from typing import Any, Optional

from ai_prompter import Prompter
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.runnables import RunnableConfig
from langgraph.graph import END, START, StateGraph
from typing_extensions import TypedDict

from open_notebook.ai.provision import provision_langchain_model
from open_notebook.utils.text_utils import clean_thinking_content, extract_text_content


class PatternChainState(TypedDict):
    prompt: str
    parser: Optional[Any]
    input_text: str
    output: str


async def call_model(state: dict, config: RunnableConfig) -> dict:
    content = state["input_text"]
    system_prompt = Prompter(
        template_text=state["prompt"], parser=state.get("parser")
    ).render(data=state)
    payload = [SystemMessage(content=system_prompt)] + [HumanMessage(content=content)]
    chain = await provision_langchain_model(
        str(payload),
        config.get("configurable", {}).get("model_id"),
        "transformation",
        max_tokens=5000,
    )

    response = await chain.ainvoke(payload)

    # Clean thinking tags from response (handles extended thinking models)
    output = clean_thinking_content(extract_text_content(response.content))
    return {"output": output}


agent_state = StateGraph(PatternChainState)
agent_state.add_node("agent", call_model)  # type: ignore[type-var]
agent_state.add_edge(START, "agent")
agent_state.add_edge("agent", END)

graph = agent_state.compile()
