# Import necessary libraries
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
superkart_sales_api = Flask("SuperKart Product Store Sales Predictor")

# Load the trained machine learning model
model = joblib.load("SuperKart_model_v1_0.joblib")

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

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

    # Extract original features from the JSON data
    original_features = {
        'Product_Id': product_data['Product_Id'],
        '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 to DataFrame to easily calculate engineered features
    input_data = pd.DataFrame([original_features])

    # Calculate engineered features
    current_year = pd.to_datetime('now').year
    input_data['Store_Age'] = current_year - input_data['Store_Establishment_Year']

    perishables = ['Fruits and Vegetables', 'Dairy', 'Meat', 'Seafood', 'Breads', 'Breakfast']
    input_data['Product_Category_Type'] = input_data['Product_Type'].apply(lambda x: 'Perishables' if x in perishables else 'Non Perishables')

    # Assuming Product_Id is in the format 'XX####' and we need the first two characters
    input_data['Product_Category_from_ID'] = input_data['Product_Id'].apply(lambda x: x[:2])


    # Make prediction and round to 2 decimal places
    prediction = round(model.predict(input_data).tolist()[0], 2)

    # Return the prediction as a JSON response
    return jsonify({'Predicted_Product_Store_Sales_Total': prediction})


# Define an endpoint for batch prediction (POST request)
@superkart_sales_api.post('/v1/productbatch')
def predict_product_batch():
    """
    This function handles POST requests to the '/v1/productbatch' endpoint.
    It expects a CSV file containing product and store details for multiple entries
    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)

    # Calculate engineered features for batch prediction
    current_year = pd.to_datetime('now').year
    input_data['Store_Age'] = current_year - input_data['Store_Establishment_Year']

    perishables = ['Fruits and Vegetables', 'Dairy', 'Meat', 'Seafood', 'Breads', 'Breakfast']
    input_data['Product_Category_Type'] = input_data['Product_Type'].apply(lambda x: 'Perishables' if x in perishables else 'Non Perishables')

    # Assuming Product_Id is in the format 'XX####' and we need the first two characters
    input_data['Product_Category_from_ID'] = input_data['Product_Id'].apply(lambda x: x[:2])


    # Make predictions for the batch data and round to 2 decimal places
    predictions = [round(pred, 2) for pred in model.predict(input_data).tolist()]

    # Add predictions to the DataFrame
    input_data['Predicted_Product_Store_Sales_Total'] = predictions


    # Convert results to dictionary
    result = input_data.to_dict(orient="records")

    return jsonify(result)

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