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4322b08 4145e24 4322b08 | 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 | # 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_api = Flask("SuperKart")
# Load the trained machine learning model
model = joblib.load("superkart_model_v1_0.joblib")
# Define a route for the home page (GET request)
@superkart_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 API!"
# Define an endpoint for single prediction (POST request)
@superkart_api.post('/v1/sales')
def predict_sales():
"""
This function handles POST requests to the '/v1/sales' endpoint.
It expects a JSON payload containing product details and returns
the predicted sales as a JSON response.
"""
# Get the JSON data from the request body
sales = request.get_json()
# Extract relevant features from the JSON data
sample = {
'Product_Weight': sales['Product_Weight'],
'Product_Allocated_Area': sales['Product_Allocated_Area'],
'Product_MRP': sales['Product_MRP'],
'Store_Establishment_Year': sales['Store_Establishment_Year'],
'Product_Sugar_Content': sales['Product_Sugar_Content'],
'Product_Type': sales['Product_Type'],
'Store_Id': sales['Store_Id'],
'Store_Size': sales['Store_Size'],
'Store_Location_City_Type': sales['Store_Location_City_Type'],
'Store_Type': sales['Store_Type']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
# Make prediction
predicted_sales = model.predict(input_data)[0]
# Convert predicted_sales to Python float
predicted_sales = round(float(predicted_sales), 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 jsonify({'Predicted Sales': predicted_sales})
# Define an endpoint for batch prediction (POST request)
@superkart_api.post('/v1/salesbatch')
def predict_sales_batch():
"""
This function handles POST requests to the '/v1/salesbatch' endpoint.
It expects a CSV file containing product details for multiple products
and returns the predicted sales 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 sales)
predicted_sales = model.predict(input_data).tolist()
# Calculate actual prices
# predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
# Create a dictionary of predictions with product IDs as keys
product_ids = input_data['Product_Id'].tolist() # Assuming 'Product_Id' is the product ID column
output_dict = dict(zip(product_ids, predicted_sales))
# 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_api.run(debug=True)
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