import os
import subprocess
import gradio as gr
import spaces
from infer_rvc_python import BaseLoader
import random
import logging
import time
import soundfile as sf
from infer_rvc_python.main import download_manager, load_hu_bert, Config
import zipfile
import edge_tts
import asyncio
import librosa
import traceback
import soundfile as sf
from pedalboard import Pedalboard, Reverb, Compressor, HighpassFilter
from pedalboard.io import AudioFile
from pydub import AudioSegment
import noisereduce as nr
import numpy as np
import urllib.request
import urllib.parse
import urllib.error
import shutil
import threading
import argparse
import sys
import torch
import fairseq
from picklescan.scanner import scan_file_path
import hashlib


ALLOWED_DOMAINS = {"huggingface.co", "hf.co"}
MODEL_CACHE = {}

parser = argparse.ArgumentParser(description="Run the app with optional sharing")
parser.add_argument(
    '--share',
    action='store_true',
    help='Enable sharing mode'
)
parser.add_argument(
    '--theme',
    type=str,
    default="aliabid94/new-theme",
    help='Set the theme (default: aliabid94/new-theme)'
)
args = parser.parse_args()

IS_COLAB = True if ('google.colab' in sys.modules or args.share) else False
IS_ZERO_GPU = os.getenv("SPACES_ZERO_GPU")

logging.getLogger("infer_rvc_python").setLevel(logging.ERROR)

torch.serialization.add_safe_globals([fairseq.data.dictionary.Dictionary])
converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)
converter.hu_bert_model = load_hu_bert(Config(only_cpu=False), converter.hubert_path)

test_model = "https://huggingface.co/sail-rvc/Aldeano_Minecraft__RVC_V2_-_500_Epochs_/resolve/main/model.pth?download=true, https://huggingface.co/sail-rvc/Aldeano_Minecraft__RVC_V2_-_500_Epochs_/resolve/main/model.index?download=true"
test_names = ["model.pth", "model.index"]

for url, filename in zip(test_model.split(", "), test_names):
    try:
        download_manager(
            url=url,
            path=".",
            extension="",
            overwrite=False,
            progress=True,
        )
        if not os.path.isfile(filename):
            raise FileNotFoundError
    except Exception:
        with open(filename, "wb") as f:
            pass

title = "<center><strong><font size='7'>RVC⚡ZERO</font></strong></center>"
description = "This demo is provided for educational and research purposes only. The authors and contributors of this project do not endorse or encourage any misuse or unethical use of this software. Any use of this software for purposes other than those intended is solely at the user's own risk. The authors and contributors shall not be held responsible for any damages or liabilities arising from the use of this demo inappropriately." if IS_ZERO_GPU else ""
RESOURCES = "- You can also try `RVC⚡ZERO` in Colab’s free tier, which provides free GPU [link](https://github.com/R3gm/rvc_zero_ui?tab=readme-ov-file#rvczero)."
theme = args.theme
delete_cache_time = (3200, 3200) if IS_ZERO_GPU else (86400, 86400)

PITCH_ALGO_OPT = [
    "pm",
    "harvest",
    "crepe",
    "rmvpe",
    "rmvpe+",
]


def check_model_safety(file_path: str):
    """Statically checks model or archive integrity. Raises ValueError if unsafe."""
    if not file_path or not os.path.exists(file_path):
        print(f"Skip file: {file_path}")
        return

    ext = os.path.splitext(file_path)[1].lower()
    if ext in (".pth", ".pt", ".bin", ".zip", ".pkl"):
        try:
            result = scan_file_path(file_path)
            if result and result.infected_files > 0:
                raise ValueError(
                    f"Integrity check failed: '{os.path.basename(file_path)}' contains unsupported or unsafe structures."
                )
        except ValueError:
            raise
        except Exception as e:
            print(f"Integrity check skipped for {file_path}: {e}")


def get_file_hash(file_path: str) -> str:
    hasher = hashlib.sha256()
    with open(file_path, "rb") as f:
        while chunk := f.read(65536):
            hasher.update(chunk)
    return hasher.hexdigest()


async def get_voices_list(proxy=None):
    """Print all available voices."""
    from edge_tts import list_voices
    voices = await list_voices(proxy=proxy)
    voices = sorted(voices, key=lambda voice: voice["ShortName"])

    table = [
        {
            "ShortName": voice["ShortName"],
            "Gender": voice["Gender"],
            "ContentCategories": ", ".join(voice["VoiceTag"]["ContentCategories"]),
            "VoicePersonalities": ", ".join(voice["VoiceTag"]["VoicePersonalities"]),
            "FriendlyName": voice["FriendlyName"],
        }
        for voice in voices
    ]

