from __future__ import annotations

import json
import os
import time
from contextlib import contextmanager
from typing import Optional, Annotated
from unicodedata import normalize
import re
import uuid
import io
import wave

import numpy as np
import onnxruntime as ort
import scipy.io.wavfile
import gradio as gr

from .File_System import ROOT_DIR
from app import _log_call_end, _log_call_start, _truncate_for_log
from ._docstrings import autodoc

try:
    import torch  # type: ignore
except Exception:  # pragma: no cover
    torch = None  # type: ignore

try:
    from kokoro import KModel, KPipeline  # type: ignore
except Exception:  # pragma: no cover
    KModel = None  # type: ignore
    KPipeline = None  # type: ignore

try:
    from huggingface_hub import snapshot_download, list_repo_files
except ImportError:
    snapshot_download = None
    list_repo_files = None


# --- Supertonic Helper Classes & Functions ---

class UnicodeProcessor:
    def __init__(self, unicode_indexer_path: str):
        with open(unicode_indexer_path, "r") as f:
            self.indexer = json.load(f)

    def _preprocess_text(self, text: str) -> str:
        # TODO: add more preprocessing
        text = normalize("NFKD", text)
        return text

    def _get_text_mask(self, text_ids_lengths: np.ndarray) -> np.ndarray:
        text_mask = length_to_mask(text_ids_lengths)
        return text_mask

    def _text_to_unicode_values(self, text: str) -> np.ndarray:
        unicode_values = np.array(
            [ord(char) for char in text], dtype=np.uint16
        )  # 2 bytes
        return unicode_values

    def __call__(self, text_list: list[str]) -> tuple[np.ndarray, np.ndarray]:
        text_list = [self._preprocess_text(t) for t in text_list]
        text_ids_lengths = np.array([len(text) for text in text_list], dtype=np.int64)
        text_ids = np.zeros((len(text_list), text_ids_lengths.max()), dtype=np.int64)
        for i, text in enumerate(text_list):
            unicode_vals = self._text_to_unicode_values(text)
            text_ids[i, : len(unicode_vals)] = np.array(
                [self.indexer[val] for val in unicode_vals], dtype=np.int64
            )
        text_mask = self._get_text_mask(text_ids_lengths)
        return text_ids, text_mask


class Style:
    def __init__(self, style_ttl_onnx: np.ndarray, style_dp_onnx: np.ndarray):
        self.ttl = style_ttl_onnx
        self.dp = style_dp_onnx


class TextToSpeech:
    def __init__(
        self,
        cfgs: dict,
        text_processor: UnicodeProcessor,
        dp_ort: ort.InferenceSession,
        text_enc_ort: ort.InferenceSession,
        vector_est_ort: ort.InferenceSession,
        vocoder_ort: ort.InferenceSession,
    ):
        self.cfgs = cfgs
        self.text_processor = text_processor
        self.dp_ort = dp_ort
        self.text_enc_ort = text_enc_ort
        self.vector_est_ort = vector_est_ort
        self.vocoder_ort = vocoder_ort
        self.sample_rate = cfgs["ae"]["sample_rate"]
        self.base_chunk_size = cfgs["ae"]["base_chunk_size"]
        self.chunk_compress_factor = cfgs["ttl"]["chunk_compress_factor"]
        self.ldim = cfgs["ttl"]["latent_dim"]

    def sample_noisy_latent(
        self, duration: np.ndarray
    ) -> tuple[np.ndarray, np.ndarray]:
        bsz = len(duration)
        wav_len_max = duration.max() * self.sample_rate
        wav_lengths = (duration * self.sample_rate).astype(np.int64)
        chunk_size = self.base_chunk_size * self.chunk_compress_factor
        latent_len = ((wav_len_max + chunk_size - 1) / chunk_size).astype(np.int32)
        latent_dim = self.ldim * self.chunk_compress_factor
        noisy_latent = np.random.randn(bsz, latent_dim, latent_len).astype(np.float32)
        latent_mask = get_latent_mask(
            wav_lengths, self.base_chunk_size, self.chunk_compress_factor
        )

        noisy_latent = noisy_latent * latent_mask
        return noisy_latent, latent_mask

