"""Transcription endpoint using WhisperX."""

import logging
from typing import Optional

from fastapi import APIRouter, HTTPException
from pydantic import BaseModel

from services.transcription import transcribe_audio
from services.diarization import diarize_and_label

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


class TranscribeRequest(BaseModel):
    file_path: str
    model: str = "base"
    language: Optional[str] = None
    use_gpu: bool = True
    use_cache: bool = True
    diarize: bool = False
    hf_token: Optional[str] = None
    num_speakers: Optional[int] = None


@router.post("/transcribe")
async def transcribe(req: TranscribeRequest):
    try:
        result = transcribe_audio(
            file_path=req.file_path,
            model_name=req.model,
            use_gpu=req.use_gpu,
            use_cache=req.use_cache,
            language=req.language,
        )

        if req.diarize and req.hf_token:
            result = diarize_and_label(
                transcription_result=result,
                audio_path=req.file_path,
                hf_token=req.hf_token,
                num_speakers=req.num_speakers,
                use_gpu=req.use_gpu,
            )

        return result

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"File not found: {req.file_path}")
    except Exception as e:
        logger.error(f"Transcription failed: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))


class ReTranscribeSegmentRequest(BaseModel):
    file_path: str
    start: float
    end: float
    model: str = "base"
    language: Optional[str] = None


@router.post("/transcribe/segment")
async def transcribe_segment(req: ReTranscribeSegmentRequest):
    """
    Re-transcribe a specific segment of audio.
    Extracts the segment with FFmpeg, transcribes it, and returns words
    with timestamps adjusted to the original file timeline.
    """
    import subprocess
    import tempfile
    import os

    try:
        # Extract the segment to a temp file
        tmp_dir = tempfile.mkdtemp(prefix="talkedit_segment_")
        segment_path = os.path.join(tmp_dir, "segment.wav")

        cmd = [
            "ffmpeg", "-y",
            "-i", req.file_path,
            "-ss", str(req.start),
            "-to", str(req.end),
            "-vn",
            "-acodec", "pcm_s16le",
            "-ar", "16000",
            "-ac", "1",
            segment_path,
        ]
        result = subprocess.run(cmd, capture_output=True, text=True)
        if result.returncode != 0:
            raise RuntimeError(f"Segment extraction failed: {result.stderr[-300:]}")

        # Transcribe the segment — try GPU first, fall back to CPU
        try:
            segment_result = transcribe_audio(
                file_path=segment_path,
                model_name=req.model,
                use_gpu=True,
                use_cache=False,
                language=req.language,
            )
        except Exception as gpu_err:
            logger.warning(f"GPU transcription failed (%s), falling back to CPU", gpu_err)
            segment_result = transcribe_audio(
                file_path=segment_path,
                model_name=req.model,
                use_gpu=False,
                use_cache=False,
                language=req.language,
            )

        # Adjust timestamps to be relative to the original file
        offset = req.start
        adjusted_words = []
        for w in segment_result.get("words", []):
            w["start"] = round(w["start"] + offset, 3)
            w["end"] = round(w["end"] + offset, 3)
            adjusted_words.append(w)

        adjusted_segments = []
        for seg in segment_result.get("segments", []):
            seg["start"] = round(seg["start"] + offset, 3)
            seg["end"] = round(seg["end"] + offset, 3)
            # Also adjust words within each segment
            for w in seg.get("words", []):
                w["start"] = round(w["start"] + offset, 3)
                w["end"] = round(w["end"] + offset, 3)
            adjusted_segments.append(seg)

        # Cleanup
        try:
            os.remove(segment_path)
            os.rmdir(tmp_dir)
        except OSError:
            pass

        return {
            "words": adjusted_words,
            "segments": adjusted_segments,
            "language": segment_result.get("language", "en"),
        }

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"File not found: {req.file_path}")
    except Exception as e:
        logger.error(f"Segment transcription failed: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))