## Appendix A  
This appendix provides details on the comprehensive structure of both the backend (Flask) and the frontend (HTML and JavaScript) components. Below is a more general structure for Flask code that separates the backend (Flask) from the front end (HTML and JavaScript) and serves as a web page that includes a map, graphs, and filters. Further implementation of map preparation, graphs, manipulation filters, and data updates in JavaScript will depend on each specific requirement. Simplified Flask Code with Data Lake Processing and Output  from flask import Flask, render_template, jsonify  import pandas as pd  import json  app = Flask(_name_)  # Function to load processed data from an output CSV  def load_data_from_processed_csv(csv_path):  try:  # Load data from the CSV file  data = pd.read_csv(csv_path)  return data  except Exception as e:  print(f\"Error loading data from CSV: {str(e)}\")  return pd.DataFrame()Extract unique values for filters  def get_unique_values(data):  unique_years = data['year'].unique().tolist()  unique_areas = data['Areas'].unique().tolist()  unique_issues = data['issue'].unique().tolist()  unique_issue_Probability = data['issue_Probability']. unique().tolist()  return unique_years, unique_areas, unique_issues,  unique_issues_Probability # Route to serve the main page  @app.route('/')  def index():  try:  # Set the path to the processed CSV file from the data lake  csv_path = r\"G:\DataLake\processed_data_output.csv\"  # Load data from the processed CSV file  digital_twin_data = load_data_from_processed_csv(csv_path)  # Check if data is empty  if digital_twin_data.empty:  return render_template('index.html', issues='[]',  unique_issue_Probability='[]')  # Get unique values for filters  unique_years, unique_areas, unique_issues,  unique_issue_Probability = get_unique_values(digital_twin_data)  # Convert DataFrame to a list of dictionaries and handle NaN values  data_list = digital_twin_data.replace({pd.NaT: None}). to_dict(orient='records')  # Pass data to the HTML template  return render_template('index.html', issues=json.dumps(data_  list), unique_issue_Probability=list(map(int, unique_issue_Probability)))  except Exception as e:  print(f\"Error processing data: {str(e)}\")  # Pass empty arrays if an error occurs  return render_template('index.html', issues='[]',  unique_issue_Probability='[]')  # Run the Flask app  if __name__ == '__main__':  app.run(debug=True)  
**Key points about the general structure of the Flask code:**  
Load Data from Data Lake Output: The load_data_from_processed_csv function is responsible for loading processed data from the output csv file specified by the csv_path variable. This function handles exceptions and returns an empty DataFrame if there is an error loading the data. Error Handling: The code includes try-except blocks for loading data from the csv file. If an error occurs during data loading, it prints an error message to the console, and an empty DataFrame is returned. Data Processing: The code assumes that the processed data is stored in the specified csv file. The csv_path variable appends to the correct path of the csv file of the processed data. Unique Values Extraction: The get_unique_values function extracts unique values for years, areas, issues, and issue probability according to the data of the current work. This information is likely used for setting up filters on the frontend. HTML Template Rendering: The code renders the HTML template ('index.html') and passes the processed data to it. If the data is empty, it passes empty arrays to ensure that the frontend gracefully handles the absence of data. Run the Flask App: The script runs the Flask app when executed directly. The _if _name___ == '_main_': block ensures that the app starts only when the script is executed directly, not when it's imported as a module.