    def _infer(
        self, text_list: list[str], style: Style, total_step: int, speed: float = 1.05
    ) -> tuple[np.ndarray, np.ndarray]:
        assert (
            len(text_list) == style.ttl.shape[0]
        ), "Number of texts must match number of style vectors"
        bsz = len(text_list)
        text_ids, text_mask = self.text_processor(text_list)
        dur_onnx, *_ = self.dp_ort.run(
            None, {"text_ids": text_ids, "style_dp": style.dp, "text_mask": text_mask}
        )
        dur_onnx = dur_onnx / speed
        text_emb_onnx, *_ = self.text_enc_ort.run(
            None,
            {"text_ids": text_ids, "style_ttl": style.ttl, "text_mask": text_mask},
        )  # dur_onnx: [bsz]
        xt, latent_mask = self.sample_noisy_latent(dur_onnx)
        total_step_np = np.array([total_step] * bsz, dtype=np.float32)
        for step in range(total_step):
            current_step = np.array([step] * bsz, dtype=np.float32)
            xt, *_ = self.vector_est_ort.run(
                None,
                {
                    "noisy_latent": xt,
                    "text_emb": text_emb_onnx,
                    "style_ttl": style.ttl,
                    "text_mask": text_mask,
                    "latent_mask": latent_mask,
                    "current_step": current_step,
                    "total_step": total_step_np,
                },
            )
        wav, *_ = self.vocoder_ort.run(None, {"latent": xt})
        return wav, dur_onnx

    def __call__(
        self,
        text: str,
        style: Style,
        total_step: int,
        speed: float = 1.05,
        silence_duration: float = 0.3,
        max_len: int = 300,
    ) -> tuple[np.ndarray, np.ndarray]:
        assert (
            style.ttl.shape[0] == 1
        ), "Single speaker text to speech only supports single style"
        text_list = chunk_text(text, max_len=max_len)
        wav_cat = None
        dur_cat = None
        for text in text_list:
            wav, dur_onnx = self._infer([text], style, total_step, speed)
            if wav_cat is None:
                wav_cat = wav
                dur_cat = dur_onnx
            else:
                silence = np.zeros(
                    (1, int(silence_duration * self.sample_rate)), dtype=np.float32
                )
                wav_cat = np.concatenate([wav_cat, silence, wav], axis=1)
                dur_cat += dur_onnx + silence_duration
        return wav_cat, dur_cat

    def stream(
        self,
        text: str,
        style: Style,
        total_step: int,
        speed: float = 1.05,
        silence_duration: float = 0.3,
        max_len: int = 300,
    ):
        assert (
            style.ttl.shape[0] == 1
        ), "Single speaker text to speech only supports single style"
        text_list = chunk_text(text, max_len=max_len)

        for i, text in enumerate(text_list):
            wav, _ = self._infer([text], style, total_step, speed)
            yield wav.flatten()

            if i < len(text_list) - 1:
                silence = np.zeros(
                    (int(silence_duration * self.sample_rate),), dtype=np.float32
                )
                yield silence

    def batch(
        self, text_list: list[str], style: Style, total_step: int, speed: float = 1.05
    ) -> tuple[np.ndarray, np.ndarray]:
        return self._infer(text_list, style, total_step, speed)


def length_to_mask(lengths: np.ndarray, max_len: Optional[int] = None) -> np.ndarray:
    """
    Convert lengths to binary mask.

    Args:
        lengths: (B,)
        max_len: int

    Returns:
        mask: (B, 1, max_len)
    """
    max_len = max_len or lengths.max()
    ids = np.arange(0, max_len)
    mask = (ids < np.expand_dims(lengths, axis=1)).astype(np.float32)
    return mask.reshape(-1, 1, max_len)


def get_latent_mask(
    wav_lengths: np.ndarray, base_chunk_size: int, chunk_compress_factor: int
) -> np.ndarray:
    latent_size = base_chunk_size * chunk_compress_factor
    latent_lengths = (wav_lengths + latent_size - 1) // latent_size
    latent_mask = length_to_mask(latent_lengths)
    return latent_mask


def load_onnx(
    onnx_path: str, opts: ort.SessionOptions, providers: list[str]
) -> ort.InferenceSession:
    return ort.InferenceSession(onnx_path, sess_options=opts, providers=providers)


def load_onnx_all(
    onnx_dir: str, opts: ort.SessionOptions, providers: list[str]
) -> tuple[
    ort.InferenceSession,
    ort.InferenceSession,
    ort.InferenceSession,
    ort.InferenceSession,
]:
    dp_onnx_path = os.path.join(onnx_dir, "duration_predictor.onnx")
    text_enc_onnx_path = os.path.join(onnx_dir, "text_encoder.onnx")
    vector_est_onnx_path = os.path.join(onnx_dir, "vector_estimator.onnx")
    vocoder_onnx_path = os.path.join(onnx_dir, "vocoder.onnx")

