# 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_price_predictor_api = Flask("Superkart Sales 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)
@superkart_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 Sales Prediction API!"

# Define an endpoint for single property prediction (POST request)
@superkart_price_predictor_api.post('/v1/sales')
def predict_sales_price():
    """
    This function handles POST requests to the '/v1/sales' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted sales revenue 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

    input_data = pd.DataFrame([{
    'Product_Weight': Product_Weight,
    'Product_Sugar_Content': Product_Sugar_Content,
    'Product_Allocated_Area': Product_Allocated_Area,
    'Product_Type': Product_Type,
    'Product_MRP': Product_MRP,
    'Store_Establishment_Year': Store_Establishment_Year,
    'Store_Size': Store_Size,
    'Store_Location_City_Type': Store_Location_City_Type,
    'Store_Type': Store_Type
}])


    # Make prediction (get log_price)
    predicted_price = model.predict(input_data)[0]

    # Return the actual price
    return jsonify({'Predicted Price (in dollars)': predicted_price})

# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
    superkart_price_predictor_api.run(debug=True)