app.py

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import xgboost as xgb
import pandas as pd

# Load model
model = xgb.XGBRegressor()
model.load_model("timePrediction.json")

class InputData(BaseModel):
    Quantity: int
    Product_Type: str

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

def encode_product(product_type: str):
    product_map = {
        "Lemon Scent Dishwashing Liquid": [1, 0, 0, 0, 0, 0, 0, 0],
        "Antibacterial Dishwashing Gel": [0, 1, 0, 0, 0, 0, 0, 0],
        "Unbleached Baking Paper": [0, 0, 1, 0, 0, 0, 0, 0],
        "Silicone-coated baking sheet": [0, 0, 0, 1, 0, 0, 0, 0],
        "Disposable plastic bag": [0, 0, 0, 0, 1, 0, 0, 0],
        "Lavender air freshener sachet": [0, 0, 0, 0, 0, 1, 0, 0],
        "Mothballs": [0, 0, 0, 0, 0, 0, 1, 0],
        "Air Fryer Paper": [0, 0, 0, 0, 0, 0, 0, 1],
    }
    return product_map.get(product_type, [0]*8)

@app.get("/")
def start():
    return "Hello World"

@app.post("/predict")
def predict(data: InputData):
    features = [data.Quantity] + encode_product(data.Product_Type)
    columns = ['Quantity',
               'Lemon Scent Dishwashing Liquid',
               'Antibacterial Dishwashing Gel',
               'Unbleached Baking Paper',
               'Silicone-coated baking sheet',
               'Disposable plastic bag',
               'Lavender air freshener sachet',
               'Mothballs',
               'Air Fryer Paper']

    X = pd.DataFrame([features], columns=columns)
    pred = model.predict(X)[0]
    final_pred = pred * 0.3
    return {"predicted_time": float(round(pred, 8))}