import os
import json
from langchain_openai import ChatOpenAI
from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langgraph.checkpoint.memory import MemorySaver
from langgraph.types import Command

# LLM setup
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.0,
)

# Simple tool: get_weather
from langchain.tools import tool

@tool
def get_weather(city: str, date: str = "сегодня") -> str:
    """Return a mock weather description for the given city and date."""
    # In a real scenario, call an API. Here we return a deterministic string.
    return f"Погода в {city} на {date}: солнечно, 25°C."

# Agent with HumanInTheLoopMiddleware
memory = MemorySaver()

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

# Helper to process interrupt and collect decisions

def handle_interrupt(interrupt_value):
    action_requests = interrupt_value["action_requests"]
    decisions = []
    for idx, action in enumerate(action_requests, 1):
        name = action.get("name")
        args = action.get("args", {})
        description = action.get("description", "")
        print(f"\n--- Подтверждение {idx} ---")
        print(f"Инструмент: {name}")
        print(f"Аргументы: {args}")
        if description:
            print(f"Описание: {description}")
        while True:
            choice = input("a = approve, r = reject: ").strip().lower()
            if choice == "a":
                decisions.append({"type": "approve"})
                break
            elif choice == "r":
                msg = input("Сообщение для агента (причина отказа): ")
                decisions.append({"type": "reject", "message": msg})
                break
            else:
                print("Неверный ввод. Попробуйте снова.")
    return decisions

# Main interaction loop
if __name__ == "__main__":
    config = {"configurable": {"thread_id": "сессия-1"}}
    while True:
        user_input = input("\nВы: ")
        if user_input.lower() in {"exit", "quit"}:
            print("Завершение.")
            break
        # Initial invoke
        result = agent.invoke({"messages": [{"role": "human", "content": user_input}]}, config=config)
        # Process interrupts
        while "__interrupt__" in result:
            interrupt = result["__interrupt__"][0].value
            decisions = handle_interrupt(interrupt)
            result = agent.invoke(Command(resume={"decisions": decisions}), config=config)
        # Final answer
        if result.get("messages"):
            final_msg = result["messages"][-1].content
            print(f"\nАгент: {final_msg}")
        else:
            print("\nАгент не вернул ответа.")