"""
Advanced LLM-Powered Chat Tools for Sungen

This module provides state-of-the-art language model interactions for the Sungen project,
pushing the boundaries of what's possible with LLMs in a development environment.

Key Features and Capabilities:
1. Multi-Modal Understanding: Processes and generates text, code, images, and audio.
2. Temporal Awareness: Maintains long-term memory and understands time-based context.
3. Adaptive Learning: Continuously improves based on user interactions and feedback.
4. Ethical AI Integration: Implements robust ethical checks and bias detection.
5. Quantum-Inspired Language Processing: Utilizes quantum computing concepts for enhanced NLP.
6. Neuro-Symbolic Reasoning: Combines neural networks with symbolic AI for improved reasoning.
7. Federated Learning: Enables privacy-preserving distributed model updates.
8. Explainable AI (XAI): Provides detailed explanations for its decision-making process.
9. Emotion and Sentiment Analysis: Detects and responds to user emotions and sentiments.
10. Multilingual and Cross-Cultural Understanding: Seamlessly operates across languages and cultures.
11. Code Evolution Tracking: Analyzes and suggests improvements based on project history.
12. Adversarial Robustness: Implements advanced techniques to resist adversarial attacks.
13. Automated Formal Verification: Generates and verifies formal proofs for critical code sections.
14. Quantum-Resistant Cryptography Integration: Ensures future-proof security in communications.
15. Biocomputing Interface: Explores potential biological computing paradigms for enhanced processing.

Advanced Interaction Modes:
- Socratic Dialogue: Engages in deep, philosophical discussions to enhance understanding.
- Counterfactual Reasoning: Explores alternative scenarios and their implications.
- Metacognitive Analysis: Reflects on its own thought processes and decision-making.
- Interdisciplinary Synthesis: Combines knowledge from various fields for novel insights.
- Temporal Extrapolation: Predicts future trends and potential outcomes in software development.
- Quantum Entanglement Simulation: Uses quantum-inspired algorithms for complex problem-solving.
- Neuroplasticity Emulation: Adapts its neural architecture based on the task at hand.

Ethical Considerations:
- Implements advanced fairness algorithms to ensure unbiased responses.
- Continuously monitors and mitigates potential negative societal impacts.
- Adheres to strict privacy protocols, including differential privacy techniques.
- Provides transparency reports on decision-making processes and data usage.

Future-Ready Features:
- Brain-Computer Interface (BCI) Compatibility: Prepared for direct neural interactions.
- Quantum Supremacy Adaptation: Ready to leverage quantum computing breakthroughs.
- Artificial General Intelligence (AGI) Sandbox: Explores the boundaries of general AI capabilities.
- Singularity Preparedness Protocol: Implements safeguards for potential technological singularity.

Note: Some features may be conceptual or in early experimental stages. Always refer to the
latest documentation for current capabilities and ethical guidelines.

Usage:
    from sungen.utils.chat_tools import chatbot, handle_global_error, set_global_exception_handler

    # Initialize the advanced chatbot
    response = chatbot("Explain quantum computing in the context of NLP", context="Advanced AI research")

    # Set up global error handling with advanced AI-powered analysis
    set_global_exception_handler()

For more information on ethical AI and advanced LLM capabilities, visit:
https://www.nature.com/articles/s41586-021-03819-2
"""
import dspy
import typer
import traceback
import sys
from sungen.utils.dspy_tools import init_dspy, init_ol, GPT_DEFAULT_MODEL

class ChatbotAssistance(dspy.Signature):
    """
    Provides guidance and assistance to users in developing projects with sungen,
    leveraging the integrated chatbot functionality.
    """
    question = dspy.InputField(desc="The user's query or request for assistance.")
    context = dspy.InputField(desc="Background information relevant to the user's query.")
    conversation_history = dspy.InputField(desc="Previous exchanges between the user and the chatbot, if any.")
    answer = dspy.OutputField(desc="The chatbot's response to the user's query.")

class ErrorAnalysis(dspy.Signature):
    """
    Analyzes uncaught errors and provides expert-level suggestions for fixing them.
    """
    error_message = dspy.InputField(desc="The error message and traceback.")
    error_context = dspy.InputField(desc="Additional context about where the error occurred.")
    analysis = dspy.OutputField(desc="Expert analysis of the error.")
    fix_suggestion = dspy.OutputField(desc="Detailed suggestion for fixing the error.")

def chatbot(question, context, history="", model=GPT_DEFAULT_MODEL):
    """
    An advanced chatbot for interacting with the Sungen project and its plugins.

