422: "value is not a valid dict" on Form() BaseModel validation - @app.post uses Path(), File() and Form() - Request called from SwaggerUI

First Check

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• I already read and followed all the tutorial in the docs and didn’t find an answer.
• I already checked if it is not related to FastAPI but to Pydantic.
• I already checked if it is not related to FastAPI but to Swagger UI.
• I already checked if it is not related to FastAPI but to ReDoc.

Commit to Help

• I commit to help with one of those options 👆

Example Code

from enum import Enum

from fastapi import (
    FastAPI,
    File,
    UploadFile,
    Form,
)
from pydantic import BaseModel, Field

app = FastAPI()


# Models
class ModelProcessor(str, Enum):
    CPU = "cpu"
    GPU = "gpu"
    TPU = "tpu"
    DEFAULT = CPU


class ModelThresholds(BaseModel):
    confidence: float = Field(0.5, ge=0.0, le=1.0, descritpiton="Confidence Threshold")
    nms: float = Field(
        0.4, ge=0.0, le=1.0, description="Non-Maximum Suppression Threshold"
    )


class ModelOptions(BaseModel):
    processor: ModelProcessor = Field(None, description="Processor to use for model")
    height: int = Field(
        416, ge=1, description="Height of the input image (resized for model)"
    )
    width: int = Field(
        416, ge=1, description="Width of the input image (resized for model)"
    )
    square: bool = Field(False, description="Zero pad the image to be a square")
    fp_16: bool = Field(False, description="model uses Floating Point 16 Backend (EXPERIMENTAL!)")

    thresholds: ModelThresholds = Field(
        default_factory=ModelThresholds, description="Thresholds for the model"
    )


@app.post("/detect/{model_uuid}", summary="Run detection on an image")
async def object_detection(
        model_uuid: str,
        model_options: ModelOptions = Form(),
        image: UploadFile = File(..., description="Image to run the ML model on"),
):
    print(f"Running detection on image '{image.filename}'")
    return {"filename": image.filename}


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=5001)

Description

What I am doing

• Open browser and head to http://localhost:5001/docs to open the SwaggerUI
• Try out the only defined endpoint, you can enter any string into model_uuid field and any image for the image: UploadFile, the model_options is prefilled with proper JSON based upon the BaseModel.
• Hit execute, a ‘422: Unprocessable Entity’ error is returned.

What is happening

Error returned is 422: Unprocessable Entity ->

{
  "detail": [
    {
      "loc": [
        "body",
        "model_options"
      ],
      "msg": "value is not a valid dict",
      "type": "type_error.dict"
    }
  ]
}

What I expect to happen

I expect that the return statement is returned with a 200 response code.

Operating System

Linux

Operating System Details

No response

FastAPI Version

0.79.0

Python Version

3.10.6

Additional Context

[iudeen]

The only limitation here is you will not be able to have nested JSONs within form.

from enum import Enum
from typing import Type
import inspect

from fastapi import (
    FastAPI,
    File,
    UploadFile,
    Form, Depends,
)
from pydantic import BaseModel, Field

app = FastAPI()

def as_form(cls: Type[BaseModel]):
    new_parameters = []

    for field_name, model_field in cls.__fields__.items():
        model_field: ModelField  # type: ignore

        new_parameters.append(
             inspect.Parameter(
                 model_field.alias,
                 inspect.Parameter.POSITIONAL_ONLY,
                 default=Form(...) if not model_field.required else Form(model_field.default),
                 annotation=model_field.outer_type_,
             )
         )

    async def as_form_func(**data):
        return cls(**data)

    sig = inspect.signature(as_form_func)
    sig = sig.replace(parameters=new_parameters)
    as_form_func.__signature__ = sig  # type: ignore
    setattr(cls, 'as_form', as_form_func)
    return cls

# Models
class ModelProcessor(str, Enum):
    CPU = "cpu"
    GPU = "gpu"
    TPU = "tpu"
    DEFAULT = CPU


class ModelThresholds(BaseModel):
    confidence: float = Field(0.5, ge=0.0, le=1.0, descritpiton="Confidence Threshold")
    nms: float = Field(
        0.4, ge=0.0, le=1.0, description="Non-Maximum Suppression Threshold"
    )

class ModelOptions(BaseModel):
    processor: ModelProcessor = Form(None, description="Processor to use for model")
    height: int = Form(
        416, ge=1, description="Height of the input image (resized for model)"
    )
    width: int = Form(
        416, ge=1, description="Width of the input image (resized for model)"
    )
    square: bool = Form(False, description="Zero pad the image to be a square")
    fp_16: bool = Form(False, description="model uses Floating Point 16 Backend (EXPERIMENTAL!)")

    # thresholds: ModelThresholds = Form(...)



@app.post("/detect/{model_uuid}", summary="Run detection on an image")
async def object_detection(
        model_uuid: str,
        model_options: ModelOptions = Depends(),
        image: UploadFile = File(..., description="Image to run the ML model on"),
):
    print(f"Running detection on image '{image.filename}'")
    return {"filename": image.filename}


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=5001)

In the above code, I just commented out thresholds and changed how you have formed the BaseModel

[iudeen]

@baudneo one last attempt to get as close as you want this to be. This handles everything.

import json
from enum import Enum
from typing import Type, Dict, Optional
import inspect

from fastapi import (
    FastAPI,
    File,
    UploadFile,
    Form, Depends,
)

from dataclasses import dataclass, field

from pydantic import BaseModel

app = FastAPI()


# Models
class ModelProcessor(str, Enum):
    CPU = "cpu"
    GPU = "gpu"
    TPU = "tpu"
    DEFAULT = CPU

class ModelThresholds(BaseModel):
    confidence: float = Form(0.5, ge=0.0, le=1.0, descritpiton="Confidence Threshold")
    nms: float = Form(
        0.4, ge=0.0, le=1.0, description="Non-Maximum Suppression Threshold"
    )

@dataclass
class ModelOptions:
    processor: ModelProcessor = Form(None, description="Processor to use for model")
    height: int = Form(
        416, ge=1, description="Height of the input image (resized for model)"
    )
    width: int = Form(
        416, ge=1, description="Width of the input image (resized for model)"
    )
    square: bool = Form(False, description="Zero pad the image to be a square")
    fp_16: bool = Form(False, description="model uses Floating Point 16 Backend (EXPERIMENTAL!)")

    thresholds: Optional[str] = Form(ModelThresholds().json())


@app.post("/detect/{model_uuid}", summary="Run detection on an image")
async def object_detection(
        model_uuid: str,
        model_options: ModelOptions = Depends(),
        image: UploadFile = File(..., description="Image to run the ML model on"),
):
    threshold = ModelThresholds(**json.loads(model_options.thresholds))
    print(threshold.dict())
    print(f"Running detection on image '{image.filename}'")
    return {"filename": image.filename}


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=5001)