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
superkart_product_sales_predictor_api = Flask("SuperKart Product Sales Predictor")

# Load the trained machine learning model
model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")

# Define a route for the home page (GET request)
@superkart_product_sales_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 SuperKart Product Sales Prediction API!"

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

    # Extract relevant features from the JSON data
    sample = {
        'Product_Weight': property_data['productWeight'],
        'Product_Allocated_Area': property_data['productAllocatedArea'],
        'Product_MRP': property_data['productMRP'],
        'Store_Establishment_Year': property_data['storeEstablishmentYear'],
        'Product_Sugar_Content': property_data['productSugarContent'],
        'Product_Type': property_data['prouctType'],
        'Store_Size': property_data['storeSize'],
        'Store_Location_City_Type': property_data['storeLocationCityType'],
        'Store_Type': property_data['storeType']
    }

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

    # Make prediction (get sales)
    predicted_sales = model.predict(input_data)[0]

    predicted_price = round(float(predicted_sales), 2)


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


# Define an endpoint for batch prediction (POST request)
@superkart_product_sales_predictor_api.post('/v1/productsbatch')
def predict_product_sale_price_batch():
    """
    This function handles POST requests to the '/v1/productsbatch' endpoint.
    It expects a CSV file containing property details for multiple properties
    and returns the predicted product sale prices as a dictionary in the JSON response.
    """
    # Get the uploaded CSV file from the request
    file = request.files['file']

    # Read the CSV file into a Pandas DataFrame
    input_data = pd.read_csv(file)

    # Make predictions for all products in the DataFrame (get predicted_sales)
    predicted_sales = model.predict(input_data).tolist()

        # Calculate actual prices
    predicted_prices = [round(float(sale_price), 2) for sale_price in predicted_sales]

    # Create a dictionary of predictions with product IDs as keys
    product_ids = input_data['Product_Id'].tolist()
    output_dict = dict(zip(product_ids, predicted_prices))

    # Return the predictions dictionary as a JSON response
    return output_dict

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