# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd  

# For loading the serialized model
import joblib  

# For creating the Flask API
from flask import Flask, request, jsonify  

# Initializing the Flask application
product_store_sales_predictor_api = Flask("Product Store Sales Predictor")

# Loading the serialised ML model (XGBRegressor Tuned)
model = joblib.load("product_store_sales_prediction_model_v1_0.joblib")

# Route for the home page (GET request)
@product_store_sales_predictor_api.get('/')
def home():
    """
    This function handles GET requests to the root URL ('/') of the API.
    It returns a welcome message.
    """
    return "Welcome to the Product Store Sales Prediction API!"

# Endpoint for Sales prediction for a single product in a given store (POST request)
@product_store_sales_predictor_api.post('/v1/sales')
def predict_sales():
    """
    This function handles POST requests to the '/v1/sales' endpoint.
    It expects a JSON payload containing Product and Store details and returns
    the predicted sales amount as a JSON response.
    """
    # Retrieving the JSON data from the request body
    product_store_data = request.get_json()

    # Extracting the required from the JSON data
    sample = {
        'Product_Weight': product_store_data['Product_Weight'], 
        'Product_Allocated_Area': product_store_data['Product_Allocated_Area'],
        'Product_MRP': product_store_data['Product_MRP'],
        'Store_Establishment_Year': product_store_data['Store_Establishment_Year'],
        'Product_Sugar_Content': product_store_data['Product_Sugar_Content'],
        'Product_Type': product_store_data['Product_Type'],
        'Store_Id': product_store_data['Store_Id'],
        'Store_Size': product_store_data['Store_Size'],
        'Store_Location_City_Type': product_store_data['Store_Location_City_Type'],
        'Store_Type': product_store_data['Store_Type']
    }

    # Converting the extracted data into a Pandas DataFrame
    input_data = pd.DataFrame([sample])

    # Predicting the sales amount
    predicted_sales = model.predict(input_data)[0]

    # Convert predicted_sales to Python float
    predicted_sales = round(float(predicted_sales), 2)

    # Return the predicted sales amount
    return jsonify({'Predicted Sales Amount': predicted_sales})

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