Addressing Persistent Bugs Not Resolved by Lovable AI

 
Error Handling Setup: Create an Error Handler Module
 

• In your project files, add a new file named errorHandler.js. This file will manage errors and help break the error loop cycle.
• Copy the following code into errorHandler.js. This function logs errors, optionally sends error details to a remote service, and can reset the application state if necessary:

  
  function handleError(error) {
    // Log the error details
    console.error("Error occurred:", error);
  
    // Optional: You can add code here to send the error information to a logging service
    // For example, if using an HTTP request, you might call sendErrorReport(error);
    // If the error is within a repetitive loop, consider resetting the state or breaking out
    // This function helps centralize error handling so you can easily add extra recovery logic
  }
  module.exports = { handleError };
      

  
  

 
Integrate Error Handling into Your Main Code
 

• Open your main code file (for instance, app.js) where you suspect recurring errors may be causing a loop.
• At the top of app.js, import the error handler using the following code snippet:

  
  const { handleError } = require('./errorHandler');
      

• Locate the section of your code that might be entering an error loop. Surround that code with try/catch blocks. For example, if you have a loop that processes data repeatedly, modify it as follows:

  
  let errorCount = 0;
  const maxAttempts = 5;
  
  while (true) {
    try {
      // Insert your main operation code here which might throw an error
      processYourData();
  // Reset error count on successful operation
  errorCount = 0;
  
    } catch (error) {
      errorCount++;
      handleError(error);
  if (errorCount >= maxAttempts) {
    console.error("Maximum error attempts reached. Exiting the loop to prevent further errors.");
    break;
  }
  
  // Optional: Pause briefly before the next try (simulate delay without terminal)
  // This can be as simple as a setTimeout if using asynchronous code:
  // await new Promise(resolve => setTimeout(resolve, 1000));
  
    }
  }
      

  
  


• 
  Ensure that the function processYourData() represents the part of your code where errors are occurring repeatedly. Replace it with your actual function or code block.
  

 
Manage Dependencies Without a Terminal
 

• Since Lovable does not have a terminal, you must include any dependency installation directly into your code.
• If your error handler or application uses external libraries, you can simulate dependency management by including their code manually or by referencing a hosted version.
• For example, if you need a library to send HTTP requests, add its code directly in your project or use a script tag in your HTML to load it.
• If you require a package.json file to declare dependencies, create a file named package.json and include content like this:

  
  {
    "dependencies": {
      "some-library": "latest"
    }
  }
      

  Note: Without a terminal, Lovable might automatically read this file to manage dependencies. If not, consult Lovable's documentation for the required format to embed dependencies.

 
Summary of Steps and Integration Points
 

• Create the errorHandler.js file to centralize your error handling logic.
• Import and use the error handling function in your main file (e.g., app.js) by wrapping your error-prone code within try/catch blocks.
• Manage dependencies by providing a package.json file or embedding required library code directly, as Lovable does not support a terminal for installation.
• If the error loop persists, consider adding further logic to reset the application state or delay subsequent attempts to allow the system to recover.

 
Centralized Error Logging
 

• In your Lovable project, create a new file named error\_logger.py. This file will include the code that logs errors to a file, helping you track and troubleshoot issues without needing a terminal.
• Paste the following code into error\_logger.py:

• 
  import datetime
  
  def log_error(error_msg):
      """
      Logs the error message with a timestamp to a file named error.log.
      """
      timestamp = datetime.datetime.now().isoformat()
      with open("error.log", "a") as log_file:
          log_file.write(f"{timestamp} - ERROR: {error_msg}\n")
        

  
  




• In your main application file (for example, main.py), import this logger by adding this line at the beginning:




• 
  

  
  from error_logger import log_error
        

  
  

 
Robust Exception Handling
 

• Surround critical code sections with try/except blocks to catch errors as they happen. This practice avoids bugs from escalating into repeated cycles.
• For example, in your main.py wherever a critical function is called, wrap it as follows:

• 
  try:
      # Replace this with your critical operation
      result = perform_critical_operation()
  except Exception as error:
      log\_error(str(error))
        

• This captures any unexpected errors and logs them using the centralized logger.

 
Input Validation
 

• Invalid inputs can cause errors down the line. Validate inputs to catch problems early before they spiral into larger issues.
• You may add a new file named utilities.py to handle data validation. In it, include a function like this:

• 
  def validate_input(user_input):
      """
      Ensures that user\_input is a string and strips unwanted spaces.
      Raises ValueError if the input is not valid.
      """
      if not isinstance(user\_input, str):
          raise ValueError("Input must be a string")
      return user\_input.strip()
        

• Then in your main.py, import and use the function as follows:

• 
  from utilities import validate\_input
  
  try:
      valid_input = validate_input(raw_input)
  except ValueError as error:
      log_error(str(error))
        

  
  

 
Modular Code Organization
 

• Splitting your code into separate files keeps each part manageable and makes troubleshooting easier. For example, maintain the following structure:
• main.py – Contains the primary application logic.
• error\_logger.py – Handles error logging.
• utilities.py – Contains helper functions like data validation.
• config.py – Manages configuration settings.
• This separation makes it easier to pinpoint and fix issues as they occur.

 
Automated Testing Integration
 

• Even if Lovable does not have a terminal, you can include testing code that helps verify each module. Create a file named test\_suite.py to hold automated tests.
• Add the following code to test\_suite.py to do basic testing on your functions:

• 
  def test\_validation():
      """
      Tests the validate\_input function to ensure it raises an error for invalid types.
      """
      try:
          # This should raise a ValueError since the input is not a string.
          validate\_input(123)
      except ValueError as error:
          print("Test Passed:", error)
      else:
          print("Test Failed")
  
  if name == "main":
      test_validation()
        

  
  




• You can manually trigger these tests by invoking the test function from your application if needed.

 
Debug Mode Toggle
 

• Sometimes you want more information during development. Introduce a debug mode flag to print extra information without overloading the release version with debug messages.
• Create or update the file config.py with these lines:

• 
  Set this to False on release to avoid debug output
  DEBUG\_MODE = True
  
  def debug_print(message):
      if DEBUG_MODE:
          print("DEBUG:", message)
        

  
  




• Import and use debug_print in your main.py when needed:




• 
  

  
  from config import debug_print
  
  debug_print("This is a debug message.")
        

  
  




• This way, you can toggle detailed debugging information without changing the core logic of your code.

 
Consistent Documentation and Code Comments
 

• Clear comments and documentation in your code help identify the purpose of each function and module, making it easier to trace the source of bugs.
• Add a header comment to every new file explaining its purpose. For instance, at the top of error\_logger.py add:

• 
  """
  error\_logger.py
  
  This module provides the log_error function to capture and log all errors.
  It helps in tracing issues in the absence of a terminal interface.
  """
        

  
  




• Using in-code comments like these throughout your project creates a self-documenting codebase that simplifies troubleshooting.