app.py

# Import necessary libraries
import numpy as np
import joblib  
import pandas as pd
from flask import Flask, request, jsonify

# Initialize Flask app with a name
app = Flask("Super Kart Product Pricing Predictor")
model = joblib.load("super_kart_product_pricing_model.joblib")

# Define a route for the home page, used to validate backend is functional and accessible
@app.get('/')
def home():
    return "Welcome to the Super Kart Product Pricing Predictor API!"

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

    # Extract relevant customer features from the input data. The order of the column names matters.
    sample = {
        'Product_Weight': data['Product_Weight'],
        'Product_Sugar_Content': data['Product_Sugar_Content'],
        'Product_Allocated_Area': data['Product_Allocated_Area'],
        'Product_Type_Category': data['Product_Type_Category'],
        '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'].astype(str),
        'Store_Age_Years': data['Store_Age_Years'],
    }

    # Convert the extracted data into a DataFrame and preprocess it
    input_data = pd.DataFrame([sample])
    prediction = model.predict(input_data).tolist()[0]

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

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