Fix any attribute errors:

```
import g4f
from g4f.client import Client
import time
import sys
import json
from typing import List, Dict, Any
import random
from datetime import datetime
import tkinter as tk
from tkinter import scrolledtext, filedialog, messagebox
import threading
import multiprocessing
import spacy
from textblob import TextBlob
import requests

# Load spaCy English model
try:
    nlp = spacy.load('en_core_web_sm')
except:
    import subprocess
    subprocess.run(['python', '-m', 'spacy', 'download', 'en_core_web_sm'])
    nlp = spacy.load('en_core_web_sm')

client = Client(provider=g4f.Provider.Blackbox)

# Configuration Constants
SEARCH_API_KEY = 'YOUR_BING_SEARCH_API_KEY'  # Replace with your Bing Search API Key
SEARCH_API_ENDPOINT = 'https://api.bing.microsoft.com/v7.0/search'

# Define AI Worker Base Class
class AIWorker:
    def __init__(self, name: str, description: str, few_shot_examples: List[Dict[str, str]] = None):
        self.name = name
        self.description = description
        self.few_shot_examples = few_shot_examples if few_shot_examples else []

    def process(self, user_input: str, settings: Dict[str, Any]) -> str:
        """
        Process the user input and return the response.
        This method should be overridden by subclasses.
        """
        raise NotImplementedError("Each AIWorker must implement the process method.")

# Example Worker: DataAnalysisWorker
class DataAnalysisWorker(AIWorker):
    def __init__(self):
        few_shot = [
            {
                "prompt": "Analyze the following sales data and identify trends:",
                "completion": "To analyze the sales data, I'll look at monthly sales figures, identify peaks and troughs, and determine seasonal trends. For example, if sales increase in December, it might indicate a holiday season trend."
            },
            {
                "prompt": "Explain the correlation between marketing spend and sales revenue:",
                "completion": "The correlation between marketing spend and sales revenue can be assessed by calculating the correlation coefficient. A positive correlation suggests that increased marketing spend leads to higher sales revenue, while a negative correlation indicates the opposite."
            }
        ]
        super().__init__(name="DataAnalysisWorker", description="Handles data analysis queries.", few_shot_examples=few_shot)

    def process(self, user_input: str, settings: Dict[str, Any]) -> str:
        # Construct prompt with few-shot examples
        prompt = "You are a data analysis expert.\n\n"
        for example in self.few_shot_examples:
            prompt += f"User: {example['prompt']}\nAssistant: {example['completion']}\n\n"
        prompt += f"User: {user_input}\nAssistant:"

        # Incorporate user settings
        prompt += f" Respond in a {settings.get('response_style', 'informative')} manner with a verbosity level of {settings.get('verbosity', 'medium')}."

        # Call AI model
        try:
            response = client.chat.completions.create(
                model=settings.get('model', 'gpt-4o-mini'),
                messages=[{"role": "system", "content": prompt}],
                stream=True
            )
            full_response = ""
            for chunk in response:
                if chunk.choices[0].delta.content is not None:
                    full_response += chunk.choices[0].delta.content
            return full_response.strip()
        except Exception as e:
            return f"Error in DataAnalysisWorker: {str(e)}"

# Example Worker: CreativeWritingWorker
class CreativeWritingWorker(AIWorker):
    def __init__(self):
        few_shot = [
            {
                "prompt": "Write a short story about a dragon and a knight.",
                "completion": "Once upon a time, in a land far away, a brave knight named Sir Cedric encountered a majestic dragon atop Mount Ember. Instead of fighting, they forged an unlikely friendship, leading their kingdom into an era of peace and prosperity."
            },
            {
                "prompt": "Compose a poem about the ocean at sunset.",
                "completion": "The ocean breathes in hues of gold,\nAs sunsets paint the sky so bold.\nWaves whisper secrets to the shore,\nIn twilight's embrace, forevermore."