    return table


def find_files(directory):
    file_paths = []
    for filename in os.listdir(directory):
        # Check if the file has the desired extension
        if filename.endswith('.pth') or filename.endswith('.zip') or filename.endswith('.index'):
            # If yes, add the file path to the list
            file_paths.append(os.path.join(directory, filename))

    return file_paths


def unzip_in_folder(my_zip, my_dir):
    with zipfile.ZipFile(my_zip) as zip:
        for zip_info in zip.infolist():
            if zip_info.is_dir():
                continue
            zip_info.filename = os.path.basename(zip_info.filename)
            zip.extract(zip_info, my_dir)


def find_my_model(a_, b_):

    if a_ is None or a_.endswith(".pth"):
        if a_ and a_.endswith(".pth"):
            check_model_safety(a_)
        return a_, b_

    input_hash = None
    if a_ and os.path.exists(a_):
        input_hash = get_file_hash(a_)
        if b_ and os.path.exists(b_):
            input_hash += "_" + get_file_hash(b_)

    if input_hash and input_hash in MODEL_CACHE:
        cached_model, cached_index = MODEL_CACHE[input_hash]
        model_exists = cached_model and os.path.exists(cached_model)
        index_exists = (cached_index is None) or os.path.exists(cached_index)

        if model_exists and index_exists:
            check_model_safety(cached_model)
            gr.Info(f"Model found: {cached_model}")
            if cached_index:
                gr.Info(f"Index found: {cached_index}")
            return cached_model, cached_index
        else:
            del MODEL_CACHE[input_hash]

    txt_files = []
    for base_file in [a_, b_]:
        if base_file is not None and base_file.endswith(".txt"):
            txt_files.append(base_file)

    directory = os.path.dirname(a_)

    for txt in txt_files:
        with open(txt, 'r') as file:
            first_line = file.readline()

        url_to_download = first_line.strip()
        ensure_valid_file(url_to_download)

        download_manager(
            url=url_to_download,
            path=directory,
            extension="",
        )

    for f in find_files(directory):
        if f.endswith(".zip"):
            check_model_safety(f)
            unzip_in_folder(f, directory)

    model = None
    index = None
    end_files = find_files(directory)

    for ff in end_files:
        if ff.endswith(".pth"):
            check_model_safety(ff)
            model = ff
            gr.Info(f"Model found: {ff}")
        if ff.endswith(".index"):
            index = ff
            gr.Info(f"Index found: {ff}")

    if not model:
        gr.Error(f"Model not found in: {end_files}")

    if not index:
        gr.Warning("Index not found")

    if model and input_hash:
        MODEL_CACHE[input_hash] = (model, index)

    return model, index


def validate_url(url: str) -> str:
    """Validate URL protocol and host."""
    url = url.strip()
    if not url:
        raise ValueError("URL cannot be empty.")

    parsed = urllib.parse.urlparse(url)
    if parsed.scheme != "https":
        raise ValueError(f"Invalid protocol '{parsed.scheme}'. Only HTTPS is allowed.")

    hostname = (parsed.hostname or "").lower()
    if not any(hostname == d or hostname.endswith("." + d) for d in ALLOWED_DOMAINS):
        raise ValueError("Only downloads from Hugging Face are allowed.")

    return url


def get_supported_audio_video_extensions():
    try:
        subtitle_codecs = set()
        decoders_res = subprocess.run(
            ["ffmpeg", "-decoders"], capture_output=True, text=True, check=True
        )
        for line in decoders_res.stdout.splitlines():
            line_str = line.strip()
            if len(line_str) > 7 and line_str[0] in ("V", "A", "S"):
                parts = line_str.split()
                if len(parts) >= 2:
                    media_type = line_str[0]
                    codec_name = parts[1].lower()
                    if media_type == "S":
                        subtitle_codecs.add(codec_name)

        demuxers_res = subprocess.run(
            ["ffmpeg", "-demuxers"], capture_output=True, text=True, check=True
        )
        extensions = set()
        for line in demuxers_res.stdout.splitlines():
            line_str = line.strip()
            if line_str.startswith("D"):
                parts = line_str.split(maxsplit=2)
                if len(parts) >= 2:
                    demux_names = parts[1].split(",")
                    description = parts[2].lower() if len(parts) >= 3 else ""

                    if any(
                        sub_kw in description
                        for sub_kw in ["subtitle", "caption", "teletext", "lyrics"]
                    ):
                        continue

                    for ext in demux_names:
                        clean_ext = ext.strip().lower()
                        if clean_ext in subtitle_codecs:
                            continue
                        if clean_ext and clean_ext.isalnum():
                            extensions.add(f".{clean_ext}")

        return sorted(list(extensions))
    except Exception as e:
        print(f"Error querying ffmpeg: {e}")
        return [".mp3", ".wav", ".flac", ".m4a", ".aac", ".ogg", ".opus", ".wma", ".aiff", ".aif", ".alac", ".caf", ".amr"]


def ensure_valid_file(url):
    url = validate_url(url)

    try:
        request = urllib.request.Request(url, method="HEAD", headers={"User-Agent": "Mozilla/5.0"})
        with urllib.request.urlopen(request, timeout=15) as response:
            content_length = response.headers.get("Content-Length")

        if content_length is None:
            raise ValueError("Unable to read file info from url. The link might be invalid or unreachable.")

