"""
POST /api/v2/transcribe — Transcription with speaker diarization.

Calls the original Whisper server for transcription (with timestamps),
then runs speaker diarization and aligns the results.
"""
import os
import logging
import tempfile
from typing import List, Optional

import httpx
from fastapi import APIRouter, UploadFile, File, Form, HTTPException
from pydantic import BaseModel

from services.diarization import diarize, align_transcription_with_diarization, extract_embedding

logger = logging.getLogger(__name__)
router = APIRouter()

WHISPER_ORIGIN = os.getenv("WHISPER_ORIGIN", "http://10.100.16.13:5003")


# ── Response model ────────────────────────────────────────────────────────────
class TranscriptionSegment(BaseModel):
    start: float
    end: float
    speaker: str
    text: str


class DiarizedTranscriptionResponse(BaseModel):
    segments: List[TranscriptionSegment]
    speakers_count: int
    audio_duration: float
    language: str
    full_text: str
    diarization_method: str


# ── Endpoint ──────────────────────────────────────────────────────────────────
@router.post(
    "/transcribe",
    response_model=DiarizedTranscriptionResponse,
    summary="Transcription + Speaker Diarization",
    description=(
        "Transcribes the audio using the Whisper.cpp GPU server and adds speaker diarization. "
        "Returns segments labelled by speaker (SPEAKER_01, SPEAKER_02, …)."
    ),
)
async def v2_transcribe(
    audio: UploadFile = File(..., description="Audio file (WAV, MP3, M4A, OGG, FLAC)"),
    language: str = Form("pt", description="Language code (default: pt)"),
    num_speakers: Optional[int] = Form(None, description="Expected number of speakers (optional, auto-detected if omitted)"),
):
    audio_bytes = await audio.read()
    filename = audio.filename or "audio.wav"

    # Step 1 — call original Whisper server for timestamps
    transcription_segments = await _call_whisper_segments(audio_bytes, filename, language)

    # Step 2 — save to temp file for diarization
    suffix = os.path.splitext(filename)[-1].lower() or ".wav"
    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
        tmp.write(audio_bytes)
        tmp_path = tmp.name

    try:
        # Step 3 — diarize
        diarization_segs, method = diarize(tmp_path, num_speakers=num_speakers)

        # Step 4 — align
        aligned = align_transcription_with_diarization(transcription_segments, diarization_segs)

        # Auto-identify speakers against enrolled profiles
        try:
            import librosa
            import numpy as np
            import soundfile as sf
            from services.enrollment import identify_speaker

            y, sr = librosa.load(tmp_path, sr=16000, mono=True)
            speaker_labels = sorted({s["speaker"] for s in aligned})

            for speaker_label in speaker_labels:
                chunks = []
                for dseg in diarization_segs:
                    if dseg["speaker"] == speaker_label:
                        s_i = int(dseg["start"] * sr)
                        e_i = int(dseg["end"] * sr)
                        if e_i > s_i + int(0.5 * sr):
                            chunks.append(y[s_i:min(e_i, len(y))])

                if not chunks:
                    continue

                speaker_audio = np.concatenate(chunks)
                if len(speaker_audio) < sr * 3:
                    continue

                spk_path = None
                try:
                    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as spk_tmp:
                        sf.write(spk_tmp.name, speaker_audio, sr)
                        spk_path = spk_tmp.name

                    embedding = extract_embedding(spk_path)
                    id_result = identify_speaker(embedding)

                    if id_result["matched"]:
                        identified_name = id_result["speaker_name"]
                        logger.info(f"Auto-identified {speaker_label} as '{identified_name}' (conf={id_result['confidence']:.2f})")
                        for seg in aligned:
                            if seg["speaker"] == speaker_label:
                                seg["speaker"] = identified_name
                finally:
                    if spk_path:
                        try:
                            os.unlink(spk_path)
                        except Exception:
                            pass
        except Exception as auto_id_err:
            logger.warning(f"Auto-identification failed (non-fatal): {auto_id_err}")

        # Step 5 — compute metadata
        speakers = sorted({s["speaker"] for s in aligned})
        full_text = " ".join(s["text"] for s in aligned if s.get("text"))

        # Audio duration from diarization or transcription
        if diarization_segs:
            audio_duration = max(s["end"] for s in diarization_segs)
        elif transcription_segments:
            audio_duration = max(s.get("end", 0) for s in transcription_segments)
        else:
            audio_duration = 0.0

        return DiarizedTranscriptionResponse(
            segments=[TranscriptionSegment(**s) for s in aligned],
            speakers_count=len(speakers),
            audio_duration=round(audio_duration, 2),
            language=language,
            full_text=full_text.strip(),
            diarization_method=method,
        )

    finally:
        try:
            os.unlink(tmp_path)
        except Exception:
            pass


# ── Helper: call Whisper for transcription with timestamps ────────────────────
async def _call_whisper_segments(audio_bytes: bytes, filename: str, language: str) -> list:
    """
    Calls the original Whisper server at /transcribe-segments and returns
    the segments list [{start, end, text}, ...].
    """
    try:
        async with httpx.AsyncClient(timeout=600.0) as client:
            files = {"file": (filename, audio_bytes, "application/octet-stream")}
            data = {"language": language}
            resp = await client.post(
                f"{WHISPER_ORIGIN}/transcribe-segments",
                files=files,
                data=data,
            )
            resp.raise_for_status()
            result = resp.json()

            # Expected: {"text": "...", "language": "...", "segments": [...]}
            segments = result.get("segments", [])
            if not segments and result.get("text"):
                # Fallback: no segments, create a single segment
                segments = [{"start": 0.0, "end": 0.0, "text": result["text"]}]
            return segments

    except httpx.ConnectError as e:
        logger.error(f"Cannot connect to Whisper server: {e}")
        raise HTTPException(
            status_code=503,
            detail=f"Whisper server unavailable: {str(e)}",
        )
    except httpx.HTTPStatusError as e:
        logger.error(f"Whisper server error {e.response.status_code}: {e.response.text}")
        raise HTTPException(
            status_code=502,
            detail=f"Whisper server returned {e.response.status_code}",
        )
    except Exception as e:
        logger.error(f"Transcription error: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=f"Transcription failed: {str(e)}")