    dp_ort = load_onnx(dp_onnx_path, opts, providers)
    text_enc_ort = load_onnx(text_enc_onnx_path, opts, providers)
    vector_est_ort = load_onnx(vector_est_onnx_path, opts, providers)
    vocoder_ort = load_onnx(vocoder_onnx_path, opts, providers)
    return dp_ort, text_enc_ort, vector_est_ort, vocoder_ort


def load_cfgs(onnx_dir: str) -> dict:
    cfg_path = os.path.join(onnx_dir, "tts.json")
    with open(cfg_path, "r") as f:
        cfgs = json.load(f)
    return cfgs


def load_text_processor(onnx_dir: str) -> UnicodeProcessor:
    unicode_indexer_path = os.path.join(onnx_dir, "unicode_indexer.json")
    text_processor = UnicodeProcessor(unicode_indexer_path)
    return text_processor


def load_text_to_speech(onnx_dir: str, use_gpu: bool = False) -> TextToSpeech:
    opts = ort.SessionOptions()
    if use_gpu:
        raise NotImplementedError("GPU mode is not fully tested")
    else:
        providers = ["CPUExecutionProvider"]
        print("Using CPU for inference")
    cfgs = load_cfgs(onnx_dir)
    dp_ort, text_enc_ort, vector_est_ort, vocoder_ort = load_onnx_all(
        onnx_dir, opts, providers
    )
    text_processor = load_text_processor(onnx_dir)
    return TextToSpeech(
        cfgs, text_processor, dp_ort, text_enc_ort, vector_est_ort, vocoder_ort
    )


def load_voice_style(voice_style_paths: list[str], verbose: bool = False) -> Style:
    bsz = len(voice_style_paths)

    # Read first file to get dimensions
    with open(voice_style_paths[0], "r") as f:
        first_style = json.load(f)
    ttl_dims = first_style["style_ttl"]["dims"]
    dp_dims = first_style["style_dp"]["dims"]

    # Pre-allocate arrays with full batch size
    ttl_style = np.zeros([bsz, ttl_dims[1], ttl_dims[2]], dtype=np.float32)
    dp_style = np.zeros([bsz, dp_dims[1], dp_dims[2]], dtype=np.float32)

    # Fill in the data
    for i, voice_style_path in enumerate(voice_style_paths):
        with open(voice_style_path, "r") as f:
            voice_style = json.load(f)

        ttl_data = np.array(
            voice_style["style_ttl"]["data"], dtype=np.float32
        ).flatten()
        ttl_style[i] = ttl_data.reshape(ttl_dims[1], ttl_dims[2])

        dp_data = np.array(
            voice_style["style_dp"]["data"], dtype=np.float32
        ).flatten()
        dp_style[i] = dp_data.reshape(dp_dims[1], dp_dims[2])

    if verbose:
        print(f"Loaded {bsz} voice styles")
    return Style(ttl_style, dp_style)


@contextmanager
def timer(name: str):
    start = time.time()
    print(f"{name}...")
    yield
    print(f"  -> {name} completed in {time.time() - start:.2f} sec")


def sanitize_filename(text: str, max_len: int) -> str:
    """Sanitize filename by replacing non-alphanumeric characters with underscores"""
    prefix = text[:max_len]
    return re.sub(r"[^a-zA-Z0-9]", "_", prefix)


def chunk_text(text: str, max_len: int = 300) -> list[str]:
    """
    Split text into chunks by paragraphs and sentences.

    Args:
        text: Input text to chunk
        max_len: Maximum length of each chunk (default: 300)

    Returns:
        List of text chunks
    """
    # Split by paragraph (two or more newlines)
    paragraphs = [p.strip() for p in re.split(r"\n\s*\n+", text.strip()) if p.strip()]

    chunks = []

    for paragraph in paragraphs:
        paragraph = paragraph.strip()
        if not paragraph:
            continue

