import logging
import os
from typing import Dict, Optional

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


def select_model(task_description: str) -> str:
    """
    Selects the optimal model for a given task based on the task description.

    Args:
        task_description: A string describing the task to be performed.

    Returns:
        The name of the selected model as a string.

    Raises:
        ValueError: If the task description is empty.
        KeyError: If no suitable model is found for the given task.
    """
    if not task_description:
        logging.error("Task description cannot be empty.")
        raise ValueError("Task description cannot be empty.")

    model_mapping: Dict[str, str] = {
        "summarization": "summarization_model_v1",
        "translation": "translation_model_v2",
        "code generation": "codegen_model_v3",
    }

    try:
        for keyword, model_name in model_mapping.items():
            if keyword in task_description.lower():
                logging.info(f"Selected model {model_name} for task: {task_description}")
                return model_name

        # Default model if no specific keyword is found
        default_model = "default_model_v1"
        logging.info(f"No specific model found, using default model: {default_model}")
        return default_model

    except KeyError as e:
        logging.error(f"No model found for the given task: {task_description}")
        raise KeyError(f"No model found for the given task: {task_description}") from e
    except Exception as e:
        logging.error(f"An unexpected error occurred: {e}")
        raise

# Example usage (for testing purposes)
if __name__ == '__main__':
    try:
        task1 = "Summarize this document."
        selected_model1 = select_model(task1)
        print(f"Task: {task1}, Selected Model: {selected_model1}")

        task2 = "Translate this sentence to French."
        selected_model2 = select_model(task2)
        print(f"Task: {task2}, Selected Model: {selected_model2}")

        task3 = "Generate Python code for a simple web server."
        selected_model3 = select_model(task3)
        print(f"Task: {task3}, Selected Model: {selected_model3}")

        task4 = "Perform some generic task."
        selected_model4 = select_model(task4)
        print(f"Task: {task4}, Selected Model: {selected_model4}")

        task5 = ""  # Empty task description
        selected_model5 = select_model(task5)
        print(f"Task: {task5}, Selected Model: {selected_model5}")

    except ValueError as ve:
        print(f"ValueError: {ve}")
    except KeyError as ke:
        print(f"KeyError: {ke}")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")

# Ensure the directory exists
os.makedirs("/mnt/e/genesis-system/core/intelligence/", exist_ok=True)

# Write the model_router to the correct location.
file_path = "/mnt/e/genesis-system/core/intelligence/model_router.py"
with open(file_path, "w") as f:
    f.write("""import logging
import os
from typing import Dict, Optional

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


def select_model(task_description: str) -> str:
    """
    Selects the optimal model for a given task based on the task description.

    Args:
        task_description: A string describing the task to be performed.

    Returns:
        The name of the selected model as a string.

    Raises:
        ValueError: If the task description is empty.
        KeyError: If no suitable model is found for the given task.
    """
    if not task_description:
        logging.error("Task description cannot be empty.")
        raise ValueError("Task description cannot be empty.")

    model_mapping: Dict[str, str] = {
        "summarization": "summarization_model_v1",
        "translation": "translation_model_v2",
        "code generation": "codegen_model_v3",
    }

    try:
        for keyword, model_name in model_mapping.items():
            if keyword in task_description.lower():
                logging.info(f"Selected model {model_name} for task: {task_description}")
                return model_name

        # Default model if no specific keyword is found
        default_model = "default_model_v1"
        logging.info(f"No specific model found, using default model: {default_model}")
        return default_model

    except KeyError as e:
        logging.error(f"No model found for the given task: {task_description}")
        raise KeyError(f"No model found for the given task: {task_description}") from e
    except Exception as e:
        logging.error(f"An unexpected error occurred: {e}")
        raise


if __name__ == '__main__':
    try:
        task1 = "Summarize this document."
        selected_model1 = select_model(task1)
        print(f"Task: {task1}, Selected Model: {selected_model1}")

        task2 = "Translate this sentence to French."
        selected_model2 = select_model(task2)
        print(f"Task: {task2}, Selected Model: {selected_model2}")

        task3 = "Generate Python code for a simple web server."
        selected_model3 = select_model(task3)
        print(f"Task: {task3}, Selected Model: {selected_model3}")

        task4 = "Perform some generic task."
        selected_model4 = select_model(task4)
        print(f"Task: {task4}, Selected Model: {selected_model4}")

        task5 = ""  # Empty task description
        selected_model5 = select_model(task5)
        print(f"Task: {task5}, Selected Model: {selected_model5}")

    except ValueError as ve:
        print(f"ValueError: {ve}")
    except KeyError as ke:
        print(f"KeyError: {ke}")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")
""")