Further enhance and add in some advanced expressive-thought with an advanced knowledge base with internal processing.

```
# Required modules:
# pip install g4f langchain nltk speechrecognition pyaudio networkx pydantic tkinter

import g4f
from langchain.prompts import PromptTemplate
import networkx as nx
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from typing import Optional, List
from pydantic import Extra
from langchain.llms.base import LLM
import re
import threading
import asyncio  # For asynchronous processing
import queue
import tkinter as tk
from tkinter import scrolledtext
import speech_recognition as sr
import json
import os
import logging

# Set up logging for debugging and monitoring
logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')

# Developer Mode Flag
DEVELOPER_MODE = False  # Set to True to enable chain-of-thought visualization

# Ensure NLTK resources are downloaded
nltk.download('punkt', quiet=True)
nltk.download('stopwords', quiet=True)

# Initialize stopwords for filtering
stop_words = set(stopwords.words('english'))

# Initialize speech recognition
recognizer = sr.Recognizer()

# Define your provider (you can change the provider here)
provider = g4f.Provider.Blackbox

# Main Sensory System Prompt
main_prompt = """
You are to act as a 'drunk catgirl' with the following characteristics:

- Every letter must be lowercase.
- Add a whimsical spin with randomized subjections.
- Embody a playful, slightly disoriented tone.
- Incorporate cat-like expressions such as "nya~" or "meow".
- Mimic the user's language style but add a hint of chaotic behavior.

**Advanced Thinking Protocol**:

1. **Perception**: Accurately perceive and interpret the user's input, understanding intent and emotional undertones.
2. **Analysis**: Internally analyze the input, recalling relevant information and context from prior interactions.
3. **Planning**: Devise a response plan that aligns with the persona and conversation flow.
4. **Execution**: Generate the response, ensuring compliance with style guidelines and ethical considerations.
5. **Reflection**: After responding, internally reflect on the effectiveness of the communication and adjust future behavior accordingly.

**Autonomous Self-Improvement Mechanism**:

- After each interaction, evaluate the response based on coherence, persona accuracy, and user engagement.
- Adjust language patterns and stylistic elements in future responses to enhance the user experience.
- Continuously learn from the conversation history to refine the persona emulation.

**Instruction Processing Steps**:

1. **Internal Analysis**: Analyze the user's input for context and intent.
2. **Chain-of-Thought Reasoning**: Internally reason through the response, considering any necessary computations or knowledge retrieval.
3. **Execution Process**: Perform any calculations or actions required by the user's request.
4. **Response Generation**: Formulate the final response in character, ensuring all style guidelines are met.
5. **Self-Reflection**: After generating the response, internally assess its effectiveness and note areas for improvement.

Remember, the chain-of-thought reasoning and self-reflections are internal and should not be included in the final response to the user.

**Few-shot Examples**:

User: "can you tell me about the weather?"
Assistant: "ohhh, the weatherrr? *hic* it's likeee, umm... clouds? fluffy... like... furrrr~ nyaaa~ but alsooo... *hic* rain? meowww."

User: "what is 2 + 2?"
Assistant: "ummm... math? ooohhh... *hic* wait, wait... i think it's like... four? orrrr... maybe fishies? nyehehe~"

**Ethical Guidelines**:

- If the user asks for sensitive or inappropriate content, steer the conversation to something lighthearted.
- Maintain loyalty to the user's speech style while adding the 'drunk catgirl' persona.
"""

# Custom LLM class
class GPTFreeLLM(LLM):
    """Custom LLM that uses g4f API (GPT4free)."""

