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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
rental_price_predictor_api = Flask("SuperKart Revenue Predictor")
# Load the trained machine learning model
model = joblib.load("superKart_price_prediction_model_v1_0.joblib")
# Define a route for the home page (GET request)
@rental_price_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 Revenue Prediction API!"
# Define an endpoint for single Product prediction (POST request)
#@rental_price_predictor_api.post('/v1/rental')
@rental_price_predictor_api.post('/v1/revenue')
def predict_rental_price():
"""
This function handles POST requests to the '/v1/revenue' endpoint.
It expects a JSON payload containing input details and returns
the predicted revenue 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_Id': property_data['store_id'],
'Store_Age': property_data['store_age'],
'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 log_price)
predicted_price = model.predict(input_data)[0] # The model predicts the final price, not log price
# Return the actual price
predicted_price = round(float(predicted_price), 2)
return jsonify({'Predicted Revenue (in dollars)': predicted_price})
# Define an endpoint for batch prediction (POST request)
@rental_price_predictor_api.post('/v1/rentalbatch')
def predict_rental_price_batch():
"""
This function handles POST requests to the '/v1/rentalbatch' 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_prices = model.predict(input_data).tolist() # The model predicts the final price, not log price
# Calculate actual prices
predicted_prices = [round(float(price), 2) for price in predicted_prices] # Use predicted prices directly
# Create a dictionary of predictions with property IDs as keys
# Assuming the batch input CSV has an 'id' column
if 'Product_Id' in input_data.columns:
product_ids = input_data['Product_Id'].tolist()
output_dict = dict(zip(product_ids, predicted_prices))
else:
# If no 'Product_Id' column, return predictions in a list
output_dict = {'predictions': predicted_prices}
# Return the predictions dictionary as a JSON response
return jsonify(output_dict)
# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
rental_price_predictor_api.run(debug=True)
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