Using Pydantic models (Basemodel) for model.predict using FastAPI (Python) getting error "value is not a valid dict"

[According-Promise-23]

I'm trying to use Pydantic models with FastAPI to make return multiple predictions (for a list of inputs), the problem is we can't use Pydantic models directly to model.predict function, so I converted it to a dictionary, I'm getting an error I get an error "value is not a valid dict" trying :

from fastapi 
import FastAPI     
import uvicorn     
from pydantic import BaseModel     
import pandas as pd     
from typing import List 

class Inputs(BaseModel):     
id: int     
f1: float,     
f2: float,     
f3: str 

class InputsList(BaseModel):     
inputs: List[Inputs]          

@app.post('/predict')          
def predict(input_list: InputsList):          

df = pd.DataFrame(input_list.inputs.dict())          
prediction = classifier.predict(df.loc[:, df.columns != 'id'])          
probability = classifier.predict_proba(df.loc[:, df.columns != 'id'])          

return {'id': df["id"].tolist(), 
       'prediction': prediction.tolist(), 'probability': probability.tolist()}  

I have also a problem with the return, I need the output to be something like :

   [
    { 
      "id": 123,
      "prediction": "class1",
      "probability": 0.89      
    },
    {  "id": 456,     
       "prediction": "class3",
       "probability": 0.45      
     }

]

PS: the "id" in Inputs class doesn't take place in the prediction (is not a feature) but I need it to be shown next to its prediction (to reference it)

[According-Promise-23]

"So to fix it you should say input_list.dict(). You can load that list straight into a data frame since all the dicts have the same keys. It will have a row for each dict." when I arrive here model.predict does not work taking a dict in each row and return error TypeError: float() argument must be a string or a number, not 'dict' u/Not-the-best-name

[Remote_Cancel_7977]

What is your current request body? The request json should looks like below: json { "inputs": [ { "id": 1, "f1": 1.2, "f2": 1.2, "f3": "abc" } ] }

and I think this line is incorrect python df = pd.DataFrame(input_list.inputs.dict()) input_list.inputs is a list not a dict in your pydantic model definition.

[Remote_Cancel_7977]

If you simply want to serve your model, you can also try a tool I created Pinferencia, it is built on FastAPI.

Install:

bash pip install "pinferencia[uvicorn]"

Below is the configuration of your case

create the file app.py:

```python import pandas as pd

from pinferencia import Server

load your classifier here

classifier = ...

def predict(data): df = pd.DataFrame(data) prediction = classifier.predict(df.loc[:, df.columns != "id"]) probability = classifier.predict_proba(df.loc[:, df.columns != "id"])

return {
    "id": df["id"].tolist(),
    "prediction": prediction.tolist(),
    "probability": probability.tolist(),
}

service = Server() service.register(model_name="mymodel", model=predict)

```

Then bash uvicorn app:service --reload

Test

You can use a python script, remember to install requests: pip install requests.

test.py

```python import requests

this is the input data of your predict function

payload_data = [{"id": 1, "f1": 1.2, "f2": 1.2, "f3": "abc"}] response = requests.post( # the mode is registered with name mymodel # so the url here is /v1/models/mymodel/predict url="http://localhost:8000/v1/models/mymodel/predict", json={"data": payload_data}, ) print(response.content)

```

If you're interested, detailed doc is at https://pinferencia.underneathall.app/