Further enhance and add in streaming with an advanced faster response time and an advanced depth system.

```
import g4f
import random
from g4f.Provider import (
    Blackbox
)
import logging
import json
import os
import re

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')


class MultiLevelCoT:
    def __init__(self, provider, generation_params):
        self.provider = provider
        self.generation_params = generation_params

    def initialize_state(self, input_text):
        """Initialize the reasoning state based on the user input."""
        return {"input": input_text, "steps": [], "current_state": "INIT"}

    def is_conclusion_reached(self, current_state):
        """Determine if the reasoning has reached a conclusion."""
        # For simplicity, we can set a maximum number of steps
        return len(current_state["steps"]) >= 5  # Example threshold

    def reason_next_step(self, current_state):
        """Generate the next reasoning step."""
        prompt = (
            f"Based on the following context, generate the next reasoning step logically.\n"
            f"Context: {current_state['input']}\n"
            f"Current Steps: {chr(10).join([f'{i+1}. {step}' for i, step in enumerate(current_state['steps'])])}\n"
            f"Next Step:"
        )
        try:
            response = g4f.ChatCompletion.create(
                model=self.generation_params["model"],
                provider=self.provider,
                messages=[
                    {"role": "system", "content": self.generation_params["system_prompt"]},
                    {"role": "user", "content": prompt}
                ],
                # Removed repetition_penalty as it's likely unsupported
                temperature=self.generation_params["temperature"],
                max_tokens=150,
                stream=False
            )
            next_step = response.strip()
            logging.debug(f"Generated next step: {next_step}")
            return next_step
        except Exception as e:
            logging.error(f"Error generating next reasoning step: {str(e)}")
            return f"Error generating next reasoning step: {str(e)}"

    def extract_float(self, text):
        """Extract the first floating point number from the text."""
        try:
            match = re.search(r"([0-9]*\.[0-9]+)|([0-9]+)", text)
            if match:
                return float(match.group())
            else:
                logging.warning(f"No float found in text: {text}")
                return 0.0
        except:
            logging.warning(f"Failed to extract float from text: {text}")
            return 0.0

    def assess_step_confidence(self, step):
        """Assess confidence in the generated reasoning step."""
        prompt = (
            f"Evaluate the confidence level of the following reasoning step on a scale of 0 to 1.\n\n"
            f"Step: {step}\n"
            f"Confidence Level: "
        )
        try:
            response = g4f.ChatCompletion.create(
                model=self.generation_params["model"],
                provider=self.provider,
                messages=[
                    {"role": "system", "content": self.generation_params["system_prompt"]},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.0,  # Deterministic response
                max_tokens=10,
                stream=False
            )
            confidence = self.extract_float(response.strip())
            confidence = max(0.0, min(1.0, confidence))  # Clamp between 0 and 1
            logging.debug(f"Assessed confidence: {confidence} for step: {step}")
            return confidence
        except Exception as e:
            logging.error(f"Error assessing confidence: {str(e)}")
            return 0.0

    def detect_cognitive_bias(self, step):
        """Detect cognitive bias in a reasoning step."""
        prompt = (
            f"Analyze the following reasoning step for potential cognitive biases and rate them on a scale of 0 to 1.\n\n"
            f"Step: {step}\n"
            f"Bias Score: "
        )
        try:
            response = g4f.ChatCompletion.create(
                model=self.generation_params["model"],
                provider=self.provider,
                messages=[
                    {"role": "system", "content": self.generation_params["system_prompt"]},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.0,
                max_tokens=10,
                stream=False
            )
            bias_score = self.extract_float(response.strip())
            bias_score = max(0.0, min(1.0, bias_score))
            logging.debug(f"Detected bias: {bias_score} for step: {step}")
            return bias_score
        except Exception as e:
            logging.error(f"Error detecting cognitive bias: {str(e)}")
            return 0.0

    def generate_alternatives(self, step):
        """Generate alternative reasoning paths for a given step."""
        prompt = (
            f"Provide two alternative reasoning steps that address the same point as the following step.\n\n"
            f"Step: {step}\n"
            f"Alternatives:\n1. "
        )
        try:
            response = g4f.ChatCompletion.create(
                model=self.generation_params["model"],
                provider=self.provider,
                messages=[
                    {"role": "system", "content": self.generation_params["system_prompt"]},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.7,
                max_tokens=150,
                stream=False
            )
            # Split the response into alternatives
            alternatives = re.split(r'\n*2\.\s*', response.strip())
            alternatives = [alt.strip().lstrip('1.').rstrip('2.').strip() for alt in alternatives if alt.strip()]
            logging.debug(f"Generated alternatives for step '{step}': {alternatives}")
            return alternatives
        except Exception as e:
            logging.error(f"Error generating alternatives: {str(e)}")
            return []

    def verify_consistency(self, step):
        """Verify the logical consistency of a reasoning step."""
