"""Human-in-the-Loop через HumanInTheLoopMiddleware (LangChain)."""
from __future__ import annotations

import os

from dotenv import load_dotenv
from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langchain.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.types import Command

load_dotenv()

# LLM: OpenRouter из .env или локальный LM Studio
llm = ChatOpenAI(
    model=os.getenv("OPENAI_MODEL", "openai/gpt-oss-20b:free"),
    base_url=os.getenv("OPENAI_BASE_URL", "https://openrouter.ai/api/v1"),
    api_key=os.getenv("OPENAI_API_KEY", "fake"),
    temperature=0.0,
)


@tool
def get_weather(city: str, date: str = "сегодня") -> str:
    """Получить погоду в городе на указанную дату."""
    return f"В {city} на {date}: около +5°C, облачно, без осадков."


memory = MemorySaver()

agent = create_agent(
    model=llm,
    tools=[get_weather],
    system_prompt="Ты полезный ассистент",
    middleware=[
        HumanInTheLoopMiddleware(
            interrupt_on={
                "get_weather": {"allowed_decisions": ["approve", "reject"]},
            },
            description_prefix="Подтвердите вызов инструмента",
        ),
    ],
    checkpointer=memory,
)


def _print_action_requests(action_requests: list[dict]) -> None:
    print("\n--- Подтверждение ---")
    for idx, action in enumerate(action_requests, start=1):
        print(f"[{idx}] Инструмент: {action.get('name')}")
        print(f"    Аргументы: {action.get('args')}")
        description = action.get("description")
        if description:
            print(f"    Описание: {description}")


def _ask_decision() -> dict:
    while True:
        choice = input("a = approve, r = reject: ").strip().lower()
        if choice in ("a", "approve"):
            return {"type": "approve"}
        if choice in ("r", "reject"):
            message = input("Сообщение для агента (причина отказа): ").strip()
            return {"type": "reject", "message": message or "Отклонено пользователем"}
        print("Введите 'a' или 'r'.")


def _collect_decisions(action_requests: list[dict]) -> list[dict]:
    _print_action_requests(action_requests)
    return [_ask_decision() for _ in action_requests]


def _extract_interrupt(result: dict) -> tuple[list[dict], list[dict]] | None:
    if "__interrupt__" not in result:
        return None
    interrupt_value = result["__interrupt__"][0].value
    return interrupt_value.get("action_requests", []), interrupt_value.get("review_configs", [])


def run_with_hitl(user_message: str, thread_id: str = "hitl-middleware-session-1") -> str:
    """Запуск агента с циклом подтверждения инструментов."""
    config = {"configurable": {"thread_id": thread_id}}
    result = agent.invoke(
        {"messages": [{"role": "human", "content": user_message}]},
        config=config,
    )

    while True:
        interrupted = _extract_interrupt(result)
        if not interrupted:
            break
        action_requests, _review_configs = interrupted
        decisions = _collect_decisions(action_requests)
        result = agent.invoke(Command(resume={"decisions": decisions}), config=config)

    messages = result.get("messages", [])
    if not messages:
        return ""
    return str(messages[-1].content)


def main() -> None:
    print("Human-in-the-Loop (middleware). Введите 'exit' для выхода.\n")
    config_thread = "hitl-middleware-session-1"

    while True:
        user_input = input("Вы: ").strip()
        if user_input.lower() in ("exit", "quit", "q"):
            break
        if not user_input:
            continue

        answer = run_with_hitl(user_input, thread_id=config_thread)
        print(f"\nАгент: {answer}\n")


if __name__ == "__main__":
    main()