# 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
store_sales_predictor_api = Flask("Store_sales Price Predictor")

# Load the trained machine learning model
model = joblib.load("store_sales_prediction_model_v1_0.joblib")
print(model)

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

# Define an endpoint for single property prediction (POST request)
@store_sales_predictor_api.post('/v1/sales')
def predict_store_sales():
    """
    This function handles POST requests to the '/v1/sales' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted sales as a JSON response.
    """
    # 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_Type':property_data['Product_Type'],
        'Product_MRP':property_data['Product_MRP'],
        'Store_Establishment_Year':property_data['Store_Establishment_Year'],
        '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])

    # Make prediction (get store_sales)
    predicted_store_sales = model.predict(input_data)[0]

    # Calculate actual price
    predicted_sales = predicted_store_sales

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

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


# Define an endpoint for batch prediction (POST request)
@store_sales_predictor_api.post('/v1/salesbatch')
def predict_store_sales_batch():
    """
    This function handles POST requests to the '/v1/salesbatch' endpoint.
    It expects a CSV file containing property details for multiple properties
    and returns the predicted sales as a dictionary in the JSON response.
    """
    # Get the uploaded CSV file from the request
    file = request.files['file']

    # Read the CSV file into a Pandas DataFrame
    input_data = pd.read_csv(file)

    # Make predictions for all properties in the DataFrame (get store_saless)
    predicted_sales = model.predict(input_data.drop("Product_Id",axis=1)).tolist()

    # Create a dictionary of predictions with property IDs as keys
    property_ids = input_data['Product_Id'].tolist()  # Assuming 'id' is the property ID column
    output_dict = dict(zip(property_ids, predicted_sales))  # Use actual prices

    # Return the predictions dictionary as a JSON response
    return output_dict

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