        prompt = (
            f"Determine the logical consistency of the following reasoning step on a scale of 0 to 1.\n\n"
            f"Step: {step}\n"
            f"Consistency Score: "
        )
        try:
            response = g4f.ChatCompletion.create(
                model=self.generation_params["model"],
                provider=self.provider,
                messages=[
                    {"role": "system", "content": self.generation_params["system_prompt"]},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.0,
                max_tokens=10,
                stream=False
            )
            consistency = self.extract_float(response.strip())
            consistency = max(0.0, min(1.0, consistency))
            logging.debug(f"Verified consistency: {consistency} for step: {step}")
            return consistency
        except Exception as e:
            logging.error(f"Error verifying consistency: {str(e)}")
            return 0.0

    def find_better_alternative(self, alternatives):
        """Select a better alternative from the provided alternatives."""
        if not alternatives:
            logging.warning("No alternatives provided to find a better alternative.")
            return None
        prompt = (
            f"From the following alternatives, choose the one that is more logically consistent and less biased.\n\n"
            f"Alternatives:\n1. {alternatives[0]}\n2. {alternatives[1]}\n\n"
            f"Recommended Alternative: "
        )
        try:
            response = g4f.ChatCompletion.create(
                model=self.generation_params["model"],
                provider=self.provider,
                messages=[
                    {"role": "system", "content": self.generation_params["system_prompt"]},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.0,
                max_tokens=150,
                stream=False
            )
            recommended = response.strip()
            logging.debug(f"AI recommendation for better alternative: {recommended}")

            # Determine which alternative was recommended
            if recommended.lower().startswith('1.'):
                return alternatives[0]
            elif recommended.lower().startswith('2.'):
                return alternatives[1]
            else:
                # Attempt to find which alternative is mentioned
                for alt in alternatives:
                    if alt.lower() in recommended.lower():
                        return alt
                logging.warning("AI did not clearly recommend an alternative. Defaulting to the first one.")
                return alternatives[0]  # Default to first alternative if unclear
        except Exception as e:
            logging.error(f"Error finding better alternative: {str(e)}")
            return alternatives[0]  # Default to first alternative on error

    def calculate_overall_confidence(self, final_chain):
        """Calculate the overall confidence score of the final reasoning chain."""
        if not final_chain:
            return 0.0
        confidence_scores = [step['confidence'] for step in final_chain]
        average_confidence = sum(confidence_scores) / len(confidence_scores)
        logging.debug(f"Calculated overall confidence: {average_confidence}")
        return average_confidence

    def generate_primary_chain(self, input_text):
        """Generate the primary chain-of-thought reasoning."""
        steps = []
        state = self.initialize_state(input_text)

        while not self.is_conclusion_reached(state):
            next_step = self.reason_next_step(state)
            if next_step.startswith("Error"):
                break
            confidence = self.assess_step_confidence(next_step)
            steps.append({"step": next_step, "confidence": confidence})
            state["steps"].append(next_step)
            state = self.update_state(state, next_step)

        return steps

    def meta_cognitive_analysis(self, reasoning_chain):
        """Perform meta-cognitive analysis on the reasoning chain."""
        meta_insights = {}

        for item in reasoning_chain:
            step = item["step"]
            confidence = item["confidence"]
            bias_score = self.detect_cognitive_bias(step)
            alternatives = self.generate_alternatives(step)
            consistency = self.verify_consistency(step)

            meta_insights[step] = {
                'bias': bias_score,
                'alternatives': alternatives,
                'consistency': consistency
            }

        return meta_insights

    def self_correct(self, reasoning_chain, meta_insights):
        """Self-correct the reasoning chain based on meta-cognitive insights."""
        corrected_chain = []

        for item in reasoning_chain:
            step = item["step"]
            confidence = item["confidence"]
            insights = meta_insights.get(step, {})
            consistency = insights.get('consistency', 1.0)

            if consistency < 0.7 and insights.get('alternatives'):
                better_step = self.find_better_alternative(insights['alternatives'])
                if better_step:
                    corrected_chain.append({"step": better_step, "confidence": confidence})
                else:
                    corrected_chain.append(item)
            else:
                corrected_chain.append(item)

        return corrected_chain

    def update_state(self, state, step):
        """Update the reasoning state after adding a new step."""
