# D:\GNPL-Private\TimeCraft\backend\app\api\endpoints.py

from fastapi import APIRouter, HTTPException, status
from typing import List

from app.models.task import TaskCreateRequest, ScheduleResponse
from app.services.llm_service import get_scheduled_tasks

# Initialize the API router
router = APIRouter()

@router.post(
    "/schedule/tasks",
    response_model=ScheduleResponse, # Define the response model for automatic serialization/validation
    status_code=status.HTTP_200_OK,  # Set the default successful status code
    summary="Create and Schedule Tasks using LLM",
    description="Receives a list of user-defined tasks, sends them to an LLM for intelligent scheduling, and returns a structured schedule."
)
async def create_and_schedule_tasks(tasks: List[TaskCreateRequest]):
    """
    Handles the POST request to create and schedule tasks.

    Args:
        tasks (List[TaskCreateRequest]): A list of task objects received from the frontend.

    Returns:
        ScheduleResponse: A structured response containing the scheduled tasks.

    Raises:
        HTTPException: If no tasks are provided, or if there's an error during LLM interaction
                       or internal processing.
    """
    # Validate if any tasks were provided in the request body
    if not tasks:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail="No tasks provided for scheduling. Please submit at least one task."
        )
    
    try:
        # Call the LLM service to get the scheduled tasks
        scheduled_response = await get_scheduled_tasks(tasks)
        return scheduled_response
    except RuntimeError as e:
        # Catch errors specifically from LLM interaction issues (e.g., network, API issues)
        print(f"LLM interaction failed: {e}") # Log the error for debugging
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to schedule tasks due to an issue with the LLM service. Please try again later. Details: {e}"
        )
    except ValueError as e:
        # Catch errors related to invalid data or parsing issues from the LLM's response
        print(f"Data validation or parsing error: {e}") # Log the error
        raise HTTPException(
            status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, # 422 for semantically erroneous request syntax
            detail=f"The LLM returned an invalid schedule structure or there was an internal data processing error. Details: {e}"
        )
    except Exception as e:
        # Catch any other unexpected errors
        print(f"An unexpected error occurred in endpoint: {e}") # Log the error
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"An unexpected error occurred while processing your request. Please contact support. Details: {e}"
        )