# 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
sales_revenue_predictor_api = Flask("Sales Revenue Predictor")

# Load the trained machine learning model
model = joblib.load("sales_revenue_prediction_model_v1_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 Sales Revenue Prediction API!"

# Define an endpoint for single property prediction (POST request)
@sales_revenue_predictor_api.post('/v1/revenue')
def predict_rental_price():
    """
    This function handles POST requests to the '/v1/revenue' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted sales revenue as a JSON response.
    """
    # Get the JSON data from the request body
    product_store_data = request.get_json()

    # Extract relevant features from the JSON data
    sample = {
        'Product_Id': product_store_data['Product_Id'],
        'Product_Weight': product_store_data['Product_Weight'],
        'Product_Sugar_Content': product_store_data['Product_Sugar_Content'],
        'Product_Allocated_Area': product_store_data['Product_Allocated_Area'],
        'Product_Type': product_store_data['Product_Type'],
        'Product_MRP': product_store_data['Product_MRP'],
        'Store_Id': product_store_data['Store_Id'],
        'Store_Establishment_Year': product_store_data['Store_Establishment_Year'],
        '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']
    }

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

    # Make prediction (raw revenue if model trained without log-transform)
    predicted_product_Store_Sales_Total = model.predict(input_data)[0]

    # Convert predicted revenue to Python float
    predicted_revenue = round(float(predicted_product_Store_Sales_Total), 2)

    # Return as JSON
    return jsonify({'predicted_revenue': predicted_revenue})
# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
    sales_revenue_predictor_api.run(debug=True)