# 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
# Changed app name to reflect SuperKart Sales Prediction
superkart_sales_api = Flask("SuperKart Sales Prediction API")

# Load the trained machine learning model
# Updated model filename to match the saved SuperKart model
try:
    model = joblib.load("best_sales_forecasting_model.joblib")
except FileNotFoundError:
    print("Error: 'best_sales_forecasting_model.joblib' not found. Make sure the model file is in the same directory as app.py.")
    # In a real deployment, you might want to handle this more gracefully
    exit() # Exit if the model file is not found

# 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 Sales Prediction API!"

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

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

        # --- Feature Engineering: Calculate 'Store_Age' ---
        current_year = 2025 # Use the same current year as during training
        input_df['Store_Age'] = current_year - input_df['Store_Establishment_Year']
        # -------------------------------------------------

        # --- Feature Engineering: Create 'Product_Id_Prefix' ---
        if 'Product_Id' in input_df:
          input_df['Product_Id_Prefix'] = input_df['Product_Id'].apply(lambda x: x[:2])
        else:
          input_df['Product_Id_Prefix'] = 'NA'

        # --- Feature Engineering: Create 'Product_Perishability' ---
        perishable_types = ['Fruits and Vegetables', 'Dairy', 'Meat', 'Seafood', 'Breakfast']
        non_perishable_types = ['Snack Foods', 'Frozen Foods', 'Household', 'Baking Goods', 'Canned',
                                'Health and Hygiene', 'Soft Drinks', 'Breads', 'Hard Drinks', 'Others',
                                'Starchy Foods']
        input_df['Product_Perishability'] = input_df['Product_Type'].apply(lambda x: 'Perishable' if x in perishable_types else ('Non-Perishable' if x in non_perishable_types else 'Unknown'))
        # -------------------------------------------------

        # Drop identifier columns ('Product_Id' and 'Store_Id') after feature engineering
        # These columns were dropped before training the preprocessor and model.
        processed_input_df = input_df.drop(['Product_Id', 'Store_Id'], axis=1, errors='ignore')

        # Make prediction using the loaded model pipeline
        predicted_sales_total = model.predict(processed_input_df)[0]

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

        # Return the predicted sales total as a JSON response
        return jsonify({'Predicted_Product_Store_Sales_Total': predicted_sales_total})

    except Exception as e:
        # Return an error message if something goes wrong
        return jsonify({'error': str(e)})

# Define an endpoint for batch sales prediction (POST request with CSV)
# This endpoint is commented out as the original task focused on a single prediction endpoint.
# If batch prediction is needed, this section can be adapted.
# @superkart_sales_api.post('/predict_sales_batch')
# def predict_sales_batch():
#     """
#     This function handles POST requests to the '/predict_sales_batch' endpoint.
#     It expects a CSV file containing product and store details for multiple entries
#     and returns the predicted sales totals as a dictionary in the JSON response.
#     """
#     try:
#         # 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)
#
#         # Drop identifier columns if they are present in the input CSV
#         input_data = input_data.drop(['Product_Id', 'Store_Id'], axis=1, errors='ignore')
#
#         # Make predictions for all entries in the DataFrame
#         predicted_sales_totals = model.predict(input_data).tolist()
#
#         # If you need to associate predictions with original identifiers, you would need
#         # to handle that before dropping 'Product_Id' and 'Store_Id'. For simplicity
#         # here, we return a list of predictions.
#         return jsonify({'predictions': predicted_sales_totals})
#
#     except Exception as e:
#         # Return an error message if something goes wrong
#         return jsonify({'error': str(e)})


# Standard Flask run configuration
if __name__ == '__main__':
    # Run the Flask app
    # In a production environment, use a production-ready WSGI server like Gunicorn
    # Using host='0.0.0.0' makes the server accessible externally (important for Hugging Face Spaces)
    superkart_sales_api.run(debug=True, host='0.0.0.0', port=7860) # Listen on all interfaces and port 5000