import logging
from typing import List, Dict, Any

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


def decompose_task(task: str) -> List[str]:
    """
    Decomposes a complex task into smaller, manageable sub-tasks.

    Args:
        task: The complex task to be decomposed.

    Returns:
        A list of sub-tasks.
    """
    try:
        # Placeholder implementation
        sub_tasks = [f"Sub-task 1 of {task}", f"Sub-task 2 of {task}"]
        logging.info(f"Task '{task}' decomposed into: {sub_tasks}")
        return sub_tasks
    except Exception as e:
        logging.error(f"Error decomposing task: {e}")
        return []


def select_tool(task: str, available_tools: List[str]) -> str:
    """
    Selects the most appropriate tool for a given task from a list of available tools.

    Args:
        task: The task to be performed.
        available_tools: A list of available tools.

    Returns:
        The name of the selected tool.
    """
    try:
        # Placeholder implementation
        if not available_tools:
            raise ValueError("No available tools provided.")

        selected_tool = available_tools[0]  # Select the first tool for now
        logging.info(f"Selected tool '{selected_tool}' for task '{task}'")
        return selected_tool
    except ValueError as ve:
        logging.error(f"Value Error selecting tool: {ve}")
        return None
    except Exception as e:
        logging.error(f"Error selecting tool: {e}")
        return None


def reflect_on_performance(task: str, result: Any) -> Dict[str, Any]:
    """
    Reflects on the performance of a task and provides insights for improvement.

    Args:
        task: The task that was performed.
        result: The result of the task.

    Returns:
        A dictionary containing insights and recommendations.
    """
    try:
        # Placeholder implementation
        insights = {
            "task": task,
            "result": result,
            "insights": "Placeholder insights. Further analysis needed.",
            "recommendations": "Placeholder recommendations. Further planning needed."
        }
        logging.info(f"Reflected on performance for task '{task}': {insights}")
        return insights
    except Exception as e:
        logging.error(f"Error reflecting on performance: {e}")
        return {}


if __name__ == '__main__':
    # Example usage
    task = "Implement self-modeling system"
    sub_tasks = decompose_task(task)
    available_tools = ["Python", "Shell", "Internet Search"]
    selected_tool = select_tool(sub_tasks[0], available_tools)

    if selected_tool:
        result = "Task completed using " + selected_tool  # Simulate task completion
        reflection = reflect_on_performance(task, result)
        print(reflection)
    else:
        print("No tool selected.")