# 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
super_kart_sales_predictor_api = Flask("Super Kart Sales Predictor")

# Load the trained machine learning model
model = joblib.load("super_kart_sales_prediction_model_v1_0.joblib")

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

# Define an endpoint for single property prediction (POST request)
@super_kart_sales_predictor_api.post('/v1/superkart')
def predict_sale_price():
    """
    This function handles POST requests to the '/v1/superkart' endpoint.
    It expects a JSON payload containing Product details and returns
    the predicted sale price as a JSON response.
    """
    # Get the JSON data from the request body
    product_data = request.get_json()

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

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

    # Calculate actual price
    predicted = model.predict(input_data)[0]

    # Convert predicted_product_price to Python float
    predicted_product_price = round(float(predicted), 2)
    # The above code returns predictions as NumPy float32 values.
    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error

    # Return the actual price
    return jsonify({'Predicted Super Kart Sale (in dollars)': predicted_product_price})


# Define an endpoint for batch prediction (POST request)
@super_kart_sales_predictor_api.post('/v1/superkartbatch')
def predict_sale_price_batch():
    try:
        file = request.files['file']
        input_data = pd.read_csv(file)

        # ✅ Drop unwanted columns
        input_data = input_data.drop(columns=['Product_Id', 'Product_Store_Sales_Total'], errors='ignore')

        predicted = model.predict(input_data)
        predicted_product_price = np.round(predicted.astype(float), 2).tolist()

        return jsonify({
            "predictions": predicted_product_price
        })

    except Exception as e:
        return jsonify({"error": str(e)}), 400

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