import logging
from typing import Optional

from modules.datamodels.datamodelUam import User
from modules.datamodels.datamodelChat import ChatWorkflow, UserInputRequest
from modules.workflows.workflowManager import WorkflowManager
from modules.services import getInterface as getServices

logger = logging.getLogger(__name__)

async def chatStart(interfaceDbChat, currentUser: User, userInput: UserInputRequest, workflowId: Optional[str] = None, workflowMode: str = "Actionplan") -> ChatWorkflow:
    """
    Starts a new chat or continues an existing one, then launches processing asynchronously.
    
    Args:
        interfaceDbChat: Chat interface instance
        currentUser: Current user
        userInput: User input request
        workflowId: Optional workflow ID to continue existing workflow
        workflowMode: "Actionplan" for traditional task planning, "React" for iterative react-style processing
    
    Example usage for React mode:
        workflow = await chatStart(interfaceDbChat, currentUser, userInput, workflowMode="React")
    """
    try:
        services = getServices(currentUser, None)
        workflowManager = WorkflowManager(services)
        workflow = await workflowManager.workflowStart(userInput, workflowId, workflowMode)
        return workflow
    except Exception as e:
        logger.error(f"Error starting chat: {str(e)}")
        raise

async def chatStop(interfaceDbChat, currentUser: User, workflowId: str) -> ChatWorkflow:
    """Stops a running chat."""
    try:
        services = getServices(currentUser, None)
        workflowManager = WorkflowManager(services)
        return await workflowManager.workflowStop(workflowId)
    except Exception as e:
        logger.error(f"Error stopping chat: {str(e)}")
        raise