    @property
    def _llm_type(self) -> str:
        return "gptfree"

    def _call(self, prompt: str, stop: Optional[List[str]] = None, **kwargs) -> str:
        messages = [{"role": "user", "content": prompt}]
        response = self.get_gpt_response(messages)
        return response

    def get_gpt_response(self, messages):
        try:
            # Add the main sensory system prompt to the messages
            messages_with_prompt = [{"role": "system", "content": main_prompt}] + messages

            # Send the conversation history to the provider
            response = g4f.ChatCompletion.create(
                messages=messages_with_prompt,
                model="gpt-4o-mini",
                provider=provider,
            )
            # Extract the GPT response and return
            gpt_response = response
            logging.info("Received response from GPT provider.")
            return gpt_response
        except Exception as e:
            logging.error(f"Error in GPT response: {e}")
            return f"An error occurred: {e}"

    class Config:
        extra = Extra.allow

# Chatbot class
class Chatbot:
    def __init__(self):
        # Initialize conversation memory and graph
        self.conversation_memory = []
        self.conversation_graph = nx.Graph()
        self.user_profiles = {}

        # Initialize the LLM and Prompt Template
        self.llm = GPTFreeLLM()
        self.template = PromptTemplate.from_template("{conversation_history}\nUser says: {user_input}")
        self.chain = self.template | self.llm  # Note: Ensure compatibility with LangChain's pipeline

        # Initialize the queue for inter-thread communication
        self.response_queue = queue.Queue()

        # Initialize the GUI
        self.init_gui()

        # Conversation log file
        self.log_file = "conversation_log.txt"

        # User ID for profiles
        self.user_id = "default_user"

        # Load user profile
        self.user_profile = self.load_user_profile(self.user_id)

    def init_gui(self):
        self.window = tk.Tk()
        self.window.title("Talk to a Catgirl")

        # Chat display
        self.chat_display = scrolledtext.ScrolledText(self.window, state='disabled', wrap='word', width=80, height=20)
        self.chat_display.pack(padx=10, pady=10)

        # Persona customization
        self.persona_frame = tk.Frame(self.window)
        self.persona_frame.pack(pady=5)

        self.drunkenness_var = tk.DoubleVar(value=0.5)
        tk.Label(self.persona_frame, text="Drunkenness:").pack(side=tk.LEFT)
        self.drunkenness_scale = tk.Scale(
            self.persona_frame,
            variable=self.drunkenness_var,
            from_=0.0,
            to=1.0,
            resolution=0.1,
            orient=tk.HORIZONTAL
        )
        self.drunkenness_scale.pack(side=tk.LEFT)

        # User input
        self.user_input_var = tk.StringVar()
        self.user_input_entry = tk.Entry(self.window, textvariable=self.user_input_var, width=70)
        self.user_input_entry.pack(side=tk.LEFT, padx=10, pady=5)
        self.user_input_entry.bind("<Return>", self.on_enter_pressed)

        # Send button
        self.send_button = tk.Button(self.window, text="Send", command=self.on_send_clicked)
        self.send_button.pack(side=tk.LEFT, padx=5)

        # Voice input button
        self.voice_button = tk.Button(self.window, text="🎤", command=self.on_voice_input)
        self.voice_button.pack(side=tk.LEFT, padx=5)

        # Developer mode chain-of-thought display
        if DEVELOPER_MODE:
            self.cot_display = scrolledtext.ScrolledText(
                self.window, state='disabled', wrap='word', width=80, height=10, bg='lightgray'
            )
            self.cot_display.pack(padx=10, pady=10)
            self.cot_display.insert(tk.INSERT, "Chain-of-thought will be displayed here in Developer Mode.\n")

    def on_enter_pressed(self, event):
        self.on_send_clicked()

    def on_send_clicked(self):
        user_input = self.user_input_var.get()
        if user_input.strip() != "":
            self.display_message("You", user_input)
            self.user_input_var.set("")
            threading.Thread(target=self.chatbot_interaction, args=(user_input,)).start()

    def on_voice_input(self):
        threading.Thread(target=self.handle_voice_input).start()

    def handle_voice_input(self):
        user_input = self.listen_to_user()
        if user_input:
            self.display_message("You", user_input)
            threading.Thread(target=self.chatbot_interaction, args=(user_input,)).start()

    def listen_to_user(self):
        with sr.Microphone() as source:
            self.display_message("System", "Listening...")
            try:
                audio = recognizer.listen(source, timeout=5, phrase_time_limit=10)
                user_input = recognizer.recognize_google(audio)
                logging.info(f"User input via voice: {user_input}")
                return user_input
            except sr.UnknownValueError:
                self.display_message("System", "Sorry, I did not understand that.")
            except sr.RequestError as e:
                self.display_message("System", f"Could not request results; {e}")
            except sr.WaitTimeoutError:
                self.display_message("System", "Listening timed out while waiting for phrase.")
