app.py

# Import necessary libraries
import numpy as np
import joblib  # For loading the serialized model
import pandas as pd  # For data manipulation
from flask import Flask, request, jsonify  # For creating the Flask API

# Initialize the Flask application
superMartSales_api = Flask("SuperMart_Sales_Revenue")

# Load the trained model
model = joblib.load("supermart_sales_prediction_model_v1_0.joblib")


# Home route
@superMartSales_api.get('/')
def home():
    return "Welcome to SuperKart sales Revenue Prediction"


import traceback

@superMartSales_api.post('/v1/superKartSales')
def sale_pred_single():
    try:
        sale_data = request.get_json()

        sample = {
            'Product_Weight': sale_data.get('Product_Weight'),
            'Product_Sugar_Content': sale_data.get('Product_Sugar_Content'),
            'Product_Allocated_Area': sale_data.get('Product_Allocated_Area'),
            'Product_Type': sale_data.get('Product_Type'),
            'Product_MRP': sale_data.get('Product_MRP'),
            'Store_Id': sale_data.get('Store_Id'),
            'Store_Establishment_Year': sale_data.get('Store_Establishment_Year'),
            'Store_Size': sale_data.get('Store_Size'),
            'Store_Location_City_Type': sale_data.get('Store_Location_City_Type'),
            'Store_Type': sale_data.get('Store_Type'),
        }

        input_data = pd.DataFrame([sample])

        predicted_sale = model.predict(input_data)[0]

        response = {
            'Store_Outlet': sample['Store_Id'],
            "Sale": round(float(predicted_sale), 2)
        }
        return jsonify(response)

    except Exception as e:
        print("❌ Error in /v1/superKartSales:", e)
        traceback.print_exc()   # this will show in Hugging Face Space logs
        return jsonify({"error": str(e)}), 500

# Batch prediction route
@superMartSales_api.post('/v1/superKartbatch')
def sale_pred_batch():
    file = request.files['file']
    print("File Received:", file.filename)

    # Read input data
    input_data = pd.read_csv(file)

    # Make predictions
    predicted_sale = model.predict(input_data).tolist()

    # Add predictions to input data
    input_data['Predicted_Sale'] = predicted_sale

    # Group by Store_Id and sum the predicted sales
    grouped_sales = input_data.groupby('Store_Id')['Predicted_Sale'].sum().to_dict()

    # Create response
    response = {
        'store_sales': {
            store_id: round(float(sale), 2)
            for store_id, sale in grouped_sales.items()
        }
    }
    print("Final Response:", response)

    return jsonify(response)


# Run the application
if __name__ == '__main__':
    superMartSales_api.run()