CODEWITHSUNDEEP
jsonpython-3.xpandasdataframefastapi
DataPlug
DataPlug

Reputation: 348

Converting JSON into a DataFrame within FastAPI app

I am trying to create an API for customer churn at a bank. I have completed the model and now want to create the API using FastAPI. My problem is converting the JSON passed data to a dataframe to be able to run it through the model. Here is the code.

from fastapi import FastAPI
from starlette.middleware.cors import CORSMiddleware
from pycaret.classification import *
import pandas as pd
import uvicorn  # ASGI
import pickle
import pydantic
from pydantic import BaseModel

class customer_input(BaseModel):
    CLIENTNUM:int
    Customer_Age:int
    Gender:str
    Dependent_count:int
    Education_Level:str
    Marital_Status:str
    Income_Category:str
    Card_Category:str
    Months_on_book:int
    Total_Relationship_Count:int
    Months_Inactive_12_mon:int
    Contacts_Count_12_mon:int
    Credit_Limit:float
    Total_Revolving_Bal:int
    Avg_Open_To_Buy:float
    Total_Amt_Chng_Q4_Q1:float
    Total_Trans_Amt:int
    Total_Trans_Ct:int
    Total_Ct_Chng_Q4_Q1:float
    Avg_Utilization_Ratio:float


app = FastAPI()

#Loading the saved model from pycaret
model = load_model('BankChurnersCatboostModel25thDec2020')

origins = [
    '*'
]

app.add_middleware(
        CORSMiddleware,
        allow_origins=origins,
        allow_credentials=True,
        allow_methods=['GET','POST'],
        allow_headers=['Content-Type','application/xml','application/json'],
    )

@app.get("/")
def index():
    return {"Nothing to see here"}

@app.post("/predict")
def predict(data: customer_input):
    
    # Convert input data into a dictionary
    data = data.dict()

    # Convert the dictionary into a dataframe
    my_data = pd.DataFrame([data])
 
    # Predicting using pycaret
    prediction = predict_model(model, my_data)
    
    return prediction



# Only use below 2 lines when testing on localhost -- remove when deploying
if __name__ == '__main__':
    uvicorn.run(app, host='127.0.0.1', port=8000)

When I test this out I get the Internal Server Error from the OpenAPI interface so I check my cmd and the error says

ValueError: [TypeError("'numpy.int64' object is not iterable"), TypeError('vars() argument must have __dict__ attribute')]

How can I have the data that is passed into the predict function successfully convert into a dataframe. Thank you.

Upvotes: 2

Views: 3190

Answers (1)

DataPlug
DataPlug

Reputation: 348

Ok so I fixed this by changing the customer_input class. Any int types I changed to a float and that fixed it. I don't understand why though. Can anyone explain?

Fundamentally those int values are only meant to be an integer because they are all discrete values (i.e choosing number of dependents in a bank) but I guess I could put a constrain on the front-end.

Upvotes: 2

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