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from fastapi import FastAPI
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field, computed_field
import numpy as np
from typing import Literal, Annotated
import pickle
import math
import pandas as pd
# import the ML model
with open('delivery_time_model.pkl','rb') as f:
model = pickle.load(f)
app = FastAPI()
@app.get('/')
def home():
return {'message' : 'Delivery time estimation API '}
@app.get('/health')
def healthcheck():
return {'status' : 'OK'}
# pydantic model build to validate the input data
class UserInput(BaseModel):
age : Annotated[int,Field(...,ge = 18, lt = 120,description = 'Age of the delivery person')]
rating : Annotated[float,Field(...,ge = 1, le = 6 ,description = 'Delivery person Ratings')]
distance : Annotated[int,Field(...,gt = 0,description = 'Total Distance to be covered')]
@app.post('/predict')
def predict_time(data: UserInput):
features = np.array([[data.age, data.rating, data.distance]])
prediction = model.predict(features)
prediction_value = math.ceil(float(prediction[0]))
return JSONResponse(
status_code=200,
content={"Predicted Delivery Time in Minutes": prediction_value}
)
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