"""
Public API for the LangGraph chatbot used by the Streamlit app.

Mirrors the public surface of the original `chatbot_agents.py` but:
- the LLM client comes from `llm_client.create_chat_client()` (HF Inference Providers)
- the DB-query function comes from `db.get_metrics_from_db_filtered`
"""
from __future__ import annotations

from typing import Any

import pandas as pd

from agents.supervisor import build_chatbot_graph
from tools.state import create_initial_state


def build_langgraph_chatbot(
    metrics_df: pd.DataFrame | None = None,
    chat_client: Any | None = None,
    db_query_fn: Any | None = None,
    column_values: dict | None = None,
):
    """Build and compile the LangGraph chatbot app.

    Pass either ``metrics_df`` (in-memory mode) or ``db_query_fn`` + ``column_values``
    (SQL mode: targeted SQLite query per user message).
    """
    return build_chatbot_graph(
        metrics_df=metrics_df,
        azure_openai_client=chat_client,
        db_query_fn=db_query_fn,
        column_values=column_values,
    )


def invoke_langgraph_chatbot(
    app: Any,
    user_query: str,
    conversation_history: list[dict] | None = None,
    prior_filters: dict | None = None,
) -> dict:
    """Invoke chatbot graph and return final graph state."""
    initial_state = create_initial_state(
        user_query=user_query,
        conversation_history=conversation_history,
        prior_filters=prior_filters,
    )
    return app.invoke(initial_state)