# Import necessary libraries
import numpy as np
import joblib
import pandas as pd
from flask import Flask, request, jsonify

# Import logging
import logging
import sys

# Initialize the Flask application
superkart_sales_predictor_api = Flask("SuperKart_Sales_Predictor")

# Load the trained machine learning model
model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")

# Define a route for the home page (GET request)
@superkart_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 SuperKart Sales Prediction API!"

@superkart_sales_predictor_api.route("/health", methods=["GET"])
def health():
    return {"status": "ok"}    

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

        # 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_MRP': property_data['Product_MRP'],
            'Store_Size': property_data['Store_Size'],
            'Store_Location_City_Type': property_data['Store_Location_City_Type'],
            'Store_Type': property_data['Store_Type'],
            'Store_Age': property_data['Store_Age'],
            'Product_Category': property_data['Product_Category'],
            'Product_Category_Type': property_data['Product_Category_Type']
        }
    
        # Convert the extracted data into a Pandas DataFrame
        input_data = pd.DataFrame([sample])
    
        # Make prediction (get log_price)
        prediction = model.predict(input_data)[0]
    
        # Return the actual price
        return jsonify({'Sales': prediction})

    except Exception as e:
        return jsonify({'error': str(e)}), 500


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