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
Product_Store_Sales_Total = Flask("SuperKart (Store sales predictor for product)")

# Load the trained machine learning model
model = joblib.load("/content/drive/MyDrive/Python/Great Learning/Model Deployment/model.joblib")

# Define a route for the home page (GET request)
@Product_Store_Sales_Total.get('/')
def home():
    """
    This function handles GET requests to the root URL ('/') of the API.
    It returns a simple welcome message.
    """
    return "Welcome to the SuperKart Store sales Prediction API!"

# Define an endpoint for single property prediction (POST request)
@Product_Store_Sales_Total.post('/v1/predict')
def predict_product_sales():
    """
    This function handles POST requests to the '/v1/predict' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted product sales as a JSON response.
    """
    # Get the JSON data from the request body
    property_data = request.get_json()
# categorical_features = ['Product_Sugar_Content', 'Product_Type', 'Store_Size', 'Store_Location_City_Type', 'Store_Type']
# numerical_features = ['Product_Weight', 'Product_Allocated_Area', 'Product_MRP', 'Store_Age']
    # Extract relevant features from the JSON data
    sample = {
        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
        'Product_Type': property_data['Product_Type'],
        'Store_Size': property_data['Store_Size'],
        'Store_Location_City_Type': property_data['Store_Location_City_Type'],
        'Store_Type': property_data['Store_Type'],
        'Product_Weight': property_data['Product_Weight'],
        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
        'Product_MRP': property_data['Product_MRP'],
        'Store_Age': property_data['Store_Age']
    }

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

    # Make prediction (get log_price)
    predicted_product_sales = model.predict(input_data)[0]

    # Calculate actual price
    predicted_sales = np.exp(predicted_product_sales)

    # Convert predicted_price to Python float
    predicted_sales = round(float(predicted_sales), 2)
    # The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error

    # Return the actual price
    return jsonify({'Predicted Sales (in dollars)': predicted_sales})


# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
    Product_Store_Sales_Total.run(debug=True)