,
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inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
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inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
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