            }
        ]
        super().__init__(name="CreativeWritingWorker", description="Handles creative writing and storytelling.", few_shot_examples=few_shot)

    def process(self, user_input: str, settings: Dict[str, Any]) -> str:
        # Construct prompt with few-shot examples
        prompt = "You are a creative writing expert.\n\n"
        for example in self.few_shot_examples:
            prompt += f"User: {example['prompt']}\nAssistant: {example['completion']}\n\n"
        prompt += f"User: {user_input}\nAssistant:"

        # Incorporate user settings
        prompt += f" Respond in a {settings.get('response_style', 'engaging')} manner with a verbosity level of {settings.get('verbosity', 'detailed')}."

        # Call AI model
        try:
            response = client.chat.completions.create(
                model=settings.get('model', 'gpt-4o-mini'),
                messages=[{"role": "system", "content": prompt}],
                stream=True
            )
            full_response = ""
            for chunk in response:
                if chunk.choices[0].delta.content is not None:
                    full_response += chunk.choices[0].delta.content
            return full_response.strip()
        except Exception as e:
            return f"Error in CreativeWritingWorker: {str(e)}"

# New Worker: WebSearchWorker
class WebSearchWorker(AIWorker):
    def __init__(self):
        few_shot = [
            {
                "prompt": "Find the latest news about renewable energy.",
                "completion": "According to recent reports, renewable energy sources are gaining significant traction globally, with solar and wind power leading the charge. Governments are increasing investments to reduce carbon emissions and promote sustainable practices."
            },
            {
                "prompt": "Search for current stock prices of Tesla.",
                "completion": "As of today, Tesla's stock is trading at approximately $850 per share, reflecting a recent surge driven by strong quarterly earnings and positive market sentiment."
            }
        ]
        super().__init__(name="WebSearchWorker", description="Performs web searches and provides concise summaries.", few_shot_examples=few_shot)

    def perform_web_search(self, query: str) -> str:
        """Performs a web search using Bing Search API and returns a concise summary."""
        headers = {"Ocp-Apim-Subscription-Key": SEARCH_API_KEY}
        params = {"q": query, "textDecorations": True, "textFormat": "HTML", "count": 3}
        try:
            response = requests.get(SEARCH_API_ENDPOINT, headers=headers, params=params)
            response.raise_for_status()
            search_results = response.json()
            snippets = []
            if 'webPages' in search_results:
                for page in search_results['webPages']['value'][:3]:
                    snippets.append(page['snippet'])
            summary = " ".join(snippets)
            # Use AI to generate a concise summary from snippets
            summary_prompt = (
                "Summarize the following web search snippets concisely:\n\n"
                + " ".join(snippets)
                + "\n\nSummary:"
            )
            reflection_prompt = summary_prompt  # Reuse reflection for summarization

            try:
                summary_response = client.chat.completions.create(
                    model='gpt-4o-mini',
                    messages=[{"role": "system", "content": summary_prompt}],
                    stream=True
                )
                full_summary = ""
                for chunk in summary_response:
                    if chunk.choices[0].delta.content is not None:
                        full_summary += chunk.choices[0].delta.content
                return full_summary.strip()
            except Exception as e:
                return f"Error during summary generation: {str(e)}"
        except Exception as e:
            return f"Error during web search: {str(e)}"

    def process(self, user_input: str, settings: Dict[str, Any]) -> str:
        # Construct prompt with few-shot examples
        prompt = "You are an expert web researcher.\n\n"
        for example in self.few_shot_examples:
            prompt += f"User: {example['prompt']}\nAssistant: {example['completion']}\n\n"
        prompt += f"User: {user_input}\nAssistant:"

        # Incorporate user settings
        prompt += f" Respond in a {settings.get('response_style', 'informative')} manner with a verbosity level of {settings.get('verbosity', 'medium')}."

        # Extract query for web search
        query = user_input  # In a real scenario, parse to extract the actual search intent

        # Perform web search and summarize
        summary = self.perform_web_search(query)
        return summary

# Define a MetaLearningWorker for Advanced Meta-Learning
class MetaLearningWorker(AIWorker):
    def __init__(self):
        few_shot = [
            {
                "prompt": "Improve the assistant's ability to handle ambiguous queries.",
                "completion": "To enhance the assistant's handling of ambiguous queries, it should ask clarifying questions to better understand the user's intent. Additionally, it can provide possible interpretations of the query and offer corresponding responses."
