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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
store_sales_predictor_api = Flask("SuperKart Store Sales Predictor")
# Load the trained machine learning model SuperKart_Project_model_v1_0.joblib
#model = joblib.load("SuperKart_Project_model_v1_0.joblib")
try:
model = joblib.load("SuperKart_Project_model_v1_0.joblib")
except Exception as e:
model = None
print("⚠️ Failed to load model:", e)
# Define a route for the home page (GET request)
@store_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 Store Sales Prediction API!"
# Define an endpoint for single sales prediction (POST request)
@store_sales_predictor_api.post('/v1/storeSales')
def predict_store_sales():
"""
This function handles POST requests to the '/v1/storeSales' endpoint.
It expects a JSON payload containing product details and returns
the predicted store sales as a JSON response.
"""
# Get the JSON data from the request body
sales_data = request.get_json()
# Extract relevant features from the JSON data
sample = {
'Product_Weight': sales_data['Product_Weight'],
'Product_Sugar_Content': sales_data['Product_Sugar_Content'],
'Product_Allocated_Area': sales_data['Product_Allocated_Area'],
'Product_Type': sales_data['Product_Type'],
'Product_MRP': sales_data['Product_MRP'],
'Store_Id': sales_data['Store_Id'],
'Store_Size': sales_data['Store_Size'],
'Store_Location_City_Type': sales_data['Store_Location_City_Type'],
'Store_Type': sales_data['Store_Type']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
# Make prediction (get store_sales)
predicted_store_sales = model.predict(input_data)[0]
# Calculate actual sales (convert to plain Python float and round)
predicted_sales = round(float(np.exp(predicted_store_sales)), 2)
# Return the actual sales
return jsonify({'Predicted Sales (in dollars)': predicted_sales})
# The conversion above is needed as we convert the model prediction (store sales) to actual sales 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 sales
return jsonify({'Predicted Sales (in dollars)': predicted_sales})
# Define an endpoint for batch prediction (POST request)
@store_sales_predictor_api.post('/v1/salesbatch')
def predict_store_sales_batch():
"""
This function handles POST requests to the '/v1/salesbatch' endpoint.
It expects a CSV file containing sales details for multiple stores
and returns the predicted store sales as a dictionary in the JSON response.
"""
# Get the uploaded CSV file from the request
file = request.files['file']
if file is None:
return jsonify({"error": "No file uploaded. Please upload a CSV file with key 'file'."}), 400
# Read the CSV file into a Pandas DataFrame
input_data = pd.read_csv(file)
# Make predictions for all products in the DataFrame (get store_sales)
predicted_store_sales = model.predict(input_data).tolist()
# Calculate actual sales
predicted_sales = [round(float(np.exp(store_sales)), 2) for store_sales in predicted_store_sales]
# Create a dictionary of predictions with store IDs as keys
store_ids = input_data['Store_Id'].tolist() # Assuming 'id' is the store ID column
output_dict = dict(zip(store_ids, predicted_sales)) # Use actual 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__':
store_sales_predictor_api.run(debug=True)
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