app.py

import numpy as np
import pandas as pd
import joblib
from flask import Flask, request, jsonify

# Initialize Flask app
superkart_app = Flask("superkart_sales_api")

# Load the trained model pipeline (preprocessing + model)
model = joblib.load("superkart.joblib")

# Health check route
@superkart_app.get('/')
def home():
    return "Welcome to the SuperKart Sales Prediction API"

# Prediction route
@superkart_app.post('/v1/predict')
def predict_sales():
    try:
        # Parse JSON payload
        data = request.get_json()
        print("Raw incoming data:", data)

        # Convert and transform input
        sample = {
            'Product_Weight': float(data['Product_Weight']),
            'Product_Sugar_Content': data['Product_Sugar_Content'],
            'Product_Allocated_Area': np.log1p(float(data['Product_Allocated_Area'])),  # transform here
            'Product_Type': data['Product_Type'],
            'Product_MRP': float(data['Product_MRP']),
            'Store_Size': data['Store_Size'],
            'Store_Location_City_Type': data['Store_Location_City_Type'],
            'Store_Type': data['Store_Type'],
            'Store_Current_Age': int(data['Store_Current_Age'])
        }

        input_df = pd.DataFrame([sample])
        print("Transformed input for model:\n", input_df)

        # Make prediction
        prediction = model.predict(input_df).tolist()[0]
        return jsonify({'Predicted_Sales': prediction})

    except Exception as e:
        print("Error during prediction:", str(e))
        return jsonify({'error': f"Prediction failed: {str(e)}"}), 500


# Run the Flask development server (for local testing/setup)
if __name__ == '__main__':
    # Ensure the backend_files directory exists (important if running this file directly)
    superkart_app.run(debug=True)