from __future__ import annotations

import uuid
from typing import Generator

from langchain_core.messages import HumanMessage

from agents.graph import app_graph
from agents.state import MathMentorState
from config import Settings, settings
from memory.store import update_feedback


def _make_config(thread_id: str) -> dict:
    return {"configurable": {"thread_id": thread_id}}


def run_pipeline(
    input_text: str,
    input_image: str | None,
    input_audio: str | None,
    input_mode: str,
    thread_id: str,
    chat_history: list,
) -> Generator[dict, None, None]:
    """Run the full agent pipeline, yielding partial state updates for streaming."""

    if input_mode == "Image" and input_image:
        input_type = "image"
        raw_input = input_image
    elif input_mode == "Audio" and input_audio:
        input_type = "audio"
        raw_input = input_audio
    else:
        input_type = "text"
        raw_input = input_text

    if not settings.is_llm_configured:
        yield {
            "node": "error",
            "output": {"error": "LLM not configured. Set Base URL and Model in Settings or .env file."},
        }
        return

    initial_state: MathMentorState = {
        "input_type": input_type,
        "raw_input": raw_input,
        "needs_human_review": False,
        "human_approved": False,
        "human_edited_text": "",
        "agent_trace": [],
        "chat_history": chat_history + [HumanMessage(content=raw_input if input_type == "text" else f"[{input_type} input]")],
        "solver_retries": 0,
        "retrieved_chunks": [],
        "similar_past_problems": [],
        "solution_steps": [],
    }

    config = _make_config(thread_id)

    try:
        for event in app_graph.stream(initial_state, config, stream_mode="updates"):
            for node_name, node_output in event.items():
                yield {
                    "node": node_name,
                    "output": node_output,
                }
    except Exception as e:
        import traceback
        tb = traceback.format_exc()
        print(f"[PIPELINE ERROR] {tb}")
        yield {
            "node": "error",
            "output": {"error": f"{e}\n\nTraceback:\n{tb}"},
        }


def resume_after_hitl(
    thread_id: str,
    human_text: str = "",
    approved: bool = True,
) -> Generator[dict, None, None]:
    """Resume the graph after HITL interrupt."""
    config = _make_config(thread_id)

    app_graph.update_state(
        config,
        {
            "human_edited_text": human_text,
            "human_approved": approved,
            "needs_human_review": False,
        },
    )

    try:
        for event in app_graph.stream(None, config, stream_mode="updates"):
            for node_name, node_output in event.items():
                yield {
                    "node": node_name,
                    "output": node_output,
                }
    except Exception as e:
        import traceback
        tb = traceback.format_exc()
        print(f"[HITL RESUME ERROR] {tb}")
        yield {
            "node": "error",
            "output": {"error": f"{e}\n\nTraceback:\n{tb}"},
        }


def submit_feedback(
    thread_id: str,
    feedback: str,
    comment: str = "",
) -> str:
    """Submit user feedback for a solved problem."""
    config = _make_config(thread_id)
    try:
        from memory.store import get_all_records
        records = get_all_records()
        if records:
            last_id = records[-1].get("id", "")
            update_feedback(last_id, feedback, comment)
            return f"Feedback recorded: {feedback}"
    except Exception as e:
        return f"Error saving feedback: {e}"
    return "No record found to update."


def update_settings(base_url: str, model: str, api_key: str) -> str:
    """Update LLM settings at runtime. Only overwrite fields the user filled in."""
    base_url = base_url.strip()
    model = model.strip()
    api_key = api_key.strip()

    if base_url:
        settings.llm_base_url = base_url
    if model:
        settings.llm_model = model
    if api_key:
        settings.llm_api_key = api_key

    if not settings.is_llm_configured:
        return "⚠ LLM not configured. Please set Base URL and Model."

    return f"Settings updated: {settings.llm_model} @ {settings.llm_base_url}"


def new_thread_id() -> str:
    return str(uuid.uuid4())