import os                                                                        # Provides access to operating system functionalities like reading files, interacting with directories, and managing environment variable
import joblib                                                                    # Used for saving and loading models efficiently. It allows serialization of Python objects, especially machine learning models.
from flask import Flask, request, jsonify                                        # Imports Flask framework for building a web API. Flask initializes the app, request handles incoming data, and jsonify converts responses to JSON format.
import pandas as pd                                                              # Loads pandas, a powerful library for data analysis and manipulation, allowing structured data handling through DataFrames.
import numpy as np                                                               # Imports numpy, which provides support for numerical computations, including arrays, mathematical functions, and statistical operations.
import warnings                                                                  # Enables control over Python warning messages that may arise due to deprecated features or ignored exceptions.
import logging                                                                   # Helps in tracking events and errors within the application by logging messages systematically.
warnings.filterwarnings("ignore")                                                # Suppresses warning messages to ensure cleaner output during execution.

# Initialize flask app with a name
sales_prediction = Flask(__name__)                                               # Creates Flask application

# Configure logging
logging.basicConfig(level=logging.DEBUG)                                         # Configures logging to display debug messages.

# Load the trained model pipeline
try:
    model = joblib.load("SuperKart_Sales_Prediction_Model.joblib")               # Loads saved model
    logging.info("Model loaded successfully.")                                   # Logs a success message     
except Exception as e:                                                           # To log any exception during handling                                                              
    logging.error(f"Error loading model: {e}")                                   # Logs an error message if loading fails
    raise                                                                        # Raises the error for troubleshooting.

# Define an endpoint for making predictions                                      # Returns a welcome message when accessed:
@sales_prediction.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 App!"           

# Define an endpoint for single property prediction (POST request)              # Handles single prediction requests and returns JSON response:
@sales_prediction.post('/v1/predict')
def predict_sales():
    """
    This function handles POST requests to the '/v1/predict' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted rental price as a JSON response.
    """
    # Get the JSON data from the request body
    try:
        business_data = request.get_json()                                       # Extracts JSON data from request body
        logging.debug(f"Received data: {business_data}")                         # Logs the received data for debugging purposes
    except Exception as e:                                                       # To log any exception during handling                             
        logging.error(f"Error decoding JSON: {e}")                               # Logs an error if JSON decoding fails
        return jsonify({'error': f'Invalid JSON: {e}'}), 400                     # Returns a JSON-formatted error response with a 400 (Bad Request) status code. This informs the client that there was an issue with their request.                        

    # Extract relevant features from the JSON data
    try:
        business_data_sample = {
            'Product_Weight': business_data['Product_Weight'],                  # Extracts the weight of the product from the JSON data, which can be a crucial feature for predicting sales
            'Product_Sugar_Content': business_data['Product_Sugar_Content'],    # Retrieves the sugar content classification (e.g., low sugar, regular) of the product, which might influence customer preferences
            'Product_Type': business_data['Product_Type'],                      # Identifies the broad category of the product, such as dairy, snacks, or frozen foods, helping in segmentation analysis.
            'Product_Allocated_Area': business_data['Product_Allocated_Area'],  # Captures the ratio of display space allocated to this product in a store, affecting visibility and sales performance
            'Product_MRP': business_data['Product_MRP'],                        # Extracts the Maximum Retail Price of the product, a key factor influencing purchasing decisions.
            'Store_Size': business_data['Store_Size'],                          # Retrieves the store's size classification (e.g., small, medium, large), which affects inventory and foot traffic.
            'Store_Location_City_Type': business_data['Store_Location_City_Type'],# Identifies whether the store is in a Tier 1, Tier 2, or Tier 3 city, which impacts customer demographics and purchasing power
            'Store_Type': business_data['Store_Type'],                            # Specifies the store format (e.g., supermarket, food mart, or departmental store), affecting pricing strategy and sales
            'Store_Current_Age': business_data['Store_Current_Age']              # Calculates the store’s age based on its establishment year, potentially relevant for analyzing store performance over time.  
        }
    except KeyError as e:                                                      
        logging.error(f"Missing key in JSON data: {e}")                         # Handles missing keys in JSON data                           
        return jsonify({'error': f'Missing key: {e}'}), 400                     # Logs an error message and returns a JSON response with an HTTP 400 status code (Bad Request)
    except TypeError as e:                                                      # Handles TypeError, which occurs when the data received is in an unexpected format
        logging.error(f"Error with data types: {e}")                            # Logs an error message
        return jsonify({'error': f'Incorrect data type: {e}'}), 400             # Logs the issue and returns an error response with HTTP status 400.

