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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
sales_total_predictor_api = Flask("SuperKart Sales Total Predictor")
# Load the trained machine learning model
model = joblib.load("product_stores_sales_total_prediction_model_v1_0.joblib")
# Define a route for the home page (GET request)
@sales_total_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 Sales Total Prediction API!"
# Define an endpoint for single property prediction (POST request)
@sales_total_predictor_api.post('/v1/sales')
def predict_sales_total():
"""
This function handles POST requests to the '/v1/sales' endpoint.
It expects a JSON payload containing product and store details and returns
the predicted product_store_sales_total as a JSON response.
"""
# Get the JSON data from the request body
product_store_data = request.get_json()
# Extract relevant features from the JSON data
sample = {
'Product_Weight': product_store_data['Product_Weight'],
'Product_Sugar_Content': product_store_data['Product_Sugar_Content'],
'Product_Allocated_Area': product_store_data['Product_Allocated_Area'],
'Product_Type': product_store_data['Product_Type'],
'Product_MRP': product_store_data['Product_MRP'],
'Store_Establishment_Year': product_store_data['Store_Establishment_Year'],
'Store_Size': product_store_data['Store_Size'],
'Store_Location_City_Type': product_store_data['Store_Location_City_Type'],
'Store_Type': product_store_data['Store_Type']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
# Make prediction
predicted_price = model.predict(input_data)[0]
# Convert predicted_price to Python float
# predicted_price = round(float(predicted_price), 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 Price': float(predicted_price)})
# Define an endpoint for batch prediction (POST request)
@sales_total_predictor_api.post('/v1/salesbatch')
def predict_sales_total_batch():
"""
This function handles POST requests to the '/v1/salesbatch' endpoint.
It expects a CSV file containing product and store details for multiple properties
and returns the predicted productstore sales total 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 product and store in the DataFrame
predicted_prices = model.predict(input_data).tolist()
# Create a dictionary of predictions with property IDs as keys
product_store_ids = input_data[['Product_Id', 'Store_Id']].values.tolist()
keys = [f"{pid}_{sid}" for pid, sid in product_store_ids]
output_dict = dict(zip(keys, predicted_prices)) # Use actual 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__':
sales_total_predictor_api.run(debug=True)
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