app.py

# 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)