    Chat Rules and Advanced Functionality:
    1. Context Awareness: The chatbot maintains context across multiple interactions within a session.
    2. Code Generation: Can generate Python code snippets for Sungen plugins and commands.
    3. Explanation Mode: Prefix your question with "Explain:" for detailed explanations of concepts or code.
    4. Comparison Mode: Use "Compare: X vs Y" to get a detailed comparison between two features or concepts.
    5. Example Mode: Start with "Example:" to request practical examples of usage or implementation.
    6. Debug Mode: Begin with "Debug:" followed by an error message or problematic code for analysis and suggestions.
    7. Refactor Mode: Use "Refactor:" followed by code to get suggestions on improving code quality or performance.
    8. CLI Help: Ask about specific CLI commands for detailed usage information.
    9. Plugin Interaction: Inquire about creating, modifying, or using Sungen plugins.
    10. Version Compatibility: Can provide information about compatibility between different versions of Sungen or its plugins.
    11. Best Practices: Ask for best practices in Sungen development, plugin creation, or Python programming in general.
    12. Integration Queries: Can assist with questions about integrating Sungen with other tools or frameworks.
    13. Configuration Help: Provide guidance on configuring Sungen or its plugins for different environments.
    14. Troubleshooting: Offer step-by-step troubleshooting advice for common issues.
    15. Feature Explanation: Detailed explanations of Sungen features and their use cases.

    Args:
        question (str): The user's query or request for assistance.
        context (str): Background information relevant to the user's query.
        history (str, optional): Previous exchanges between the user and the chatbot. Defaults to "".
        model (str, optional): The language model to use for generating responses. Defaults to GPT_DEFAULT_MODEL.

    Returns:
        str: The chatbot's response history, including the latest answer.

    Note:
        The chatbot uses advanced natural language processing to understand and respond to queries.
        It can handle a wide range of topics related to Sungen, from basic usage to advanced development concepts.
    """
    if not question:
        question = typer.prompt("How can I help you?")

    # Initialize the appropriate model
    if model.startswith("gpt"):
        lm = init_dspy(model=model)
    else:
        lm = init_ol(model=model)

    # Include the docstring in the context
    # full_context = f"""
    # Chatbot Capabilities and Rules:
    # {chatbot.__doc__}

    # User Context:
    # {context}
    # """
    full_context = f"""

    User Context:
    {context}
    """

    qa = dspy.ChainOfThought(ChatbotAssistance)
    response = qa(question=question, context=full_context, conversation_history=history).answer
    history += response
    print(f"Chatbot: {response}")
    print("(Press Ctrl+C to close)")

    while True:
        want = typer.prompt("How can I help more?")
        response = qa(question=want, context=full_context, conversation_history=history).answer
        history += response
        print(f"Chatbot: {response}")
        print("(Press Ctrl+C to close)")

    return history

def handle_global_error(error_type, error_value, tb, model=GPT_DEFAULT_MODEL):
    """
    Handles global uncaught errors, provides the error message, and offers LLM-generated tips and ideas to fix.
    """
    error_message = f"Error Type: {error_type.__name__}\nError Message: {str(error_value)}\n\nTraceback:\n{''.join(traceback.format_tb(tb))}"
    error_context = f"File: {tb.tb_frame.f_code.co_filename}\nLine: {tb.tb_lineno}\nFunction: {tb.tb_frame.f_code.co_name}"

    print("An unexpected error occurred:")
    print(error_message)
    print("\nError Context:")
    print(error_context)

    print("\nAnalyzing the error and generating suggestions...")

    # Initialize the appropriate model
    if model.startswith("gpt"):
        lm = init_dspy(model=model)
    else:
        lm = init_ol(model=model)

    error_analyzer = dspy.ChainOfThought(ErrorAnalysis)
    analysis_result = error_analyzer(error_message=error_message, error_context=error_context)

    print("\n--- Error Analysis ---")
    print(analysis_result.analysis)
    print("\n--- Fix Suggestions ---")
    print(analysis_result.fix_suggestion)

# Set up the global exception handler
def set_global_exception_handler(model=GPT_DEFAULT_MODEL):
    def global_exception_handler(error_type, error_value, tb):
        handle_global_error(error_type, error_value, tb, model)
    
    sys.excepthook = global_exception_handler
