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ViewsBuild API using FastAPI for Classification Model produced using pycaret
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?
2 answers
Answer question0
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
0
Your problem is with the definition of the API's function. You added an argument for you data input but you didn't tell FastAPI it's type. Also I assume that you mean't to use the model that you've loaded globally instead of received it as a parameter. Also you don't need to create a global instance for your input data, as you want to get it from the user.
Therefore, simply change the signature of your function to:
def predict(input_dict: Data):
and remove the line:
input_dict = Data
(Which just creates an Alias to your class Data, named input_dict)
You'll end up with:
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
@app.post("/predict")
def predict(input_dict: Data):
predictions_df = predict_model(estimator=model, data=input_dict)
predictions = predictions_df['Score'][0]
return predictions
Also, I would recommend changing the name of the class Data to something more clear and easier to understand, even DataUnit would be better in my opinion as Data is too general.