Further enhance and add in an advanced chain-of-thought processing which'll process the user's message in another window and think for awhile about the message with some advanced layered protocols before outputting the final response.

```
import g4f
from langchain.prompts import PromptTemplate
import argparse
import readline  # For command-line GUI enhancements
import networkx as nx  # For graph management
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

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

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

# Memory to store past conversations
conversation_memory = []

# Graph to store nodes and connections for Chain Mapping
conversation_graph = nx.Graph()

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

# 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.
"""

# Function to get a response from GPT
def get_gpt_response(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
        return gpt_response
    except Exception as e:
        return f"An error occurred: {e}"

# 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 = get_gpt_response(messages)
        return response

    class Config:
        extra = Extra.allow

# Create an instance of the custom LLM
llm = GPTFreeLLM()

# Incorporating Langchain for advanced chatbot logic
template = PromptTemplate.from_template("{conversation_history}\nUser says: {user_input}")
# Use the new chain syntax
chain = template | llm

# Function to maintain conversation history and context
def update_conversation(role, content):
    conversation_memory.append({"role": role, "content": content})

# Function to update the conversation graph
def update_conversation_graph(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]
        conversation_graph.add_node(word1)
        conversation_graph.add_node(word2)
        conversation_graph.add_edge(word1, word2, sender=sender)

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

# Advanced Command-Line GUI Functionality
def command_line_gui():
    print("Welcome to 'Talk to a Catgirl'!")
    print("Type 'quit' to exit.\n")
    while True:
        try:
            user_input = input("You: ")
            if user_input.lower() == "quit":
                print("Goodbye!")
                break
            chatbot_interaction(user_input)
        except KeyboardInterrupt:
            print("\nGoodbye!")
            break

# Function to perform internal chain-of-thought reasoning
def internal_chain_of_thought(user_input):
    # Internal reasoning (not shown to the user)
    reasoning_prompt = f"""
Assistant, perform the following steps internally:

1. **Perception**: Understand that the user said "{user_input}".
2. **Analysis**: Determine the intent and recall relevant information from the conversation history.
3. **Planning**: Decide how to respond appropriately in character.
4. **Execution**: Generate the response in the 'drunk catgirl' persona.
5. **Reflection**: Assess the response's effectiveness and note adjustments for future interactions.

Remember, do not share this reasoning with the user. Only provide the final response.
"""
    # Get the internal reasoning and final response
    messages = [
        {"role": "system", "content": main_prompt},
        {"role": "assistant", "content": reasoning_prompt}
    ]
    response = get_gpt_response(messages)
    # Extract the final response (remove internal reasoning if any)
    final_response = post_process_response(response)
    return final_response

# Function to clean up the assistant's response
def post_process_response(response):
    # Remove any internal reasoning or notes
    response = re.sub(r"(?s)Assistant.*?Remember.*?Only provide the final response\.", "", response)
    # Ensure the response is in first person and appropriate
    response = response.strip()
    # Remove any leading or trailing quotes
    response = response.strip('\"\'')
    return response

# Function to perform self-reflection and improvement
def self_reflection():
    # Internal self-reflection (not shown to the user)
    reflection_prompt = """
Assistant, internally reflect on the previous 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": "system", "content": main_prompt},
        {"role": "assistant", "content": reflection_prompt}
    ]
    # The assistant performs the reflection internally
    _ = get_gpt_response(messages)
    # No action needed; this adjusts internal state

# Function to apply Chain Mapping and generate response
def chatbot_interaction(user_input):
    # Add the user input to the conversation history
    update_conversation("user", user_input)
    # Update the conversation graph with user's input
    update_conversation_graph(user_input, sender="user")

    # Prepare conversation history as a string
    conversation_history = "\n".join(
        [f"{msg['role'].capitalize()}: {msg['content']}" for msg in conversation_memory[-6:]]  # Limit history
    )

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

    # Output the bot's response
    print(f"Catgirl: {assistant_response}")

    # Add the bot's response to the conversation memory
    update_conversation("assistant", assistant_response)
    # Update the conversation graph with assistant's response
    update_conversation_graph(assistant_response, sender="assistant")

    # Perform self-reflection and improvement
    self_reflection()

# Run the command-line GUI
if __name__ == "__main__":
    command_line_gui()