app.py

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

# Initialize Flask app with a name
SalesRevenue_predictor_api = Flask("Sales Revenue predictor")

# Load the trained revenue prediction model
model = joblib.load("SuperKart_turnOver_prediction_model_v1_0.joblib")

# Define a route for the home page
@SalesRevenue_predictor_api.get('/')
def home():
    return "Welcome to the Sales Revenue Prediction API!"

# Define an endpoint to predict revenue for a single customer
@SalesRevenue_predictor_api.route('/v1/Sales_prediction', methods=['POST'])
def predict_revenue():
    # Get JSON data from the request
    product_data = request.get_json()

    # Extract relevant customer features from the input data
    sample = {
        'Product_Id': product_data['Product_Id'],
        'Product_Weight': product_data['Product_Weight'],
        'Product_Sugar_Content': product_data['Product_Sugar_Content'],
        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
        'Product_Type': product_data['Product_Type'],
        'Product_MRP': product_data['Product_MRP'],
        'Store_Id': product_data['Store_Id'],
        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
        'Store_Size': product_data['Store_Size'],
        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
        'Store_Type' : product_data['Store_Type']
    }

    # Convert the extracted data into a DataFrame
    input_data = pd.DataFrame([sample])

    # Make a revenue prediction using the trained model
    #prediction = model.predict(input_data).tolist()[0]
    prediction = model.predict(input_data)[0]

    # Return the prediction as a JSON response
    return jsonify({ 'Prediction': prediction, 'Message': 'Prediction completed' })


# Run the Flask app in debug mode
if __name__ == '__main__':
    SalesRevenue_predictor_api.run(debug=True)