How to upgrade your exception handling by using decorators to automatically request debug suggestions from ChatGPT. Code to the tutorial: https://medium.com/@stefan.herdy/upgrade-your-python-exception-handling-with-decorators-e30cc6a4eefa

chatgpdebug.py

import openai
import yaml
import sys
import numpy as np
import openai
import yaml
import traceback
import inspect


def message_compose(err_type, err_message, err_code, err_description):
  message = f"""Hey ChatGPT! I have the following {err_type}: {err_message}. 
  The last lines of my code are: {err_code} . 
  The description of the failing function is: {err_description} 
  How can I improve my code to fix this error? Be as precise as possible!"""
  
  return message

def chat_with_chatgpt(api_path, message):
  with open(api_path, 'r') as file:
          config = yaml.safe_load(file)
          openai.api_key = config['api_key']
      
  messages = [ {"role": "system", "content":  
                "Hey ChatGPT!"} ] 
      
  if message: 
      messages.append( 
          {"role": "user", "content": message}, 
      ) 
      chat = openai.ChatCompletion.create( 
          model="gpt-3.5-turbo", messages=messages 
      ) 
  reply = chat.choices[0].message.content 
  print(f"ChatGPT: {reply}") 
  messages.append({"role": "assistant", "content": reply}) 

  return reply

def capture_stderr(err_description = '', api_path = './config.yaml'):
    def wrapper(func):
        def wrapped_args(*args):
            # Create a StringIO object to capture stderr
            error_message = ''
            try:
                result = func(*args)
                return result
                # If there is an error message, you can handle it here
            except Exception as e:
                # Access more detailed information about the exception
                err_type = type(e).__name__
                err_message = e.args
                print(f'Error Type: {err_type}')
                print(f'Error Message: {e}')
                print("Traceback:")
                traceback.print_tb(e.__traceback__)
                tracebackmessage = e.__traceback__
                # Retrieve the last n lines of code from the traceback
                stack = traceback.extract_tb(sys.exc_info()[2])
                #for filename, line_number, function, line in stack[-1]:
                line_number = stack[-1].lineno
                function = stack[-1].name
                source_code = inspect.getsourcelines(inspect.getmodule(inspect.currentframe()))[0]
                err_code = source_code[line_number-5: line_number]
                message = message_compose(err_type, err_message, err_code, err_description)
                reply = chat_with_chatgpt(api_path, message)
                print(reply)
        return wrapped_args
    return wrapper

description = 'This function initializes an array and returns the value at desired index.'
index = 7
@capture_stderr(description)
def get_value(index):
    simplearray = np.zeros_like(range(7))
    value = simplearray[index]
    return value

value = get_value(index)