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
rental_price_predictor_api = Flask("Super Kart Sales Predictor")

# Load the trained machine learning model
model = joblib.load("super_kart_prediction_model_v1_0.joblib")

# Define a route for the home page (GET request)
@rental_price_predictor_api.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 Super Kart Sales Predictor API!"

# Define an endpoint for single property prediction (POST request)
@rental_price_predictor_api.post('/v1/superkart')
def predict_rental_price():
    """
    This function handles POST requests to the '/v1/superkart' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted rental price as a JSON response.
    """
    # Get the JSON data from the request body
    product_data = request.get_json()

    # Extract relevant features from the JSON data
    sample = {
      '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 Pandas DataFrame
    input_data = pd.DataFrame([sample])

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

    # Clip log prediction to avoid overflow in exp
    # predicted_log_price_clipped = np.clip(predicted_price, a_min=None, a_max=700)

    # # Calculate actual price safely
    # predicted_price = np.exp(predicted_log_price_clipped)

    # # Convert predicted_price to Python float and round
    # predicted_price = round(float(predicted_price), 2)

    # Return the actual price
    return jsonify({'Predicted Sales Total': predicted_price})