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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
app = Flask("Store Sales Predictor")
# Load the trained machine learning model
model = joblib.load("store_sales_prediction_model_v1_0.joblib")
# Define a route for the home page (GET request)
@app.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 Store Sales Prediction API!"
# Define an endpoint for single property prediction (POST request)
@app.post('/v1/sales')
def predict_sales():
"""
This function handles POST requests to the '/v1/sales' endpoint.
It expects a JSON payload containing property details and returns
the predicted store sales as a JSON response.
"""
# Get the JSON data from the request body
dataset = request.get_json()
# Extract relevant features from the JSON data
sample = {
'Product_Weight': dataset['Product_Weight'],
'Product_Sugar_Content': dataset['Product_Sugar_Content'],
'Product_Allocated_Area': dataset['Product_Allocated_Area'],
'Product_Type': dataset['Product_Type'],
'Product_MRP': dataset['Product_MRP'],
'Store_Establishment_Year': dataset['Store_Establishment_Year'],
'Store_Size': dataset['Store_Size'],
'Store_Location_City_Type': dataset['Store_Location_City_Type'],
'Store_Type': dataset['Store_Type']
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([sample])
# Make a sales prediction using the trained model
prediction = model.predict(input_data)[0]
# Return the prediction as a JSON response
return jsonify({'predicted_sales': float(round(prediction, 2))})
# Define an endpoint for batch prediction (POST request)
#@rental_price_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 store and product details for multiple stores and 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 properties in the DataFrame (get log_prices)
# predicted_log_sales = model.predict(input_data).tolist()
# Calculate actual prices
# predicted_sales = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_sales]
# Create a dictionary of predictions with property IDs as keys
# property_ids = input_data['id'].tolist() # Assuming 'id' is the property ID column
# output_dict = dict(zip(property_ids, predicted_sales)) # 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__':
app.run(debug=True)
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