app.py

# Import necessary libraries
from flask import Flask, request, jsonify
import joblib
import pandas as pd
import numpy as np
# Initialize the Flask application
api = Flask("Superkart Sales Predictor")
# Load the trained machine learning model
model = joblib.load('superkart_prediction_model_v1_0.joblib')
# Define a route for the home page (GET request)
# Health check (important for deployment)

@api.route("/", methods=["GET"])
def health():
    return "SuperKart Backend is running"


@api.route("/predict", methods=["POST"])
def predict():
    try:
        data = request.get_json(force=True)

        sample = {
            "Product_Weight": data["Product_Weight"][0],
            "Product_Sugar_Content": data["Product_Sugar_Content"][0],
            "Product_Allocated_Area": data["Product_Allocated_Area"][0],
            "Product_Type": data["Product_Type"][0],
            "Product_MRP": data["Product_MRP"][0],
            "Store_Establishment_Year": data["Store_Establishment_Year"][0],
            "Store_Size": data["Store_Size"][0],
            "Store_Location_City_Type": data["Store_Location_City_Type"][0],
            "Store_Type": data["Store_Type"][0]
        }

        query_df = pd.DataFrame([sample])
        prediction = model.predict(query_df).tolist()

        return jsonify({"predictions": prediction})

    except Exception as e:
        return jsonify({"error": str(e)}), 500


if __name__ == "__main__":
    app.api(debug=True)