import os
import asyncio
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langgraph.checkpoint.memory import MemorySaver
from langgraph.types import Command

# --- LLM initialization (OpenRouter) -------------------------------------------------
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,
)

# --- Backend setup -------------------------------------------------
backend = CompositeBackend([
    LocalShellBackend(workspace_dir="./workspace"),
    FilesystemBackend(),
])

# --- Tool definition -------------------------------------------------
@tool
def get_weather(city: str, date: str) -> str:
    """Return a mock weather report for the given city and date."""
    # In a real scenario this would call an external API.
    return f"The weather in {city} on {date} is sunny with a high of 25°C."

# --- Agent creation -------------------------------------------------
agent = create_deep_agent(
    model=llm,
    tools=[get_weather],
    backend=backend,
    system_prompt="You are a helpful assistant.",
    middleware=[
        HumanInTheLoopMiddleware(
            interrupt_on={"get_weather": True},
            description_prefix="Подтвердите вызов инструмента",
        ),
    ],
    checkpointer=MemorySaver(),
)

# --- Helper functions -------------------------------------------------
async def prompt_user_for_decisions(action_requests, review_configs):
    decisions = []
    for idx, action in enumerate(action_requests):
        print(f"\n--- Подтверждение ---")
        print(f"Инструмент: {action.get('name')}\n")
        print(f"Аргументы: {action.get('args')}\n")
        if "description" in action:
            print(f"Описание: {action['description']}\n")
        allowed = review_configs[idx].get("allowed_decisions", ["approve", "reject", "edit"])
        prompt = f"a = approve, r = reject{', e = edit' if 'edit' in allowed else ''}: "
        while True:
            choice = input(prompt).strip().lower()
            if choice == "a" and "approve" in allowed:
                decisions.append({"type": "approve"})
                break
            elif choice == "r" and "reject" in allowed:
                msg = input("Сообщение для агента (причина отказа): ")
                decisions.append({"type": "reject", "message": msg})
                break
            elif choice == "e" and "edit" in allowed:
                # Simple edit: ask for new JSON args
                new_args = input("Введите отредактированные аргументы в формате JSON: ")
                try:
                    import json
                    edited = json.loads(new_args)
                    decisions.append({"type": "edit", "edited_action": {"name": action['name'], "args": edited}})
                    break
                except json.JSONDecodeError:
                    print("Неверный JSON. Попробуйте снова.")
            else:
                print("Неверный выбор. Попробуйте снова.")
    return decisions

# --- Main interaction loop -------------------------------------------------
async def main():
    config = {"configurable": {"thread_id": "session-1"}}
    # Initial user message
    user_input = input("Вы: ")
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=user_input)]},
        config,
    )

    # Process possible interrupts
    while "__interrupt__" in result:
        interrupt = result["__interrupt__"][0].value
        action_requests = interrupt.get("action_requests", [])
        review_configs = interrupt.get("review_configs", [])
        decisions = await prompt_user_for_decisions(action_requests, review_configs)
        result = await agent.ainvoke(Command(resume={"decisions": decisions}), config)

    # Final answer
    final_message = result["messages"][-1].content
    print(f"\nАгент: {final_message}")

if __name__ == "__main__":
    asyncio.run(main())