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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_total_sales_predictor_api = Flask("Store Total Sales Predictor")
# Load the trained machine learning model
model = joblib.load("store_total_sales_prediction_model_v1_0.joblib")
# Define a route for the home page (GET request)
@store_total_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 Store Total Sales Prediction API!"
# Define an endpoint for single sales prediction (POST request)
@store_total_sales_predictor_api.post('/v1/storeSales')
def predict_store_total_sales():
"""
This function handles POST requests to the '/v1/storeSales' endpoint.
It expects a JSON payload containing store details and returns
the predicted total sales as a JSON response.
"""
# Get the JSON data from the request body
store_data = request.get_json()
# Extract relevant features from the JSON data
sample = {
'Product_Weight': store_data['product_weight'],
'Product_Sugar_Content': store_data['product_sugar_content'],
'Product_Allocated_Area': store_data['product_allocated_area'],
'Product_Type': store_data['product_type'],
'Product_MRP': store_data['product_mrp'],
'Store_Id': store_data['store_id'],
'Store_Establishment_Year': store_data['store_establishment_year'],
'Store_Size': store_data['store_size'],
'Store_Location_City_Type': store_data['store_location_city_type'],
'Store_Type': store_data['store_type']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
#st.write("Converted Json:", input_data.to_dict(orient='records')[0])
# Make prediction (get log_sales)
predicted_log_total_sales = model.predict(input_data).tolist()[0]
# Calculate actual price
#predicted_total_sales = np.exp(predicted_log_total_sales)
predicted_total_sales = predicted_log_total_sales
# Convert predicted_price to Python float
#predicted_total_sales = round(float(predicted_total_sales), 2)
# The conversion above is needed as we convert the model prediction (log total 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 price
return jsonify({'Predicted_Store_Total_Sales': predicted_total_sales})
# Define an endpoint for batch prediction (POST request)
@store_total_sales_predictor_api.post('/v1/storeSalesbatch')
def predict_store_total_sales_batch():
"""
This function handles POST requests to the '/v1/storeSalesbatch' endpoint.
It expects a CSV file containing property details for multiple properties
and returns the predicted rental 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 properties in the DataFrame (get log_sales)
predicted_log_total_sales = model.predict(input_data).tolist()
# Calculate actual prices
#predicted_store_total_sales = [round(float(np.exp(log_total_sales)), 2) for log_total_sales in predicted_log_total_sales]
predicted_store_total_sales = predicted_log_total_sales
# Create a dictionary of predictions with Product Id as Unique keys for each record
product_ids = input_data['Product_Id'].tolist() # Assuming 'id' is the Product ID column
output_dict = dict(zip(product_ids, predicted_store_total_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__':
store_total_sales_predictor_api.run(debug=True)
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