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10e06ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | # 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_revenue_predictor_api = Flask("Predict Product Store Sales based on product and store attributes")
# Load the trained machine learning model
model = joblib.load("superkart_revenue_prediction_model_v1_0.joblib")
# Define a route for the home page (GET request)
@superkart_revenue_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 Prediction API!"
# Define an endpoint for single property prediction (POST request)
@superkart_revenue_predictor_api.post('/v1/sales')
def predict_sales_price():
"""
This function handles POST requests to the '/v1/sales' endpoint.
It expects a JSON payload containing property details and returns
the predicted sales 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
json_extract = {
'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_Size': product_data['Store_Size'],
'Store_Location_City_Type': product_data['Store_Location_City_Type'],
'Store_Type': product_data['Store_Type'],
'Product_Category': product_data['Product_Category'],
'Perishable': product_data['Perishable'],
'Store_Age': product_data['Store_Age']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([json_extract])
# Change MRP to Log as this is done before pipeline (feature engineering)
input_data['MRP_log'] = np.log(input_data['Product_MRP'])
input_data['Price_Per_Display'] = input_data['Product_MRP'] * input_data['Product_Allocated_Area']
# Make prediction
predicted_price = model.predict(input_data)[0]
# Return the actual price
return jsonify({'Predicted Sales': predicted_price})
# Define an endpoint for batch prediction (POST request)
@superkart_revenue_predictor_api.post('/v1/salesbatch')
def predict_salesprice_batch():
"""
This function handles POST requests to the '/v1/salesbatch' endpoint.
It expects a CSV file containing property details for multiple properties
and returns the predicted sales 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)
# Change MRP to Log as this is done before pipeline (feature engineering)
input_data['MRP_log'] = np.log(input_data['Product_MRP'])
input_data['Price_Per_Display'] = input_data['Product_MRP'] * input_data['Product_Allocated_Area']
# Save ID
product_ids = input_data['Product_Id']
# Drop ID
input_data = input_data.drop('Product_Id', axis=1)
# Make predictions for all properties in the DataFrame (get log_prices)
predicted_prices = model.predict(input_data).tolist()
# Create a dictionary of predictions with property IDs as keys
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_revenue_predictor_api.run(debug=True)
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