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from inflection import underscore
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field, create_model
from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
import inspect
class ModelDef(BaseModel):
"""Assistance Class for Pydantic Dynamic Model Generation"""
field: str
field_alias: str
field_type: Any
field_value: Any
class pydanticModelGenerator:
"""
Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
source_data is a snapshot of the default values produced by the class
params are the names of the actual keys required by __init__
"""
def __init__(
self,
model_name: str = None,
source_data: {} = {},
params: Dict = {},
overrides: Dict = {},
optionals: Dict = {},
):
def field_type_generator(k, v, overrides, optionals):
field_type = str if not overrides.get(k) else overrides[k]["type"]
if v is None:
field_type = Any
else:
field_type = type(v)
return Optional[field_type]
self._model_name = model_name
self._json_data = source_data
self._model_def = [
ModelDef(
field=underscore(k),
field_alias=k,
field_type=field_type_generator(k, v, overrides, optionals),
field_value=v
)
for (k,v) in source_data.items() if k in params
]
def generate_model(self):
"""
Creates a pydantic BaseModel
from the json and overrides provided at initialization
"""
fields = {
d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
}
DynamicModel = create_model(self._model_name, **fields)
DynamicModel.__config__.allow_population_by_field_name = True
return DynamicModel
StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing",
StableDiffusionProcessing().__dict__,
inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
|
