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Dockerfile

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+
+FROM python:3.12.10-slim-bookworm
+
+WORKDIR /app
+
+COPY app.py .
+COPY requirements.txt .
+COPY util.py ./backend/
+COPY SuperKart_clf_model_v1_0.joblib .
+COPY SuperKart_reg_model_v1_0.joblib .
+
+RUN python -m pip install --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:app"]

SuperKart_clf_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ec11f58ed68ae52a7fda7fdd111f719e720be9b7b777974cb727c477a63068fc
+size 479126

SuperKart_reg_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3da42fd9b3a16c5fde1f63acc730d8b961e8538de420cf3188b3472f362eacf3
+size 9613354

app.py

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+
+import os
+import pandas as pd
+from typing import Any
+from flask import Flask, request, jsonify
+from pydantic import BaseModel, Field, ValidationError, model_validator
+from backend.util import num_features_selector, cat_features_selector, store_age
+
+import joblib
+clf = joblib.load("SuperKart_clf_model_v1_0.joblib")
+reg = joblib.load("SuperKart_reg_model_v1_0.joblib")
+
+app = Flask("SuperKart: Revenue Forecasting")
+
+@app.get("/")
+def liveProbe():
+    return app.name
+
+class ReqSchema(BaseModel):
+    Store_Size: str = Field(..., description="Size of the store")
+    Store_Type: str = Field(..., description="Type of the store")
+    Product_Type: str = Field(..., description="Type of the product")
+    Product_Weight: float = Field(..., description="Weight of the product", gt=0)
+    Product_MRP: float = Field(..., description="Maximum Retail Price of the product")
+    Store_Establishment_Year: int = Field(..., description="Store Establishment year")
+    Product_Allocated_Area: float = Field(..., description="Allocated area for the product")
+    Store_Location_City_Type: str = Field(..., description="Location city type of the store")
+
+    Store_Age: int = Field(..., description="Age of the store", exclude=True)
+
+    @model_validator(mode="before")
+    def set_store_age(cls, values: dict[str, Any]) -> dict[str, Any]:
+        establishment_year = values.get('Store_Establishment_Year')
+        values['Store_Age'] = store_age(establishment_year)
+        return values   
+
+@app.post("/v1/predict")
+def predict():
+    auth = request.headers.get('Authorization')
+    if auth != os.environ['auth_key']:
+        return jsonify({"error": "Unauthorized"}), 401
+    elif clf is None or reg is None:
+        return jsonify({"error": "Prediction service is unavailable. Model(s) failed to load."}), 503
+
+    try:
+        payload = request.get_json()
+        reqData = ReqSchema(**payload)
+    except ValidationError as e:
+        return jsonify({"error": e.errors()}), 400
+    except Exception as e:
+        return jsonify({"error": f"Invalid request payload: {e}"}), 400
+
+    try:
+        data = pd.DataFrame([{
+            'Product_Weight': reqData.Product_Weight,
+            'Product_Allocated_Area': reqData.Product_Allocated_Area,
+            'Product_Type': reqData.Product_Type,
+            'Product_MRP': reqData.Product_MRP,
+            'Store_Size': reqData.Store_Size,
+            'Store_Type': reqData.Store_Type,
+            'Store_Location_City_Type': reqData.Store_Location_City_Type,
+            'Store_Age': reqData.Store_Age,
+        }])
+
+        pred_label = clf.predict(data).tolist()[0]
+        pred_value = reg.predict(data).tolist()[0]
+        print(pred_label, pred_value)
+        return jsonify({'performance': pred_label, 'revenue': round(pred_value, 2)})
+    except Exception as e:
+        return jsonify({"error": f"Prediction failed: {e}"}), 500
+
+@app.post("/v1/predict/bulk")
+def predictBulk():
+    auth = request.headers.get('Authorization')
+    if auth != os.environ['auth_key']:
+        return jsonify({"error": "Unauthorized"}), 401
+    elif clf is None or reg is None:
+        return jsonify({"error": "Prediction service is unavailable. Model(s) failed to load."}), 503
+    elif 'file' not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+
+    file = request.files['file']
+    try:
+        if file.filename.endswith(".csv"):
+            df = pd.read_csv(file)
+        elif file.filename.endswith((".xls", ".xlsx")):
+            df = pd.read_excel(file)
+        else:
+            return jsonify({"error": "Unsupported file format! Upload CSV or Excel"}), 400
+
+        reqCols = ['Product_Weight', 'Product_Allocated_Area', 'Product_Type', 'Product_MRP', 'Store_Size', 'Store_Location_City_Type', 'Store_Type', 'Store_Establishment_Year']
+        missingCols = [col for col in reqCols if col not in df.columns]
+        if missingCols:
+            return jsonify({"error": f"Missing columns: {missingCols}"}), 400
+
+        X = df[reqCols].copy()
+        X["Store_Age"] = X["Store_Establishment_Year"].apply(store_age)
+        df["Sales_Performance"] = clf.predict(X)
+        df["Sales_Revenue"] = reg.predict(X)
+
+        return jsonify(df.to_dict(orient="records"))
+    except Exception as e:
+        return jsonify({"error": f"Bulk prediction failed: {e}"}), 500
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+numpy==2.1.2
+pandas==2.3.1
+joblib==1.5.1
+flask==3.1.2
+Werkzeug==3.1.3
+gunicorn==23.0.0
+pydantic==2.11.7
+uvicorn[standard]
+scikit-learn==1.7.1

util.py

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+import numpy as np
+from datetime import datetime
+def num_features_selector(X):
+    return X.select_dtypes(include=np.number).columns.to_list()   
+def cat_features_selector(X):
+    return X.select_dtypes(include=['object', 'category']).columns.to_list()
+def store_age(establishmentYear):
+    return datetime.now().year - establishmentYear