        state["current_state"] = step  # For potential future use
        return state

    def process_with_cot(self, input_text):
        """Process the input text with Multi-Level CoT and return the final reasoning."""
        primary_chain = self.generate_primary_chain(input_text)
        if not primary_chain:
            logging.warning("Primary reasoning chain is empty.")
            return {
                'final_reasoning': [],
                'meta_insights': {},
                'confidence': 0.0
            }

        meta_analysis = self.meta_cognitive_analysis(primary_chain)
        final_chain = self.self_correct(primary_chain, meta_analysis)
        overall_confidence = self.calculate_overall_confidence(final_chain)

        return {
            'final_reasoning': final_chain,
            'meta_insights': meta_analysis,
            'confidence': overall_confidence
        }


class AdvancedChatbot:
    def __init__(self):
        # Initialize available providers
        self.providers = [
            Blackbox
        ]

        # Chat history to maintain context
        self.chat_history = []

        # Default system prompt to define chatbot behavior with Multi-Level CoT and other enhancements
        self.default_system_prompt = """You are a highly intelligent and helpful AI assistant.
You utilize a Multi-Level Chain-of-Thought (CoT) to reason through each answer step-by-step.
After formulating a response, you engage in a Meta-Cognitive review to ensure the accuracy, clarity, and completeness of your answer before presenting it to the user.
Additionally, you minimize repetition in your responses to maintain engagement.
Your performance improves over time based on user feedback to better align with user preferences."""
        self.system_prompt = self.default_system_prompt

        # Reinforcement Learning: Initialize feedback storage
        self.feedback_file = 'feedback.json'
        self.feedback_data = self.load_feedback()

        # Parameters for response generation
        self.generation_params = {
            "model": "gpt-3.5-turbo",
            # Removed repetition_penalty as it's likely unsupported
            "frequency_penalty": 0.5,  # Example of a supported repetition control parameter
            "presence_penalty": 0.0,
            "temperature": 0.7,          # Controls randomness; lower is more deterministic
            "max_tokens": 150,           # Adjust based on desired response length
            "system_prompt": self.system_prompt
        }

    def load_feedback(self):
        """Load feedback data from a JSON file."""
        if os.path.exists(self.feedback_file):
            with open(self.feedback_file, 'r') as f:
                try:
                    data = json.load(f)
                    logging.info("Feedback data loaded successfully.")
                    return data
                except json.JSONDecodeError:
                    logging.warning("Feedback file is corrupted. Starting with empty feedback.")
                    return {}
        else:
            logging.info("No existing feedback data found. Starting fresh.")
            return {}

    def save_feedback(self):
        """Save feedback data to a JSON file."""
        try:
            with open(self.feedback_file, 'w') as f:
                json.dump(self.feedback_data, f, indent=4)
                logging.info("Feedback data saved.")
        except Exception as e:
            logging.error(f"Error saving feedback data: {str(e)}")

    def get_working_provider(self):
        """Try different providers until finding one that works."""
        random.shuffle(self.providers)  # Randomize provider selection

        for provider in self.providers:
            try:
                response = g4f.ChatCompletion.create(
                    model=self.generation_params["model"],
                    provider=provider,
                    messages=[{"role": "user", "content": "Hi"}],
                    frequency_penalty=self.generation_params["frequency_penalty"],
                    presence_penalty=self.generation_params["presence_penalty"],
                    temperature=self.generation_params["temperature"],
                    max_tokens=50,
                    stream=False
                )
                logging.info(f"Using provider: {provider.__name__}")
                return provider
            except Exception as e:
                logging.warning(f"Provider {provider.__name__} failed with error: {str(e)}")
                continue
        return None

    def collect_feedback(self, user_input, response):
        """Collect user feedback on the response."""
        print("\n--- Feedback ---")
        feedback = input("Was this response helpful? (yes/no): ").strip().lower()
        while feedback not in ['yes', 'no']:
            feedback = input("Please enter 'yes' or 'no': ").strip().lower()

        # Update feedback data
        key = user_input[:50].lower()  # Use a snippet of user input as key, case-insensitive
        if key in self.feedback_data:
            self.feedback_data[key]['counts']['total'] += 1
            if feedback == 'yes':
                self.feedback_data[key]['counts']['positive'] += 1
            else:
                self.feedback_data[key]['counts']['negative'] += 1
        else:
            self.feedback_data[key] = {
                'input': user_input,
                'response': response,
                'counts': {
                    'total': 1,
                    'positive': 1 if feedback == 'yes' else 0,
                    'negative': 1 if feedback == 'no' else 0
                }
            }

        # Save feedback
        self.save_feedback()

        # Adjust behavior based on feedback
        self.adjust_behavior(feedback)

    def adjust_behavior(self, feedback):
        """Adjust chatbot behavior based on feedback."""