            },
            {
                "prompt": "Adjust the response style to be more empathetic.",
                "completion": "The assistant should incorporate empathetic language, acknowledge the user's emotions, and respond in a warm and understanding tone. Phrases like 'I understand how you feel' or 'I'm here to help' can make interactions more compassionate."
            }
        ]
        super().__init__(name="MetaLearningWorker", description="Learns from interactions to improve chatbot performance.", few_shot_examples=few_shot)

    def process(self, reflection: str, settings: Dict[str, Any]) -> str:
        """
        Process the reflection to adjust settings or behaviors.
        This is a simplified example of meta-learning where reflections can be used to tweak settings.
        """
        # Example: Parse reflection and adjust settings accordingly
        # This would require more sophisticated NLP for real applications
        if "improve" in reflection.lower() and "clarifying questions" in reflection.lower():
            settings['response_style'] = 'concise'
            settings['verbosity'] = 'high'
            adjustment = "Adjusted response style to 'concise' and verbosity to 'high' based on reflection."
            return adjustment
        elif "empathetic" in reflection.lower():
            settings['response_style'] = 'engaging'
            adjustment = "Adjusted response style to 'engaging' to be more empathetic."
            return adjustment
        else:
            return "No adjustments were necessary based on the reflection."

# New: Multi-Processing Manager
class MultiProcessingManager:
    def __init__(self):
        self.pool = multiprocessing.Pool(processes=4)  # Adjust the number of processes as needed

    def execute(self, func, args):
        return self.pool.apply_async(func, args)

    def close_pool(self):
        self.pool.close()
        self.pool.join()

# New: Enhanced GUI Class with Meta-Learning and Web-Search
class AdvancedChatbotGUI:
    def __init__(self, master, chatbot):
        self.master = master
        self.chatbot = chatbot
        master.title("Advanced AI Chatbot")
        master.geometry("900x700")

        # Initialize conversation history and reflection history
        self.conversation_history: List[Dict[str, str]] = self.chatbot.conversation_history
        self.reflection_history: List[Dict[str, str]] = self.chatbot.reflection_history

        # Initialize AI Workers including WebSearchWorker and MetaLearningWorker
        self.workers = self.chatbot.workers

        # Initialize Multi-Processing Manager
        self.mpm = self.chatbot.mpm  # Reference to multiprocessing manager

        # Initialize main frame
        self.main_frame = tk.Frame(master)
        self.main_frame.pack(fill=tk.BOTH, expand=True)

        # Create text display area
        self.chat_display = scrolledtext.ScrolledText(self.main_frame, wrap=tk.WORD, state='disabled', font=("Arial", 12))
        self.chat_display.pack(padx=10, pady=10, fill=tk.BOTH, expand=True)

        # Create input frame
        self.input_frame = tk.Frame(self.main_frame)
        self.input_frame.pack(padx=10, pady=10, fill=tk.X)

        # User input entry
        self.user_input = tk.Entry(self.input_frame, font=("Arial", 12))
        self.user_input.pack(side=tk.LEFT, fill=tk.X, expand=True, padx=(0,10))
        self.user_input.bind("<Return>", self.send_message)

        # Send button
        self.send_button = tk.Button(self.input_frame, text="Send", command=self.send_message, font=("Arial", 12))
        self.send_button.pack(side=tk.RIGHT)

        # Menu bar
        self.menu = tk.Menu(master)
        master.config(menu=self.menu)

        # File menu
        self.file_menu = tk.Menu(self.menu, tearoff=0)
        self.menu.add_cascade(label="File", menu=self.file_menu)
        self.file_menu.add_command(label="Save Conversation", command=self.chatbot.save_conversation)
        self.file_menu.add_command(label="Load Conversation", command=self.chatbot.load_conversation)
        self.file_menu.add_separator()
        self.file_menu.add_command(label="Exit", command=master.quit)

        # Settings menu
        self.settings_menu = tk.Menu(self.menu, tearoff=0)
        self.menu.add_cascade(label="Settings", menu=self.settings_menu)
        self.settings_menu.add_command(label="Customize AI Behavior", command=self.chatbot.gui.customize_behavior)

        # Initialize Recursive Self-Reflection flag
        self.enable_reflection = True  # Can be toggled via settings

        # Display existing history
        self.display_history()

    def display_history(self):
        """Display the loaded conversation history in the chat_display."""