        # Split by sentence boundaries (period, question mark, exclamation mark followed by space)
        # But exclude common abbreviations like Mr., Mrs., Dr., etc. and single capital letters like F.
        pattern = r"(?<!Mr\.)(?<!Mrs\.)(?<!Ms\.)(?<!Dr\.)(?<!Prof\.)(?<!Sr\.)(?<!Jr\.)(?<!Ph\.D\.)(?<!etc\.)(?<!e\.g\.)(?<!i\.e\.)(?<!vs\.)(?<!Inc\.)(?<!Ltd\.)(?<!Co\.)(?<!Corp\.)(?<!St\.)(?<!Ave\.)(?<!Blvd\.)(?<!\b[A-Z]\.)(?<=[.!?])\s+"
        sentences = re.split(pattern, paragraph)

        current_chunk = ""

        for sentence in sentences:
            if len(current_chunk) + len(sentence) + 1 <= max_len:
                current_chunk += (" " if current_chunk else "") + sentence
            else:
                if current_chunk:
                    chunks.append(current_chunk.strip())
                current_chunk = sentence

        if current_chunk:
            chunks.append(current_chunk.strip())

    return chunks


# --- Main Tool Logic ---

# --- Kokoro State ---
_KOKORO_STATE = {
    "initialized": False,
    "device": "cpu",
    "model": None,
    "pipelines": {},
}

# --- Supertonic State ---
_SUPERTONIC_STATE = {
    "initialized": False,
    "tts": None,
    "assets_dir": None,
}

def _audio_np_to_int16(audio_np: np.ndarray) -> np.ndarray:
    audio_clipped = np.clip(audio_np, -1.0, 1.0)
    return (audio_clipped * 32767.0).astype(np.int16)

# --- Kokoro Functions ---

def get_kokoro_voices() -> list[str]:
    try:
        if list_repo_files:
            files = list_repo_files("hexgrad/Kokoro-82M")
            voice_files = [file for file in files if file.endswith(".pt") and file.startswith("voices/")]
            voices = [file.replace("voices/", "").replace(".pt", "") for file in voice_files]
            return sorted(voices) if voices else _get_fallback_voices()
        return _get_fallback_voices()
    except Exception:
        return _get_fallback_voices()


def _get_fallback_voices() -> list[str]:
    return [
        "af_alloy", "af_aoede", "af_bella", "af_heart", "af_jessica", "af_kore", "af_nicole", "af_nova", "af_river", "af_sarah", "af_sky",
        "am_adam", "am_echo", "am_eric", "am_fenrir", "am_liam", "am_michael", "am_onyx", "am_puck", "am_santa",
        "bf_alice", "bf_emma", "bf_isabella", "bf_lily",
        "bm_daniel", "bm_fable", "bm_george", "bm_lewis",
        "ef_dora", "em_alex", "em_santa",
        "ff_siwis",
        "hf_alpha", "hf_beta", "hm_omega", "hm_psi",
        "if_sara", "im_nicola",
        "jf_alpha", "jf_gongitsune", "jf_nezumi", "jf_tebukuro", "jm_kumo",
        "pf_dora", "pm_alex", "pm_santa",
        "zf_xiaobei", "zf_xiaoni", "zf_xiaoxiao", "zf_xiaoyi",
        "zm_yunjian", "zm_yunxi", "zm_yunxia", "zm_yunyang",
    ]


def _init_kokoro() -> None:
    if _KOKORO_STATE["initialized"]:
        return
    if KModel is None or KPipeline is None:
        raise RuntimeError("Kokoro is not installed. Please install the 'kokoro' package (>=0.9.4).")
    device = "cpu"
    if torch is not None:
        try:
            if torch.cuda.is_available():
                device = "cuda"
        except Exception:
            device = "cpu"
    model = KModel(repo_id="hexgrad/Kokoro-82M").to(device).eval()
    pipelines = {"a": KPipeline(lang_code="a", model=False, repo_id="hexgrad/Kokoro-82M")}
    try:
        pipelines["a"].g2p.lexicon.golds["kokoro"] = "kˈOkəɹO"
    except Exception:
        pass
    _KOKORO_STATE.update({"initialized": True, "device": device, "model": model, "pipelines": pipelines})

# --- Supertonic Functions ---

def _init_supertonic() -> None:
    if _SUPERTONIC_STATE["initialized"]:
        return
    
    if snapshot_download is None:
        raise RuntimeError("huggingface_hub is not installed.")

