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 Flask app with a name
superkart_api = Flask("SuperKart")

# Load the trained churn prediction model
model = joblib.load("superkart_model.joblib")

# Define a route for the home page
@superkart_api.get('/')
def home():
    return "Welcome to the SuperKart System"

# Define an endpoint to predict churn for a single customer
@superkart_api.post('/v1/predict')
def predict_sales():
    # Get JSON data from the request
    data = request.get_json()

    # Extract relevant customer features from the input data
    sample = {
    'Product_Weight': data['Product_Weight'],
    'Product_Sugar_Content': data['Product_Sugar_Content'],
    'Product_Allocated_Area': data['Product_Allocated_Area'],
    'Product_MRP': data['Product_MRP'],
    'Store_Size': data['Store_Size'],
    'Store_Location_City_Type': data['Store_Location_City_Type'],
    'Store_Type': data['Store_Type'],
    'Product_Id_char': data['Product_Id_char'],
    'Store_Age_Years': data['Store_Age_Years'],
    'Product_Type_Category': data['Product_Type_Category']
}

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

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

    # Return the prediction as a JSON response
    return jsonify({'Sales': prediction})


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