        self.chat_display.config(state='normal')
        self.chat_display.delete(1.0, tk.END)
        for message in self.conversation_history:
            role = "You" if message['role'] == 'user' else "Assistant"
            self.chat_display.insert(tk.END, f"{role}: {message['content']}\n\n")
        # Optionally display reflections
        for reflection in self.reflection_history:
            self.chat_display.insert(tk.END, f"Assistant (Reflection): {reflection['reflection']}\n\n")
        self.chat_display.config(state='disabled')

    def stream_response(self, response: str, role: str = "Assistant"):
        """Stream the response with a typing effect in the GUI."""
        self.chat_display.config(state='normal')
        self.chat_display.insert(tk.END, f"{role}: ")
        self.chat_display.config(state='disabled')

        def write_chars():
            for char in response:
                self.chat_display.config(state='normal')
                self.chat_display.insert(tk.END, char)
                self.chat_display.yview(tk.END)
                self.chat_display.config(state='disabled')
                time.sleep(random.uniform(0.005, 0.02))
            self.chat_display.insert(tk.END, "\n\n")
            self.chat_display.yview(tk.END)

        threading.Thread(target=write_chars).start()

    def send_message(self, event=None):
        """Handle sending of user messages."""
        user_text = self.user_input.get().strip()
        if user_text:
            # Display user message
            self.chat_display.config(state='normal')
            self.chat_display.insert(tk.END, f"You: {user_text}\n\n")
            self.chat_display.config(state='disabled')
            self.chat_display.yview(tk.END)

            # Clear input field
            self.user_input.delete(0, tk.END)

            # Start a new thread to process the message using multi-processing
            threading.Thread(target=self.process_message, args=(user_text,)).start()

    def process_message(self, user_input: str):
        """Process the user message, handle NLP, get response, and perform reflection."""
        # Perform NLP enhancements
        intent = self.chatbot.detect_intent(user_input)
        sentiment = self.chatbot.analyze_sentiment(user_input)
        entities = self.chatbot.extract_entities(user_input)

        # Determine which worker to use
        worker = self.chatbot.select_worker(user_input, intent)

        if worker:
            # Execute worker processing in a separate process
            async_result = self.chatbot.mpm.execute(worker.process, (user_input, self.chatbot.settings))
            response = async_result.get()
        else:
            # If no specific worker, use default AI response
            async_result = self.chatbot.mpm.execute(self.chatbot.default_ai_response, (user_input,))
            response = async_result.get()

        # Append to conversation history
        self.chatbot.conversation_history.append({"role": "user", "content": user_input})
        self.chatbot.conversation_history.append({"role": "assistant", "content": response})

        # Perform self-reflection in a separate process
        if self.enable_reflection:
            reflection_result = self.chatbot.mpm.execute(self.chatbot.recursive_self_reflection, (user_input, response))
            reflection = reflection_result.get()
            if reflection:
                # Append reflection to conversation history
                self.chatbot.conversation_history.append({"role": "assistant", "content": reflection})
                self.chatbot.reflection_history.append({"query": user_input, "response": response, "reflection": reflection})

                # Meta-Learning: Adjust settings based on reflection
                meta_result = self.chatbot.mpm.execute(self.chatbot.meta_worker.process, (reflection, self.chatbot.settings))
                adjustment = meta_result.get()
                if adjustment:
                    self.chatbot.conversation_history.append({"role": "assistant", "content": adjustment})
                    self.chatbot.reflection_history.append({"query": user_input, "response": response, "reflection": adjustment})

        # Update configuration
        self.chatbot.save_config()

        # Display the response
        self.stream_response(response)

        # Optionally display reflection
        if self.enable_reflection and reflection:
            self.stream_response(reflection, role="Assistant (Reflection)")

    def on_close(self):
        """Handle closing of the GUI."""
