"""
routing.py

Routing helpers for the WalkXR LangGraph workflow.

These functions keep conditional graph logic separate from graph construction,
so branching decisions are easy to read and extend later.
"""

from __future__ import annotations
from walkxr_ai.core.state import WalkState

MEMORY_UPDATE_INTERVAL = 10


def route_after_call_llm(state: WalkState) -> str:
    """
    Routes the graph after the call_llm node.

    Returns:
    - "error" if the LLM step failed
    - "call_supervisor" if the response was generated successfully
    """
    if state.get("error"):
        return "error"

    return "call_supervisor"

def route_after_supervisor_score(state: WalkState) -> str:
    """
    Routes the graph after the reflection node.

    Returns:
    - "exit" if the supervisor scored the llm response as "good"
        or maximum response generation attempts have been reached
    - "loop" if the supervisor scored the llm response as "bad"
    """
    if state["supervisor_score"] == "good":
        return "exit"
    elif state["supervisor_retries"] >= state["max_retries_allowed"]:
        return "fail-safe"
    return "loop"


def route_after_update_conversation_history(state: WalkState) -> str:
    """
    Routes the graph after the conversation history has been updated.

    Returns:
    - "update_memory" when the current completed turn hits the memory interval
    - "end" otherwise
    """
    completed_turn = state.get("turn_index", 0) + 1

    # Update memory every MEMORY_UPDATE_INTERVAL turns
    if completed_turn % MEMORY_UPDATE_INTERVAL == 0:
        return "update_memory"

    return "end"