    # Use a local assets directory within Nymbo-Tools
    # Assuming this file is in Nymbo-Tools/Modules
    base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
    assets_dir = os.path.join(base_dir, "assets", "supertonic")
    
    if not os.path.exists(assets_dir):
        print(f"Downloading Supertonic models to {assets_dir}...")
        snapshot_download(repo_id="Supertone/supertonic", local_dir=assets_dir)
    
    onnx_dir = os.path.join(assets_dir, "onnx")
    tts = load_text_to_speech(onnx_dir, use_gpu=False)
    
    _SUPERTONIC_STATE.update({"initialized": True, "tts": tts, "assets_dir": assets_dir})


def get_supertonic_voices() -> list[str]:
    # We need assets to list voices. If not initialized, try to find them or init.
    if not _SUPERTONIC_STATE["initialized"]:
        # Check if assets exist without full init
        base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        assets_dir = os.path.join(base_dir, "assets", "supertonic")
        if not os.path.exists(assets_dir):
             # If we can't list, return a default list or empty
             return ["F1", "F2", "M1", "M2"] # Known defaults
    else:
        assets_dir = _SUPERTONIC_STATE["assets_dir"]

    voice_styles_dir = os.path.join(assets_dir, "voice_styles")
    if not os.path.exists(voice_styles_dir):
        return ["F1", "F2", "M1", "M2"]
        
    files = os.listdir(voice_styles_dir)
    voices = [f.replace('.json', '') for f in files if f.endswith('.json')]
    return sorted(voices)


def List_Kokoro_Voices() -> list[str]:
    return get_kokoro_voices()

def List_Supertonic_Voices() -> list[str]:
    return get_supertonic_voices()


# Single source of truth for the LLM-facing tool description
TOOL_SUMMARY = (
    "Synthesize speech from text using Supertonic-66M (default) or Kokoro-82M. "
    "Supertonic: faster, supports steps/silence/chunking. "
    "Kokoro: slower, supports many languages/accents. "
    "Return the generated media to the user in this format `![Alt text](URL)`."
)


@autodoc(
    summary=TOOL_SUMMARY,
)
def Generate_Speech(
    text: Annotated[str, "The text to synthesize (English)."],
    model: Annotated[str, "The TTS model to use: 'Supertonic' or 'Kokoro'."] = "Supertonic",
    speed: Annotated[float, "Speech speed multiplier in 0.5–2.0; 1.0 = normal speed."] = 1.3,
    steps: Annotated[int, "Supertonic only. Diffusion steps (1-50). Higher = better quality but slower."] = 5,
    voice: Annotated[str, "Voice identifier. Default 'F1' for Supertonic, 'af_heart' for Kokoro."] = "F1",
    silence_duration: Annotated[float, "Supertonic only. Silence duration between chunks (0.0-2.0s)."] = 0.3,
    max_chunk_size: Annotated[int, "Supertonic only. Max text chunk length (50-1000)."] = 300,
) -> str:
    _log_call_start("Generate_Speech", text=_truncate_for_log(text, 200), model=model, speed=speed, voice=voice)
    
    if not text or not text.strip():
        try:
            _log_call_end("Generate_Speech", "error=empty text")
        finally:
            pass
        raise gr.Error("Please provide non-empty text to synthesize.")

    model_lower = model.lower()
    
    # Handle default voice switching if user didn't specify appropriate voice for model
    if model_lower == "kokoro" and voice == "F1":
        voice = "af_heart"
    elif model_lower == "supertonic" and voice == "af_heart":
        voice = "F1"

    try:
        if model_lower == "kokoro":
            return _generate_kokoro(text, speed, voice)
        else:
            # Default to Supertonic
            return _generate_supertonic(text, speed, voice, steps, silence_duration, max_chunk_size)
            
    except gr.Error as exc:
        _log_call_end("Generate_Speech", f"gr_error={str(exc)}")
        raise
    except Exception as exc:  # pylint: disable=broad-except
        _log_call_end("Generate_Speech", f"error={str(exc)[:120]}")
        raise gr.Error(f"Error during speech generation: {exc}")


def _generate_kokoro(text: str, speed: float, voice: str) -> str:
    _init_kokoro()
    model = _KOKORO_STATE["model"]
    pipelines = _KOKORO_STATE["pipelines"]
    pipeline = pipelines.get("a")
    if pipeline is None:
        raise gr.Error("Kokoro English pipeline not initialized.")
    
    audio_segments = []
    pack = pipeline.load_voice(voice)
    
    segments = list(pipeline(text, voice, speed))
    total_segments = len(segments)
    for segment_idx, (text_chunk, ps, _) in enumerate(segments):
        ref_s = pack[len(ps) - 1]
        try:
            audio = model(ps, ref_s, float(speed))
            audio_segments.append(audio.detach().cpu().numpy())
            if total_segments > 10 and (segment_idx + 1) % 5 == 0:
                print(f"Progress: Generated {segment_idx + 1}/{total_segments} segments...")
        except Exception as exc:
            raise gr.Error(f"Error generating audio for segment {segment_idx + 1}: {exc}")
            
    if not audio_segments:
        raise gr.Error("No audio was generated (empty synthesis result).")
        