        self.chatbot.mpm.close_pool()
        self.master.destroy()

# New Worker: WebSearchWorker (Detailed)
class WebSearchWorker(AIWorker):
    def __init__(self):
        few_shot = [
            {
                "prompt": "Find the latest news about renewable energy.",
                "completion": "According to recent reports, renewable energy sources are gaining significant traction globally, with solar and wind power leading the charge. Governments are increasing investments to reduce carbon emissions and promote sustainable practices."
            },
            {
                "prompt": "Search for current stock prices of Tesla.",
                "completion": "As of today, Tesla's stock is trading at approximately $850 per share, reflecting a recent surge driven by strong quarterly earnings and positive market sentiment."
            }
        ]
        super().__init__(name="WebSearchWorker", description="Performs web searches and provides concise summaries.", few_shot_examples=few_shot)

    def perform_web_search(self, query: str) -> str:
        """Performs a web search using Bing Search API and returns a concise summary."""
        headers = {"Ocp-Apim-Subscription-Key": SEARCH_API_KEY}
        params = {"q": query, "textDecorations": True, "textFormat": "HTML", "count": 3}
        try:
            response = requests.get(SEARCH_API_ENDPOINT, headers=headers, params=params)
            response.raise_for_status()
            search_results = response.json()
            snippets = []
            if 'webPages' in search_results:
                for page in search_results['webPages']['value'][:3]:
                    # Strip HTML tags from snippet
                    snippet = page['snippet']
                    cleaned_snippet = ''.join(c for c in snippet if c.isprintable())
                    snippets.append(cleaned_snippet)
            summary = " ".join(snippets)
            # Use AI to generate a concise summary from snippets
            summary_prompt = (
                "Summarize the following web search snippets concisely:\n\n"
                + " ".join(snippets)
                + "\n\nSummary:"
            )
            try:
                summary_response = client.chat.completions.create(
                    model='gpt-4o-mini',
                    messages=[{"role": "system", "content": summary_prompt}],
                    stream=True
                )
                full_summary = ""
                for chunk in summary_response:
                    if chunk.choices[0].delta.content is not None:
                        full_summary += chunk.choices[0].delta.content
                return full_summary.strip()
            except Exception as e:
                return f"Error during summary generation: {str(e)}"
        except Exception as e:
            return f"Error during web search: {str(e)}"

    def process(self, user_input: str, settings: Dict[str, Any]) -> str:
        # Construct prompt with few-shot examples
        prompt = "You are an expert web researcher.\n\n"
        for example in self.few_shot_examples:
            prompt += f"User: {example['prompt']}\nAssistant: {example['completion']}\n\n"
        prompt += f"User: {user_input}\nAssistant:"

        # Incorporate user settings
        prompt += f" Respond in a {settings.get('response_style', 'informative')} manner with a verbosity level of {settings.get('verbosity', 'medium')}."

        # Extract query for web search
        query = user_input  # In a real scenario, parse to extract the actual search intent

        # Perform web search and summarize
        summary = self.perform_web_search(query)
        return summary

# Define a MetaLearningWorker for Advanced Meta-Learning
class MetaLearningWorker(AIWorker):
    def __init__(self):
        few_shot = [
            {
                "prompt": "Improve the assistant's ability to handle ambiguous queries.",
                "completion": "To enhance the assistant's handling of ambiguous queries, it should ask clarifying questions to better understand the user's intent. Additionally, it can provide possible interpretations of the query and offer corresponding responses."
            },
            {
                "prompt": "Adjust the response style to be more empathetic.",
                "completion": "The assistant should incorporate empathetic language, acknowledge the user's emotions, and respond in a warm and understanding tone. Phrases like 'I understand how you feel' or 'I'm here to help' can make interactions more compassionate."