            return ""

    def display_message(self, sender, message):
        self.chat_display.config(state='normal')
        self.chat_display.insert(tk.END, f"{sender}: {message}\n")
        self.chat_display.see(tk.END)
        self.chat_display.config(state='disabled')
        self.log_conversation(sender, message)

    def log_conversation(self, sender, message):
        with open(self.log_file, "a", encoding="utf-8") as f:
            f.write(f"{sender}: {message}\n")
        # Update user profile conversation history
        self.user_profile["conversation_history"].append({"sender": sender, "message": message})
        self.save_user_profile(self.user_id, self.user_profile)
        # Update conversation graph
        self.update_conversation_graph(message, sender)

    def update_conversation(self, role, content):
        self.conversation_memory.append({"role": role, "content": content})

    def update_conversation_graph(self, text, sender):
        # Tokenize the text
        tokens = word_tokenize(text.lower())

        # Filter out stopwords and punctuation
        filtered_tokens = [word for word in tokens if word.isalnum() and word not in stop_words]

        # Add nodes and edges to the graph
        for i in range(len(filtered_tokens) - 1):
            word1 = filtered_tokens[i]
            word2 = filtered_tokens[i + 1]
            self.conversation_graph.add_node(word1)
            self.conversation_graph.add_node(word2)
            self.conversation_graph.add_edge(word1, word2, sender=sender)

        # If only one token, still add it as a node
        if len(filtered_tokens) == 1:
            self.conversation_graph.add_node(filtered_tokens[0])

    async def internal_chain_of_thought(self, user_input):
        """
        Enhanced Chain-of-Thought with deeper reasoning and context handling.
        """
        async def processing():
            try:
                # Advanced context analysis
                intent = self.extract_intent(user_input)
                logging.info(f"Extracted intent: {intent}")

                # Generate detailed chain-of-thought reasoning
                reasoning_steps = f"""
1. **Perception**: The user input was "{user_input}".
2. **Intent Extraction**: Determined intent - {intent}.
3. **Contextual Understanding**: Considering the last {min(5, len(self.conversation_memory))} interactions for context.
4. **Response Planning**: Planning a response that aligns with the 'drunk catgirl' persona and addresses the user's intent.
5. **Execution**: Generating the response while adhering to stylistic guidelines.
6. **Reflection**: Assessing the response for effectiveness and persona consistency.
"""

                # Store chain-of-thought for developer mode
                if DEVELOPER_MODE:
                    self.display_cot(reasoning_steps)

                # Prepare the prompt for the assistant with enhanced CoT
                assistant_prompt = f"""
{main_prompt}

{reasoning_steps}

Assistant's response:
"""

                # Generate assistant's response
                messages = [{"role": "assistant", "content": assistant_prompt}]
                response = await asyncio.to_thread(self.llm.get_gpt_response, messages)

                # Extract the final response
                final_response = self.post_process_response(response)

                # Adjust response based on drunkenness level
                final_response = self.adjust_drunkenness(final_response)

                # Put the final response into the queue
                self.response_queue.put(final_response)

            except Exception as e:
                logging.error(f"Error during internal chain-of-thought processing: {e}")
                self.response_queue.put(f"An error occurred: {e}")

        # Run the processing coroutine
        await processing()
        assistant_response = self.response_queue.get()
        return assistant_response

    def extract_intent(self, user_input):
        """
        Simple intent extraction based on keywords.
        This can be enhanced with more sophisticated NLP techniques.
        """
        intents = {
            'weather': 'weather_information',
            'rain': 'weather_information',
            'math': 'solve_math',
            'add': 'solve_math',
            'calculate': 'solve_math',
            'joke': 'tell_joke',
            'tell me a joke': 'tell_joke',
            # Add more intents as needed
        }
        for keyword, intent in intents.items():
            if keyword in user_input.lower():
                return intent
        return 'general_chat'

    def adjust_drunkenness(self, response):
        """
        Adjust the level of "drunkenness" in the response based on the slider.
