"""Whisper transcription service — CPU-only, async-ready."""

import io
import tempfile
import time
from pathlib import Path

_model = None
_model_name = None


def _load_model(name: str = "base"):
    """Lazy-load Whisper model (downloads on first use)."""
    global _model, _model_name
    import whisper

    if _model is None or _model_name != name:
        _model = whisper.load_model(name)
        _model_name = name
    return _model


def transcribe_bytes(audio_bytes: bytes, model_name: str = "base") -> dict:
    """Transcribe audio from bytes. Returns {"text": "...", "segments": [...], "language": "..."}"""
    t0 = time.time()

    model = _load_model(model_name)

    # Write to temp file (whisper needs a file path or numpy array)
    suffix = ".wav"
    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
        tmp.write(audio_bytes)
        tmp_path = tmp.name

    try:
        result = model.transcribe(tmp_path, fp16=False)  # fp16=False for CPU
    finally:
        Path(tmp_path).unlink(missing_ok=True)

    elapsed = round(time.time() - t0, 1)

    return {
        "text": result["text"].strip(),
        "segments": [
            {
                "start": round(seg["start"], 2),
                "end": round(seg["end"], 2),
                "text": seg["text"].strip(),
            }
            for seg in result.get("segments", [])
        ],
        "language": result.get("language", "unknown"),
        "duration_seconds": elapsed,
        "model": model_name,
    }