main.py

import joblib
import pandas as pd
from fastapi import FastAPI
from fastapi.encoders import jsonable_encoder
from loguru import logger
from pydantic import BaseModel

# Initialize instance
app = FastAPI()


# Class to define the request body
class Diabetes_measures(BaseModel):
    Pregnancies: int = 6  # Number of times pregnant
    Glucose: int = (
        148  # Plasma glucose concentration a 2 hours in an oral glucose tolerance test
    )
    BloodPressure: int = 72  # Diastolic blood pressure (mm Hg)
    SkinThickness: int = 35  # Triceps skin fold thickness (mm)
    Insulin: int = 0  # 2-Hour serum insulin
    BMI: float = 33.6  # Body mass index
    DiabetesPedigreeFunction: float = 0.627  # Diabetes pedigree function
    Age: int = 50  # Age (years)


# Load model
model = joblib.load("./models/model.pkl")


# Create an endpoint to check api work or not
@app.get("/")
def check_health():
    return {"status": "Oke"}


# Initialize cache
cache = {}


# Create an endpoint to make prediction
@app.post("/predict")
def predict(data: Diabetes_measures):
    logger.info("Making predictions...")
    logger.info(data)
    logger.info(jsonable_encoder(data))
    logger.info(pd.DataFrame(jsonable_encoder(data), index=[0]))
    result = model.predict(pd.DataFrame(jsonable_encoder(data), index=[0]))[0]

    return {"result": ["Normal", "Diabetes"][result]}


@app.post("/predict_cache")
def predict_cache(data: Diabetes_measures):
    if str(data) in cache:
        logger.info("Getting result from cache!")
        return cache[str(data)]
    else:
        logger.info("Making predictions...")
        logger.info(data)
        logger.info(jsonable_encoder(data))
        logger.info(pd.DataFrame(jsonable_encoder(data), index=[0]))
        result = model.predict(pd.DataFrame(jsonable_encoder(data), index=[0]))[0]
        cache[str(data)] = ["Normal", "Diabetes"][result]

        return {"result": ["Normal", "Diabetes"][result]}