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Dockerfile

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+FROM python:3.13
+
+COPY . .
+
+WORKDIR /
+
+RUN pip install --no-cache-dir --upgrade -r /requirements.txt
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8080"]

app.py

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+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.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]
+    return {"predicted_time": float(round(pred, 8))}
+

requirements.txt

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+fastapi
+uvicorn
+xgboost
+pandas
+scikit-learn