import os
import gradio as gr
from faster_whisper import WhisperModel
import soundfile as sf

# Fixed config for this app
MODEL_NAME = os.getenv("MODEL_NAME", "Systran/faster-whisper-small")  # fast & accurate enough for short clips
LANGUAGE = "ja"  # force Japanese
VAD = os.getenv("VAD_FILTER", "1") == "1"
MAX_SECONDS = int(os.getenv("MAX_SECONDS", "120"))  # 2 minutes

_model = None

def get_model():
    global _model
    if _model is not None:
        return _model
    # GPU first, then CPU fallbacks
    for device, compute_type in [("cuda", "float16"), ("cuda", "int8_float16"), ("cpu", "int8")]:
        try:
            m = WhisperModel(MODEL_NAME, device=device, compute_type=compute_type)
            _model = m
            print(f"[load] {MODEL_NAME} on {device}/{compute_type}")
            return _model
        except Exception as e:
            print(f"[load-failed] {device}/{compute_type}: {e}")
            continue
    raise RuntimeError("Unable to load model.")

def transcribe_upload(audio_path):
    if not audio_path:
        return "ファイルが選択されていません。"

    # duration guard
    try:
        data, sr = sf.read(audio_path)
        duration = len(data) / float(sr)
        if duration > MAX_SECONDS:
            return f"音声が長すぎます（{duration:.1f}秒）。最大{MAX_SECONDS}秒のファイルのみ対応しています。"
    except Exception as e:
        print(f"[warn] duration check failed: {e}")

    model = get_model()
    segments, info = model.transcribe(
        audio_path,
        language=LANGUAGE,   # 固定: 日本語
        task="transcribe",
        vad_filter=VAD,
    )
    text = "".join(seg.text for seg in segments)
    return text.strip()

with gr.Blocks() as demo:
    gr.Markdown("# 🇯🇵 日本語 音声→テキスト（アップロードのみ）\n- 日本語の音声ファイル（最大2分）をアップロードしてください。\n- 変換後のテキストが下に表示されます。")
    audio = gr.Audio(sources=["upload"], type="filepath", label="音声ファイルをアップロード（<2分）")
    out = gr.Textbox(lines=8, label="テキスト")
    gr.Button("文字起こし").click(transcribe_upload, inputs=[audio], outputs=[out])

demo.launch()