# 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
app = Flask("Store Sales Predictor")

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

# Define a route for the home page (GET request)
@app.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)
@app.post('/v1/sales')
def predict_sales():
    """
    This function handles POST requests to the '/v1/sales' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted store sales as a JSON response.
    """
    # Get the JSON data from the request body
    dataset = request.get_json()

    # Extract relevant features from the JSON data
    sample = {
        'Product_Weight': dataset['Product_Weight'],
        'Product_Sugar_Content': dataset['Product_Sugar_Content'],
        'Product_Allocated_Area': dataset['Product_Allocated_Area'],
        'Product_Type': dataset['Product_Type'],
        'Product_MRP': dataset['Product_MRP'],
        'Store_Establishment_Year': dataset['Store_Establishment_Year'],
        'Store_Size': dataset['Store_Size'],
        'Store_Location_City_Type': dataset['Store_Location_City_Type'],
        'Store_Type': dataset['Store_Type']
    }

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

    # Make a sales prediction using the trained model
    prediction = model.predict(input_data)[0]

    # Return the prediction as a JSON response
    return jsonify({'predicted_sales': float(round(prediction, 2))})


# Define an endpoint for batch prediction (POST request)
#@rental_price_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 store and product details for multiple stores and products
   # 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 log_prices)
   # predicted_log_sales = model.predict(input_data).tolist()

    # Calculate actual prices
   # predicted_sales = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_sales]

    # Create a dictionary of predictions with property IDs as keys
   # property_ids = input_data['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__':
    app.run(debug=True)