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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
superkart_sales_forecast_api = Flask("SuperKart Sales Forecast Backend")
# Load the trained machine learning model
model = joblib.load("SuperKart_Sales_Forecast_v1_0.joblib")
# Define a route for the home page (GET request)
@superkart_sales_forecast_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 Forecast API!"
# Define an endpoint for single property prediction (POST request)
@superkart_sales_forecast_api.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 rental price as a JSON response.
"""
# Get the JSON data from the request body
property_data = request.get_json()
# Extract relevant features from the JSON data
sample = {
'Product_Weight': property_data['product_weight'],
'Product_Sugar_Content': property_data['product_sugar_content'],
'Product_Allocated_Area': property_data['product_allocated_area'],
'Product_Type': property_data['product_type'],
'Product_MRP': property_data['product_mrp'],
'Store_Establishment_Year': property_data['store_establishment_year'],
'Store_Size': property_data['store_size'],
'Store_Location_City_Type': property_data['store_location_city_type'],
'Store_Type': property_data['store_type']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
# Make prediction (get sales)
predicted_sales = model.predict(input_data)[0]
# Convert to Python float and round
predicted_sales = round(float(predicted_sales), 2)
# Return the actual price
return jsonify({'Predicted Sales Forecast (in dollars)': predicted_sales})
# Define an endpoint for batch prediction (POST request)
@superkart_sales_forecast_api.post('/v1/salesbatch')
def predict_sales_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 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_prices)
predicted_sales = model.predict(input_data).tolist()
# Use row indices as keys
output_dict = {f"Row_{i}": round(float(pred), 2) for i, pred in enumerate(predicted_sales)}
return jsonify(output_dict)
# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
superkart_sales_forecast_api.run(debug=True)
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