            }
        ]
        super().__init__(name="MetaLearningWorker", description="Learns from interactions to improve chatbot performance.", few_shot_examples=few_shot)

    def process(self, reflection: str, settings: Dict[str, Any]) -> str:
        """
        Process the reflection to adjust settings or behaviors.
        This is a simplified example of meta-learning where reflections can be used to tweak settings.
        """
        # Example: Parse reflection and adjust settings accordingly
        # This would require more sophisticated NLP for real applications
        if "improve" in reflection.lower() and "clarifying questions" in reflection.lower():
            settings['response_style'] = 'concise'
            settings['verbosity'] = 'high'
            adjustment = "Adjusted response style to 'concise' and verbosity to 'high' based on reflection."
            return adjustment
        elif "empathetic" in reflection.lower():
            settings['response_style'] = 'engaging'
            adjustment = "Adjusted response style to 'engaging' to be more empathetic."
            return adjustment
        else:
            return "No adjustments were necessary based on the reflection."

# Multi-Processing Manager is now part of the chatbot class

class AdvancedChatbot:
    def __init__(self):
        self.config_file = "config.json"
        self.config = self.load_config()

        # Initialize Multi-Processing Manager
        self.mpm = MultiProcessingManager()

        # Initialize AI Workers including WebSearchWorker and MetaLearningWorker
        self.workers = [DataAnalysisWorker(), CreativeWritingWorker(), WebSearchWorker()]
        self.meta_worker = MetaLearningWorker()

        # Initialize settings
        self.settings = self.config.get('settings', {
            'response_style': 'informative',
            'verbosity': 'medium',
            'language': 'English',
            'custom_prompt': ''
        })

        # Initialize conversation history and reflection history
        self.conversation_history: List[Dict[str, str]] = self.config.get('history', [])
        self.reflection_history: List[Dict[str, str]] = self.config.get('reflection_history', [])

        # Initialize Meta-Learning Worker
        self.meta_worker = MetaLearningWorker()

        # Initialize GUI
        self.root = tk.Tk()
        self.gui = AdvancedChatbotGUI(self.root, self)

    def load_config(self) -> Dict[str, Any]:
        """Load configuration from file."""
        try:
            with open(self.config_file, 'r') as f:
                config = json.load(f)
        except FileNotFoundError:
            config = {
                'model': 'gpt-4o-mini',
                'history': [],
                'reflection_history': [],
                'settings': {
                    'response_style': 'informative',
                    'verbosity': 'medium',
                    'language': 'English',
                    'custom_prompt': ''
                }
            }
            self.save_config(config)
        return config

    def save_config(self, config: Dict[str, Any] = None):
        """Save current configuration to file."""
        if config is None:
            config = {
                'model': self.config.get('model', 'gpt-4o-mini'),
                'history': self.conversation_history,
                'reflection_history': self.reflection_history,
                'settings': self.settings
            }
        with open(self.config_file, 'w') as f:
            json.dump(config, f, indent=4)

    def detect_intent(self, user_input: str) -> str:
        """Detect the intent of the user input using NLP."""
        doc = nlp(user_input)
        # Simple intent detection based on keywords
        intents = {
            'data_analysis': ['analyze', 'data', 'statistics', 'trend', 'market analysis'],
            'creative_writing': ['write', 'story', 'poem', 'creative', 'compose'],
            'web_search': ['search', 'find', 'lookup', 'look up', 'google', 'bing'],
            # Add more intents as needed
        }
        for intent, keywords in intents.items():
            if any(keyword in user_input.lower() for keyword in keywords):
                return intent
        return 'general'

    def analyze_sentiment(self, user_input: str) -> str:
        """Analyze the sentiment of the user input."""
        blob = TextBlob(user_input)
        sentiment = blob.sentiment.polarity
        if sentiment > 0.1:
            return 'positive'
        elif sentiment < -0.1:
            return 'negative'
        else:
            return 'neutral'

    def extract_entities(self, user_input: str) -> List[str]:
        """Extract named entities from the user input."""
