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# 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_model_api = Flask("SuperKart’s Decision-Making System")
# Load the trained machine learning model
model = joblib.load("superkart_decision_making_model_v1_0.joblib")
# Define a route for the home page (GET request)
@superkart_model_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’s Decision-Making System API!"
# Define an endpoint for single product sale prediction (POST request)
@superkart_model_api.post('/v1/productsale')
def predict_product_sales():
"""
This function handles POST requests to the '/v1/productsale' endpoint.
It expects a JSON payload containing product and store details and returns
total revenue by the sale of that particular product in that particular store 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_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 Product_Store_Sales_Total)
predicted_Product_Store_Sales_Total = model.predict(input_data)[0]
print(f"Predicted Product_Store_Sales_Total: {predicted_Product_Store_Sales_Total}")
# Convert predicted_price to Python float
predicted_price = round(float(predicted_Product_Store_Sales_Total), 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({'Total Revenue (in dollars)': predicted_price})
# Define an endpoint for batch prediction (POST request)
@superkart_model_api.post('/v1/productsalebatch')
def predict_product_sale_price_batch():
"""
This function handles POST requests to the '/v1/productsalebatch' endpoint.
It expects a CSV file containing product and store details and returns the predicted
total revenue 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 product sale in the stores in the DataFrame (get log_prices)
predicted_Product_Store_Sales_Total = model.predict(input_data).tolist()
# Calculate actual prices
predicted_prices = [round(float(total_sale_price), 2) for total_sale_price in predicted_Product_Store_Sales_Total]
# Create a dictionary of predictions with product IDs as keys
product_ids = input_data['Product_Id'].tolist() # Assuming 'id' is the product ID column
# Create a dictionary of predictions with Store IDs as keys
store_ids = input_data['Store_Id'].tolist()
# Build predictions with both Product and Store IDs
output_list = []
for pid, sid, price in zip(product_ids, store_ids, predicted_prices):
output_list.append({
"Product_Id": pid,
"Store_Id": sid,
"Predicted_Revenue": round(float(price), 2)
})
# Return as JSON response
return jsonify({"predictions": output_list})
# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
superkart_model_api.run(debug=True)
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