import asyncio
import sqlite3
from typing import Annotated, Optional

from ai_prompter import Prompter
from langchain_core.messages import AIMessage, SystemMessage
from langchain_core.runnables import RunnableConfig

from open_notebook.utils import clean_thinking_content
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages
from typing_extensions import TypedDict

from open_notebook.config import LANGGRAPH_CHECKPOINT_FILE
from open_notebook.domain.notebook import Notebook
from open_notebook.graphs.utils import provision_langchain_model


class ThreadState(TypedDict):
    messages: Annotated[list, add_messages]
    notebook: Optional[Notebook]
    context: Optional[str]
    context_config: Optional[dict]
    model_override: Optional[str]


def call_model_with_messages(state: ThreadState, config: RunnableConfig) -> dict:
    system_prompt = Prompter(prompt_template="chat").render(data=state)  # type: ignore[arg-type]
    payload = [SystemMessage(content=system_prompt)] + state.get("messages", [])
    model_id = config.get("configurable", {}).get("model_id") or state.get(
        "model_override"
    )

    # Handle async model provisioning from sync context
    def run_in_new_loop():
        """Run the async function in a new event loop"""
        new_loop = asyncio.new_event_loop()
        try:
            asyncio.set_event_loop(new_loop)
            return new_loop.run_until_complete(
                provision_langchain_model(
                    str(payload), model_id, "chat", max_tokens=8192
                )
            )
        finally:
            new_loop.close()
            asyncio.set_event_loop(None)

    try:
        # Try to get the current event loop
        asyncio.get_running_loop()
        # If we're in an event loop, run in a thread with a new loop
        import concurrent.futures

        with concurrent.futures.ThreadPoolExecutor() as executor:
            future = executor.submit(run_in_new_loop)
            model = future.result()
    except RuntimeError:
        # No event loop running, safe to use asyncio.run()
        model = asyncio.run(
            provision_langchain_model(
                str(payload),
                model_id,
                "chat",
                max_tokens=8192,
            )
        )

    ai_message = model.invoke(payload)

    # Clean thinking content from AI response (e.g., <think>...</think> tags)
    content = ai_message.content if isinstance(ai_message.content, str) else str(ai_message.content)
    cleaned_content = clean_thinking_content(content)
    cleaned_message = ai_message.model_copy(update={"content": cleaned_content})

    return {"messages": cleaned_message}


conn = sqlite3.connect(
    LANGGRAPH_CHECKPOINT_FILE,
    check_same_thread=False,
)
memory = SqliteSaver(conn)

agent_state = StateGraph(ThreadState)
agent_state.add_node("agent", call_model_with_messages)
agent_state.add_edge(START, "agent")
agent_state.add_edge("agent", END)
graph = agent_state.compile(checkpointer=memory)