import hashlib
import os
import subprocess

SERVER_DIR = os.path.dirname(os.path.abspath(__file__))
DEFAULT_CACHE_DIR = os.path.join(SERVER_DIR, "data", "tts_cache")

PIPER_BIN = os.environ.get("PIPER_BIN", "/opt/piper/venv/bin/piper")
PIPER_MODELS_DIR = os.environ.get("PIPER_MODELS_DIR", "/opt/piper/voices")
TTS_CACHE_DIR = os.environ.get("TTS_CACHE_DIR", DEFAULT_CACHE_DIR)

VOICES = {
    "es": "es_ES-davefx-medium.onnx",
    "en": "en_US-lessac-medium.onnx",
    "zh": "zh_CN-huayan-medium.onnx",
}


def piper_available():
    return bool(PIPER_BIN) and os.path.isfile(PIPER_BIN)


def generate_speech(text, lang="es"):
    voice = VOICES.get(lang, VOICES["en"])
    digest = hashlib.sha256((lang + "\0" + text).encode("utf-8")).hexdigest()[:24]
    cache_path = os.path.join(TTS_CACHE_DIR, f"{lang}_{digest}.wav")
    if os.path.isfile(cache_path):
        return cache_path

    model_path = os.path.join(PIPER_MODELS_DIR, voice)
    if not os.path.isfile(model_path):
        raise RuntimeError(
            f"Piper 模型不存在: {model_path}，请先下载语音模型并配置 PIPER_MODELS_DIR"
        )

    os.makedirs(TTS_CACHE_DIR, exist_ok=True)
    tmp_path = cache_path + ".tmp"
    try:
        proc = subprocess.run(
            [
                PIPER_BIN,
                "--model",
                model_path,
                "--output_file",
                tmp_path,
            ],
            input=text.encode("utf-8"),
            capture_output=True,
            timeout=90,
        )
    except subprocess.TimeoutExpired as exc:
        raise RuntimeError("Piper 生成超时") from exc

    if proc.returncode != 0 or not os.path.isfile(tmp_path):
        stderr = proc.stderr.decode("utf-8", errors="replace")
        raise RuntimeError(stderr or "Piper 生成失败")

    os.replace(tmp_path, cache_path)
    return cache_path