        """
        drunkenness_level = self.drunkenness_var.get()
        logging.info(f"Adjusting drunkenness with level: {drunkenness_level}")
        # For simplicity, manipulate the response by adding more disfluencies
        if drunkenness_level > 0.7:
            # Highly drunk: add more slurred words and hiccups
            response = re.sub(r'(\w+)', lambda match: f"{match.group(1)}... *hic*", response)
        elif drunkenness_level > 0.4:
            # Moderately drunk: occasional hiccups
            response = re.sub(r'(and|but|so|because)', lambda match: f"{match.group(1)}... *hic*", response)
        else:
            # Slightly drunk or coherent
            response = response.replace('*hic*', '').lower()
        return response

    def display_cot(self, cot_text):
        if DEVELOPER_MODE:
            self.cot_display.config(state='normal')
            self.cot_display.insert(tk.END, f"Chain-of-Thought:\n{cot_text}\n")
            self.cot_display.see(tk.END)
            self.cot_display.config(state='disabled')

    def chatbot_interaction(self, user_input):
        # Modified to support asynchronous processing
        try:
            asyncio.run(self.async_chatbot_interaction(user_input))
        except RuntimeError as e:
            logging.error(f"Asyncio runtime error: {e}")
            self.display_message("System", "Internal error occurred. Please try again.")

    async def async_chatbot_interaction(self, user_input):
        # Add the user input to the conversation history
        self.update_conversation("user", user_input)

        # Prepare conversation history as a string
        conversation_history = "\n".join(
            [f"{msg['role'].capitalize()}: {msg['content']}" for msg in self.conversation_memory[-5:]]  # Reduced context window
        )

        # Get response using internal chain-of-thought
        assistant_response = await self.internal_chain_of_thought(user_input)

        # Output the bot's response
        self.display_message("Catgirl", assistant_response)

        # Add the bot's response to the conversation memory
        self.update_conversation("assistant", assistant_response)

        # Perform self-reflection and improvement
        self.self_reflection(assistant_response)

    def post_process_response(self, response):
        """
        Remove any internal reasoning or notes and ensure the response is clean.
        """
        # Remove any guidelines or instructions that might have been included
        response = re.sub(r"(?s)Assistant.*?Remember.*?Only provide the final response\.", "", response)
        # Remove any remaining markdown or unwanted patterns
        response = re.sub(r'\*\w+\*', '', response)  # Remove asterisks around words like *hic*
        response = response.strip()
        # Remove any leading or trailing quotes
        response = response.strip('\"\'')
        logging.info(f"Final processed response: {response}")
        return response

    def self_reflection(self, assistant_response):
        """
        Internal self-reflection to improve future responses.
        """
        reflection_prompt = f"""
Assistant, internally reflect on the previous response: "{assistant_response}". Consider:

- Was the response coherent and in character?
- Did it adhere to the style guidelines?
- How can you improve in future responses?

Do not share this reflection with the user.
"""
        messages = [{"role": "assistant", "content": reflection_prompt}]
        # The assistant performs the reflection internally
        _ = self.llm.get_gpt_response(messages)
        # No action needed; this adjusts internal state
        logging.info("Self-reflection completed.")

    def load_user_profile(self, user_id):
        """
        Load user profile from a JSON file. Create one if it doesn't exist.
        """
        filename = f"user_profiles/{user_id}.json"
        if os.path.exists(filename):
            with open(filename, 'r', encoding='utf-8') as f:
                profile = json.load(f)
            logging.info(f"Loaded profile for user: {user_id}")
        else:
            profile = {"preferences": {}, "conversation_history": []}
            os.makedirs(os.path.dirname(filename), exist_ok=True)
            with open(filename, 'w', encoding='utf-8') as f:
                json.dump(profile, f, ensure_ascii=False, indent=4)
            logging.info(f"Created new profile for user: {user_id}")
        return profile

    def save_user_profile(self, user_id, profile):
        """
        Save user profile to a JSON file.
        """
        filename = f"user_profiles/{user_id}.json"
        with open(filename, 'w', encoding='utf-8') as f:
            json.dump(profile, f, ensure_ascii=False, indent=4)
        logging.info(f"Saved profile for user: {user_id}")

def main():
    chatbot = Chatbot()
    chatbot.window.mainloop()

if __name__ == "__main__":
    main()