# 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
import logging

# Configure the logging
logging.basicConfig(
    level=logging.INFO,  # You can change to DEBUG for more details
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Initialize the Flask application
sales_predictor_api = Flask("Sales Predictor")

# Load the trained machine learning model
model = joblib.load("deployment_files/sales_forecast_model_v1_0.joblib")

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

# Define an endpoint for single property prediction (POST request)
@sales_predictor_api.post('/v1/prediction')
def predict_rental_price():
    """
    This function handles POST requests to the '/v1/prediction' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted rental price as a JSON response.
    """
    # Get the JSON data from the request body
    logger.debug(f"predict_rental_price called")
    property_data = request.get_json()
    logger.debug(f"property data {property_data}")

    # Extract relevant features from the JSON data
    sample = {
        'Product_Weight': property_data['Product_Weight'],
        'Product_Allocated_Area': property_data['Product_Allocated_Area'],
        'Product_MRP': property_data['Product_MRP'],
        'Store_Establishment_Year': property_data['Store_Establishment_Year'],
        'Product_Sugar_Content': property_data['Product_Sugar_Content'],
        'Product_Type': property_data['Product_Type'],
        'Store_Id': property_data['Store_Id'],
        '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])
    logger.debug(f"input_data {input_data}")
    # Make prediction 
    predicted_sales = model.predict(input_data)[0]

    logger.debug(f"predicted_sales {predicted_sales}")
   

    # Return the actual price
    return jsonify({'Predicted Sales ': str(predicted_sales)})



if __name__ == '__main__':
    sales_predictor_api.run(debug=True)