    # Convert the extracted data into a Pandas DataFrame
    try:
        business_df = pd.DataFrame(business_data_sample, index=[0])             # Transforms the extracted business data sample into a single-row Pandas DataFrame for structured analysis and model predictions.          
        logging.debug(f"DataFrame: {business_df.head().to_string()}")           # Logs the first few rows of the DataFrame in a formatted string for clearer debugging output
    except Exception as e:                                                      # To log any exception during handling    
        logging.error(f"Error creating DataFrame: {e}")                         # Logs an error message if DataFrame creation fails
        return jsonify({'error': f'Error creating DataFrame: {e}'}), 500        # Returns a JSON-formatted error message with a 500 Internal Server Error status, signaling a failure in DataFrame processing.       

    # Make predictions using the loaded model
    try:                                                                          
        prediction = model.predict(business_df)                                 # Uses the loaded model to make predictions on the business data
        logging.debug(f"Prediction: {prediction}")                              # Logs an error message
    except Exception as e:                                                      # To log any exception during handling                                 
        logging.error(f"Error during prediction: {e}")                          # Logs an error message if DataFrame creation fails                         
        return jsonify({'error': f'Prediction error: {e}'}), 500                # Returning an error code 500 if any issue with JSON

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

# Define an endpoint for batch prediction (POST request)
@sales_prediction.post('/v1/batch_predict')                                     
def batch_predict():
    """
    This function handles POST requests to the '/v1/batch_predict' endpoint.
    It expects a JSON payload containing a list of property details and returns
    the predicted rental prices for each property as a JSON response.
    """
    # Get the uploaded CSV file from the request
    try:
        file = request.files['file']                                            # Retrieves the uploaded file from the request
        logging.debug(f"Received file: {file.filename}")                        # Logs the received file for debugging purposes           
    except Exception as e:                                                      # To log any exception during handling                                                              
        logging.error(f"Error getting file: {e}")                               # Logs an error if file retrieval fails
        return jsonify({'error': f'Error getting file: {e}'}), 400              # Logs the issue and returns an error response with HTTP status 400.          

    # Read the CSV file into a Pandas DataFrame
    try:
        df = pd.read_csv(file)                                                  # creating a variable to store the csv file
        logging.debug(f"DataFrame shape: {df.shape}")                           # Logs the shape of the DataFrame for debugging purposes
    except Exception as e:                                                      # To log any exception during handling    
        logging.error(f"Error reading CSV: {e}")                                # Logs an error if CSV reading fails
        return jsonify({'error': f'Error reading CSV file: {e}'}), 400          # Logs the issue and returns an error response with HTTP status 400. 

    # Make preductions using the loaded model
    try:
        prediction = model.predict(df)                                          # Fit the model to variable 'prediction'
        logging.debug(f"Prediction: {prediction}")                              # Logs the prediction for debugging purposes
    except Exception as e:                                                      # To log any exception during handling  
        logging.error(f"Error during prediction: {e}")                          # Logs an error if prediction fails
        return jsonify({'error': f'Prediction error: {e}'}), 500                # Returning an error code 500 if any issue with JSON

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

# Run the Flask app if this script is executed
if __name__ == '_main_':                                                        # Checks if the script is executed directly.
    sales_prediction.run(debug=True)                                            # Display the sales predicted and handle any debug issue