pydantic instrumentation

modules/api/processing.py

@@ -0,0 +1,99 @@
+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 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.
+    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
+
+    It does not process full JSON data structures but takes simple JSON document with basic elements
+
+    Provide a model_name, an example of JSON data and a dict of type overrides
+
+    Example:
+
+    source_data = {'Name': '48 Rainbow Rd',
+        'GroupAddressStyle': 'ThreeLevel',
+        'LastModified': '2020-12-21T07:02:51.2400232Z',
+        'ProjectStart': '2020-12-03T07:36:03.324856Z',
+        'Comment': '',
+        'CompletionStatus': 'Editing',
+        'LastUsedPuid': '955',
+        'Guid': '0c85957b-c2ae-4985-9752-b300ab385b36'}
+
+    source_overrides = {'Guid':{'type':uuid.UUID},
+            'LastModified':{'type':datetime },
+            'ProjectStart':{'type':datetime },
+            }
+    source_optionals = {"Comment":True}
+
+    #create Model
+    model_Project=pydanticModelGenerator(
+        model_name="Project",
+        source_data=source_data,
+        overrides=source_overrides,
+        optionals=source_optionals).generate_model()
+
+    #create instance using DynamicModel
+    project_instance=model_Project(**project_info)
+
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        source_data: str = None,
+        params: Dict = {},
+        overrides: Dict = {},
+        optionals: Dict = {},
+    ):
+        def field_type_generator(k, v, overrides, optionals):
+            print(k, v)
+            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()