Build API using FastAPI for Classification Model produced using pycaret

Question

I'm using pycaret as my ML workflow, I tried to create an API using FastAPI. This is my first time playing into production level, so I'm bit confused about API

I have 10 features; age: float, live_province: str, live_city: str, live_area_big: str, live_area_small: str, sex: float, marital: float, bank: str, salary: float, amount: float and a label which it contains the binary value (0 and 1).

This is what my script for building the API

from pydantic import BaseModel
import numpy as np
from pycaret.classification import *

import uvicorn
from fastapi import FastAPI

app = FastAPI()

model = load_model('catboost_cm_creditable')

class Data(BaseModel):
    age: float
    live_province: str
    live_city: str
    live_area_big: str
    live_area_small: str
    sex: float
    marital: float
    bank: str
    salary: float
    amount: float

input_dict = Data

@app.post("/predict")
def predict(model, input_dict):
    predictions_df = predict_model(estimator=model, data=input_dict)
    predictions = predictions_df['Score'][0]
    return predictions

When I tried to run uvicorn script:app and went to the documentation I can't find the parameter for my features, the parameters only show model and input_dict

How to take my Features onto Parameters in the API?

Answer 1

You need to Type-hint your Pydantic model to make it work with your FastAPI

Imagine like you are really working with Standard Python, when you need to documentate that function,

def some_function(price: int) ->int:
    return price

With Pydantic there is nothing different than the example above

Your class Data is actually a python @dataclass with super-powers(comes from Pydantic)

from fastapi import Depends

class Data(BaseModel):
    age: float
    live_province: str
    live_city: str
    live_area_big: str
    live_area_small: str
    sex: float
    marital: float
    bank: str
    salary: float
    amount: float


@app.post("/predict")
def predict(data: Data = Depends()):
    predictions_df = predict_model(estimator=model, data=data)
    predictions = predictions_df["Score"][0]
    return predictions

There is a one little trick, with Depends, you 'll get a single queries like when you are defining each field seperately.

With Depends

Without Depends