# Import necessary libraries
import numpy as np
import joblib  # For loading the serialized model
import pandas as pd  # For data manipulation
import logging
import sys
from flask import Flask, request, jsonify  # For creating the Flask API

# Initialize the Flask application
sales_revenue_predictor_api = Flask("SuperKart Sales Revenue Predictor")

# Configure logging to output to stdout (Hugging Face captures this)
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[logging.StreamHandler(sys.stdout)]
)

logger = logging.getLogger("SuperKart Sales Revenue Predictor")

# Load the trained machine learning model
model = joblib.load("superkart_sales_prediction_model_v2_0.joblib")


# Define a route for the home page (GET request)
@sales_revenue_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 Revenue Prediction API!"

# Define an endpoint for single property prediction (POST request)
@sales_revenue_predictor_api.post('/v1/sales')
def predict_sales_revenue():
    """
    This function handles POST requests to the '/v1/sales' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted Sales Revenue as a JSON response.
    """
    logger.info(f"Request received: {request.method} {request.path}")
    logger.debug(f"Headers: {dict(request.headers)}")
    logger.debug(f"Body: {request.get_json()}")
    # Get the JSON data from the request body
    property_data = request.get_json()

    # Extract relevant features from the JSON data
    sample = {
    'Store_Id': property_data['store_id'],
	'Product_Type': property_data['product_type'],
	'Product_Sugar_Content': property_data['product_sugar_content'],
	'Product_MRP': property_data['product_mrp'],
    'Product_Weight': property_data['product_weight'],
    'Product_Allocated_Area': property_data['product_allocated_area']
    }

    # Convert the extracted data into a Pandas DataFrame
    input_data = pd.DataFrame([sample])
    logger.debug(f"input Data : {input_data}")
    # Make prediction (get sales revenue)
    predicted_sales_revenue = model.predict(input_data)[0]

    # Convert predicted_sales_revenue to Python float
    predicted_sales_revenue = round(float(predicted_sales_revenue), 2)
    logger.debug(f"Predicted Sales Revenue : {predicted_sales_revenue}")
    # Return the predicted_sales_revenue
    return jsonify({'Predicted Sales Revenue': predicted_sales_revenue})


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