import asyncio
import logging
import os
import tempfile
import traceback
from datetime import datetime

from api.state import state, Status
from api.router import broadcast
from config import load as load_config
from transcription import engine as transcription_engine
from llm import OllamaClient
from output import write_solo_docs, write_meeting_docs

logger = logging.getLogger(__name__)


async def run_pipeline():
    cfg = load_config()
    recorder = getattr(state, "_recorder", None)
    if recorder is None:
        return

    output_dir = getattr(state, "_recording_output_dir", cfg["output"]["path"])
    instructions = getattr(state, "_recording_instructions", "")
    diar_cfg = cfg.get("diarization", {})
    use_diarization = diar_cfg.get("enabled") and diar_cfg.get("hf_token")

    recorder.stop()
    await state.set_status(Status.PROCESSING)
    await broadcast({"event": "processing"})

    wav_path = None
    try:
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
            wav_path = f.name
        recorder.save_wav(wav_path)

        if use_diarization:
            await _run_meeting_pipeline(cfg, wav_path, output_dir, instructions, diar_cfg)
        else:
            await _run_solo_pipeline(cfg, wav_path, output_dir, instructions)

    except Exception as e:
        tb = traceback.format_exc()
        logger.error("Pipeline error:\n%s", tb)
        state.last_error = str(e)
        await state.set_status(Status.ERROR)
        await broadcast({"event": "error", "message": str(e)})
    finally:
        state.recording_user = None
        state._recording_output_dir = None
        state._recording_instructions = ""
        state._speakers_event = None
        state._pending_aligned_segments = None
        state._speaker_names = None
        if wav_path:
            try:
                os.unlink(wav_path)
            except OSError:
                pass


async def _run_solo_pipeline(cfg, wav_path, output_dir, instructions):
    """Original single-document pipeline (no diarization)."""
    raw_text = await transcription_engine.transcribe_file(
        wav_path,
        language=cfg["whisper"]["language"],
        model_name=cfg["whisper"]["model"],
        device=cfg["whisper"]["device"],
        base_url=cfg["whisper"].get("base_url", ""),
        backend=cfg["whisper"].get("backend", "openai"),
    )
    await broadcast({"event": "transcribed", "raw": raw_text})

    client = OllamaClient(base_url=cfg["ollama"]["base_url"])
    refined = await client.refine(
        raw_text=raw_text,
        instructions=instructions,
        model=cfg["ollama"]["model"],
    )

    dt = datetime.now()
    paths = write_solo_docs(raw_text=raw_text, refined=refined, output_dir=output_dir, dt=dt)

    title = "Diktat"
    for line in refined.splitlines():
        if line.startswith("# "):
            title = line[2:].strip()
            break

    await state.set_status(Status.IDLE)
    await broadcast({
        "event": "saved",
        "path": paths["index"],
        "title": title,
        "paths": paths,
    })


async def _run_meeting_pipeline(cfg, wav_path, output_dir, instructions, diar_cfg):
    """Diarization pipeline: 3 documents, speaker identification."""
    from diarization import Diarizer
    from alignment import align_segments

    diarizer = Diarizer(hf_token=diar_cfg["hf_token"])
    whisper_task = asyncio.create_task(
        transcription_engine.transcribe_file(
            wav_path,
            language=cfg["whisper"]["language"],
            model_name=cfg["whisper"]["model"],
            device=cfg["whisper"]["device"],
            base_url=cfg["whisper"].get("base_url", ""),
            backend=cfg["whisper"].get("backend", "openai"),
            with_segments=True,
        )
    )
    diar_task = asyncio.create_task(diarizer.diarize(wav_path))
    whisper_segs, speaker_segs = await asyncio.gather(whisper_task, diar_task)

    aligned = align_segments(whisper_segs, speaker_segs)
    await broadcast({"event": "transcribed", "raw": " ".join(t for _, t in aligned)})

    excerpt = "\n".join(f"{s}: {t}" for s, t in aligned[:20])
    client = OllamaClient(base_url=cfg["ollama"]["base_url"])
    name_map = await client.identify_speakers(excerpt, model=cfg["ollama"]["model"])

    if not name_map:
        excerpts_per_speaker = _build_excerpts(aligned)
        state._speakers_event = asyncio.Event()
        state._pending_aligned_segments = aligned
        await state.set_status(Status.AWAITING_SPEAKERS)
        await broadcast({"event": "speakers_unknown", "speakers": [
            {"id": spk, "excerpts": exs}
            for spk, exs in excerpts_per_speaker.items()
        ]})
        await state._speakers_event.wait()
        name_map = state._speaker_names or {}

    def resolve(label):
        name = name_map.get(label, "")
        if name:
            return name
        num = label.replace("SPEAKER_", "").lstrip("0") or "1"
        return f"Sprecher {num}"

    named_aligned = [(resolve(spk), text) for spk, text in aligned]
    speakers = sorted({spk for spk, _ in named_aligned})

    total_secs = sum(s["end"] - s["start"] for s in whisper_segs) if whisper_segs else 0
    duration_min = max(1, round(total_secs / 60))

    transcript_text = "\n\n".join(f"**{spk}:** {txt}" for spk, txt in named_aligned)
    summary = await client.summarize(transcript_text, model=cfg["ollama"]["model"])

    dt = datetime.now()
    paths = write_meeting_docs(
        aligned_segments=named_aligned,
        summary=summary,
        speakers=speakers,
        duration_min=duration_min,
        output_dir=output_dir,
        dt=dt,
    )

    await state.set_status(Status.IDLE)
    await broadcast({
        "event": "saved",
        "path": paths["index"],
        "title": f"Meeting {dt.strftime('%d.%m.%Y %H:%M')}",
        "meeting": True,
        "paths": paths,
    })


def _build_excerpts(aligned: list[tuple[str, str]], max_per_speaker: int = 4) -> dict[str, list[str]]:
    """Build a dict of speaker → list of text excerpts."""
    from collections import defaultdict
    buckets: dict[str, list[str]] = defaultdict(list)
    for spk, text in aligned:
        if len(buckets[spk]) < max_per_speaker:
            buckets[spk].append(text[:200])
    return dict(buckets)