import os
import json
import asyncio
from rich.console import Console
from langchain_openai import ChatOpenAI
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from langgraph.checkpoint.memory import MemorySaver

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

# --- Tool ------------------------------------------------------------------
@tool
def get_weather(query: str) -> str:
    """Return a fake weather report for a given city and date.
    The argument is a JSON string with keys "city" and "date".
    """
    try:
        data = json.loads(query)
        city = data.get("city", "unknown")
        date = data.get("date", "unknown")
        return f"Weather in {city} on {date}: sunny, 25°C."
    except Exception as e:
        return f"Error parsing query: {e}"

# --- Agent -----------------------------------------------------------------
memory = MemorySaver()

agent = create_deep_agent(
    model=llm,
    tools=[get_weather],
    backend=backend,
    system_prompt="You are a helpful weather assistant. Use the get_weather tool when asked about weather.",
    checkpointer=memory,
    interrupt_before=["tools"],  # pause before each tool call
)

console = Console()

# ---------------------------------------------------------------------------
async def ask_and_run(user_input, config):
    """Run the agent with optional user input.
    If user_input is None, the agent resumes from the paused state.
    """
    # Stream the agent's response
    async for chunk in agent.stream(user_input, config=config, stream_mode=["messages", "updates"]):
        state = agent.get_state(config)
        chunk_type, chunk_data = chunk

        if chunk_type == "messages":
            # chunk_data can be a dict with 'content'
            if isinstance(chunk_data, dict) and "content" in chunk_data:
                console.print(chunk_data["content"], end="")
            else:
                console.print(chunk_data)
        elif chunk_type == "updates":
            console.print("\n[bold]Tool call:[/bold]", chunk_data)

        # Detect pause before tool call
        if "__interrupt__" in chunk_data and state.next == ("tools",):
            # Retrieve the pending tool call
            last_msg = state.values["messages"][-1]
            tool_call = None
            if hasattr(last_msg, "tool_calls") and last_msg.tool_calls:
                tool_call = last_msg.tool_calls[0]
            if tool_call:
                tool_name = tool_call["name"]
                tool_args = tool_call["args"]
                console.print(f"\n[bold]Agent wants to call {tool_name}({tool_args})[/bold]")
                answer = input("Разрешить? (Y/n): ")
                if answer.lower().strip() == "y":
                    await ask_and_run(None, config)  # resume
                else:
                    console.print("[red]Отменено[/red]")
                    break

# ---------------------------------------------------------------------------
async def main():
    console.print("\n[bold green]Weather Agent ready! Type 'exit' to quit.[/bold green]")
    config = {"configurable": {"thread_id": "session-1"}}
    while True:
        user_input = input("\nВы: ")
        if user_input.lower() == "exit":
            break
        await ask_and_run({"messages": [{"role": "human", "content": user_input}]}, config)

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