        doc = nlp(user_input)
        return [ent.text for ent in doc.ents]

    def select_worker(self, user_input: str, intent: str) -> AIWorker:
        """Selects an appropriate worker based on the user input intent."""
        if intent == 'data_analysis':
            return next((worker for worker in self.workers if isinstance(worker, DataAnalysisWorker)), None)
        elif intent == 'creative_writing':
            return next((worker for worker in self.workers if isinstance(worker, CreativeWritingWorker)), None)
        elif intent == 'web_search':
            return next((worker for worker in self.workers if isinstance(worker, WebSearchWorker)), None)
        else:
            return None  # Use default AI response if no worker matches

    def default_ai_response(self, user_input: str) -> str:
        """Generates a default AI response when no specific worker is selected."""
        # Incorporate user settings into the prompt
        prompt = f"Please respond to the following query.\n\nUser: {user_input}\nAssistant:"

        # Add customization settings
        prompt += f" Respond in a {self.settings.get('response_style', 'informative')} manner with a verbosity level of {self.settings.get('verbosity', 'medium')} and in {self.settings.get('language', 'English')}."

        # Append custom prompt if exists
        custom_prompt = self.settings.get('custom_prompt', '')
        if custom_prompt:
            prompt = f"{custom_prompt}\n\n{prompt}"

        try:
            response = client.chat.completions.create(
                model=self.config.get('model', 'gpt-4o-mini'),
                messages=[{"role": "system", "content": prompt}],
                stream=True
            )
            full_response = ""
            for chunk in response:
                if chunk.choices[0].delta.content is not None:
                    full_response += chunk.choices[0].delta.content
            return full_response.strip()
        except Exception as e:
            return f"Error: {str(e)}"

    def recursive_self_reflection(self, user_input: str, assistant_response: str) -> str:
        """Performs recursive self-reflection on the assistant's response."""
        # Construct reflection prompt
        reflection_prompt = (
            "Evaluate the following response for accuracy, coherence, and relevance.\n\n"
            f"User: {user_input}\n"
            f"Assistant: {assistant_response}\n\n"
            "Provide a brief assessment and suggest any improvements if necessary."
        )

        try:
            # Call AI model for reflection
            reflection = client.chat.completions.create(
                model=self.config.get('model', 'gpt-4o-mini'),
                messages=[{"role": "system", "content": reflection_prompt}],
                stream=True
            )
            full_reflection = ""
            for chunk in reflection:
                if chunk.choices[0].delta.content is not None:
                    full_reflection += chunk.choices[0].delta.content
            return full_reflection.strip()
        except Exception as e:
            return f"Error during self-reflection: {str(e)}"

    def perform_meta_learning(self, reflection: str):
        """Adjust settings based on reflection using MetaLearningWorker."""
        adjustment = self.meta_worker.process(reflection, self.settings)
        if adjustment.startswith("Adjusted"):
            # Log the adjustment in conversation history
            self.conversation_history.append({"role": "assistant", "content": adjustment})
            self.reflection_history.append({"query": "Meta-Learning Adjustment", "response": adjustment, "reflection": ""})

    def run(self):
        """Run the GUI."""
        self.root.protocol("WM_DELETE_WINDOW", self.on_close)
        self.gui.display_history()
        self.root.mainloop()

# New Worker: MetaLearningWorker is already defined above

# Multi-Processing Manager Class
class MultiProcessingManager:
    def __init__(self):
        self.pool = multiprocessing.Pool(processes=2)  # Adjust the number of processes as needed

    def execute(self, func, args):
        return self.pool.apply_async(func, args)

    def close_pool(self):
        self.pool.close()
        self.pool.join()

# New: Enhanced GUI Class with Multi-Processing and Meta-Learning Integration
# The AdvancedChatbotGUI class is already updated above.

# Instantiate and run the chatbot
if __name__ == "__main__":
    chatbot = AdvancedChatbot()
    chatbot.run()