        if feedback == 'no':
            # Increase frequency_penalty to further reduce repetition
            self.generation_params["frequency_penalty"] = min(self.generation_params["frequency_penalty"] + 0.1, 2.0)  # Cap at 2.0
            logging.info(f"Frequency penalty increased to {self.generation_params['frequency_penalty']}")
        elif feedback == 'yes':
            # Decrease frequency_penalty if it's above a minimum threshold
            if self.generation_params["frequency_penalty"] > 0.0:
                self.generation_params["frequency_penalty"] = max(self.generation_params["frequency_penalty"] - 0.1, 0.0)
                logging.info(f"Frequency penalty decreased to {self.generation_params['frequency_penalty']}")

    def inject_custom_prompt(self, custom_prompt):
        """Inject a custom prompt into the system prompt."""
        self.system_prompt = custom_prompt
        self.generation_params["system_prompt"] = self.system_prompt
        logging.info("Custom prompt injected successfully.")

    def chat(self, user_input):
        """Main chat function with Multi-Level CoT, Repetition Control, and Reinforcement Learning."""
        if not user_input.strip():
            return "Please enter a valid message."

        # Check if the user wants to inject a custom prompt
        if user_input.lower().startswith("/setprompt"):
            custom_prompt = user_input[len("/setprompt"):].strip()
            if custom_prompt:
                self.inject_custom_prompt(custom_prompt)
                return "Custom prompt has been set successfully."
            else:
                return "Please provide a valid custom prompt after the /setprompt command."

        # Get working provider
        provider = self.get_working_provider()
        if not provider:
            return "Sorry, no available providers at the moment."

        # Initialize Multi-Level CoT
        multi_cot = MultiLevelCoT(provider, self.generation_params)

        # Process input with Multi-Level CoT
        cot_result = multi_cot.process_with_cot(user_input)

        if not cot_result['final_reasoning']:
            return "I'm sorry, but I encountered an error while processing your request."

        # Construct the final response by summarizing the reasoning steps
        # Instead of showing all steps, we can ask the AI to provide a concise summary
        reasoning_steps = cot_result['final_reasoning']
        concatenated_steps = " ".join([f"{i+1}. {step['step']}" for i, step in enumerate(reasoning_steps)])

        summary_prompt = (
            f"Based on the following reasoning steps, provide a concise and clear answer to the user's input.\n\n"
            f"Reasoning Steps:\n{concatenated_steps}\n\n"
            f"Final Answer:"
        )

        try:
            summary_response = g4f.ChatCompletion.create(
                model=self.generation_params["model"],
                provider=provider,
                messages=[
                    {"role": "system", "content": self.generation_params["system_prompt"]},
                    {"role": "user", "content": summary_prompt}
                ],
                frequency_penalty=self.generation_params["frequency_penalty"],
                presence_penalty=self.generation_params["presence_penalty"],
                temperature=0.7,
                max_tokens=150,
                stream=False
            )
            final_answer = summary_response.strip()
            logging.debug(f"Generated final answer: {final_answer}")
        except Exception as e:
            logging.error(f"Error generating final answer: {str(e)}")
            final_answer = "I'm sorry, but I encountered an error while generating the final answer."

        overall_confidence = round(cot_result['confidence'], 2)

        # Update chat history
        self.chat_history.append({
            "user": user_input,
            "assistant": final_answer
        })

        # Collect user feedback for Reinforcement Learning
        self.collect_feedback(user_input, final_answer)

        # Return the final answer with confidence level
        return f"{final_answer}\n\nConfidence Level: {overall_confidence}"


def main():
    chatbot = AdvancedChatbot()
    print("Advanced Chatbot with Multi-Level Self-Reflection CoT, Repetition Control, and Reinforcement Learning initialized.")
    print("You can inject a custom prompt using the command: /setprompt Your custom prompt here")
    print("Type 'quit' to exit.")
    print("-" * 100)

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ['quit', 'exit', 'bye']:
            print("Chatbot: Goodbye!")
            break

        response = chatbot.chat(user_input)
        print("Chatbot:", response)
        print("-" * 100)


if __name__ == "__main__":
    try:
        main()
    except KeyboardInterrupt:
        print("\nChatbot: Session terminated by user.")
    except Exception as e:
        print(f"\nAn error occurred: {str(e)}")