    if len(audio_segments) == 1:
        final_audio = audio_segments[0]
    else:
        final_audio = np.concatenate(audio_segments, axis=0)
        if total_segments > 1:
            duration = len(final_audio) / 24_000
            print(f"Completed: {total_segments} segments concatenated into {duration:.1f} seconds of audio")
    
    # Save to file
    filename = f"speech_kokoro_{uuid.uuid4().hex[:8]}.wav"
    output_path = os.path.join(ROOT_DIR, filename)
    
    # Normalize to 16-bit PCM
    audio_int16 = (final_audio * 32767).astype(np.int16)
    scipy.io.wavfile.write(output_path, 24000, audio_int16)
    
    _log_call_end("Generate_Speech", f"saved_to={os.path.basename(output_path)} duration_sec={len(final_audio)/24_000:.2f}")
    return output_path


def _generate_supertonic(text: str, speed: float, voice: str, steps: int, silence_duration: float, max_chunk_size: int) -> str:
    _init_supertonic()
    tts = _SUPERTONIC_STATE["tts"]
    assets_dir = _SUPERTONIC_STATE["assets_dir"]
    
    voice_path = os.path.join(assets_dir, "voice_styles", f"{voice}.json")
    if not os.path.exists(voice_path):
        # Fallback or error?
        # Try to find if it's just a name mismatch or use default
        if not os.path.exists(voice_path):
             raise gr.Error(f"Voice style {voice} not found for Supertonic.")

    style = load_voice_style([voice_path])
    
    sr = tts.sample_rate
    
    # Supertonic returns a generator of chunks, or we can use __call__ for full audio
    # Using __call__ to get full audio for saving
    # But __call__ returns (wav_cat, dur_cat)
    
    wav_cat, _ = tts(text, style, steps, speed, silence_duration, max_chunk_size)
    
    if wav_cat is None or wav_cat.size == 0:
         raise gr.Error("No audio generated.")

    # wav_cat is (1, samples) float32
    final_audio = wav_cat.flatten()
    
    # Save to file
    filename = f"speech_supertonic_{uuid.uuid4().hex[:8]}.wav"
    output_path = os.path.join(ROOT_DIR, filename)
    
    audio_int16 = _audio_np_to_int16(final_audio)
    scipy.io.wavfile.write(output_path, sr, audio_int16)
    
    _log_call_end("Generate_Speech", f"saved_to={os.path.basename(output_path)} duration_sec={len(final_audio)/sr:.2f}")
    return output_path


def build_interface() -> gr.Interface:
    kokoro_voices = get_kokoro_voices()
    supertonic_voices = get_supertonic_voices()
    all_voices = sorted(list(set(kokoro_voices + supertonic_voices)))

    return gr.Interface(
        fn=Generate_Speech,
        inputs=[
            gr.Textbox(label="Text", placeholder="Type text to synthesize…", lines=4, info="The text to synthesize (English)"),
            gr.Dropdown(label="Model", choices=["Supertonic", "Kokoro"], value="Supertonic", info="The TTS model to use"),
            gr.Slider(minimum=0.5, maximum=2.0, value=1.3, step=0.1, label="Speed", info="Speech speed multiplier (1.0 = normal)"),
            gr.Slider(minimum=1, maximum=50, value=5, step=1, label="Steps", info="Supertonic only: Diffusion steps (1-50)"),
            gr.Dropdown(
                label="Voice",
                choices=all_voices,
                value="F1",
                info="Select voice (F1/F2/M1/M2 for Supertonic, others for Kokoro)",
            ),
            gr.Slider(minimum=0.0, maximum=2.0, value=0.3, step=0.1, label="Silence Duration", info="Supertonic only: Silence duration between chunks"),
            gr.Slider(minimum=50, maximum=1000, value=300, step=10, label="Max Chunk Size", info="Supertonic only: Max text chunk length"),
        ],
        outputs=gr.Audio(label="Audio", type="filepath", format="wav"),
        title="Generate Speech",
        description=(
            "<div style=\"text-align:center\">Generate speech with Supertonic-66M or Kokoro-82M. Runs on CPU.</div>"
        ),
        api_description=TOOL_SUMMARY,
        flagging_mode="never",
    )


__all__ = ["Generate_Speech", "List_Kokoro_Voices", "List_Supertonic_Voices", "build_interface"]