        file_size = int(content_length)
        # print("debug", url, file_size)
        if file_size > 900000000 and IS_ZERO_GPU:
            raise ValueError("The file is too large. Max allowed is 900 MB.")

        return file_size

    except urllib.error.HTTPError as err:
        raise ValueError(
            f"HTTP Error {err.code} ({err.reason}): The file at the provided URL does not exist, is private, or has been deleted from Hugging Face. Please verify the link."
        )

    except Exception as e:
        raise e

def clear_files(directory):
    time.sleep(15)
    print(f"Clearing files: {directory}.")
    shutil.rmtree(directory)


def get_my_model(url_data, progress=gr.Progress(track_tqdm=True)):

    if not url_data:
        return None, None

    if "," in url_data:
        a_, b_ = url_data.split(",")
        a_, b_ = a_.strip().replace("/blob/", "/resolve/"), b_.strip().replace("/blob/", "/resolve/")
    else:
        a_, b_ = url_data.strip().replace("/blob/", "/resolve/"), None

    out_dir = "downloads"
    folder_download = str(random.randint(1000, 9999))
    directory = os.path.join(out_dir, folder_download)
    os.makedirs(directory, exist_ok=True)

    try:
        valid_url = [a_] if not b_ else [a_, b_]
        for link in valid_url:
            ensure_valid_file(link)
            download_manager(
                url=link,
                path=directory,
                extension="",
            )

        for f in find_files(directory):
            if f.endswith(".zip"):
                check_model_safety(f)
                unzip_in_folder(f, directory)

        model = None
        index = None
        end_files = find_files(directory)

        for ff in end_files:
            if ff.endswith(".pth"):
                check_model_safety(ff)
                model = ff
                gr.Info(f"Model found: {ff}")
            if ff.endswith(".index"):
                index = ff
                gr.Info(f"Index found: {ff}")

        if not model:
            raise ValueError(f"Model not found in: {end_files}")

        if not index:
            gr.Warning("Index not found")
        else:
            index = os.path.abspath(index)

        return os.path.abspath(model), index

    except Exception as e:
        raise e
    finally:
        # time.sleep(10)
        # shutil.rmtree(directory)
        t = threading.Thread(target=clear_files, args=(directory,))
        t.start()


def add_audio_effects(audio_list, type_output):
    print("Audio effects")

    result = []
    for audio_path in audio_list:
        try:
            output_path = f'{os.path.splitext(audio_path)[0]}_effects.{type_output}'

            # Initialize audio effects plugins
            board = Pedalboard(
                [
                    HighpassFilter(),
                    Compressor(ratio=4, threshold_db=-15),
                    Reverb(room_size=0.10, dry_level=0.8, wet_level=0.2, damping=0.7)
                 ]
            )

            # Temporary WAV to hold processed data before exporting
            temp_wav = f'{os.path.splitext(audio_path)[0]}_temp.wav'

            with AudioFile(audio_path) as f:
                with AudioFile(temp_wav, 'w', f.samplerate, f.num_channels) as o:
                    while f.tell() < f.frames:
                        chunk = f.read(int(f.samplerate))
                        effected = board(chunk, f.samplerate, reset=False)
                        o.write(effected)

            # Convert with pydub to desired output type
            audio_seg = AudioSegment.from_file(temp_wav, format=type_output)
            audio_seg.export(output_path, format=type_output, bitrate=("320k" if type_output == "mp3" else None))

            # Clean up temp file
            os.remove(temp_wav)

            result.append(output_path)
        except Exception as e:
            traceback.print_exc()
            print(f"Error noisereduce: {str(e)}")
            result.append(audio_path)

    return result


def apply_noisereduce(audio_list, type_output):
    # https://github.com/sa-if/Audio-Denoiser
    print("Noice reduce")

    result = []
    for audio_path in audio_list:
        out_path = f"{os.path.splitext(audio_path)[0]}_noisereduce.{type_output}"

        try:
            # Load audio file
            audio = AudioSegment.from_file(audio_path)

            # Convert audio to numpy array
            samples = np.array(audio.get_array_of_samples())

            # Reduce noise
            reduced_noise = nr.reduce_noise(samples, sr=audio.frame_rate, prop_decrease=0.6)

            # Convert reduced noise signal back to audio
            reduced_audio = AudioSegment(
                reduced_noise.tobytes(), 
                frame_rate=audio.frame_rate, 
                sample_width=audio.sample_width,
                channels=audio.channels
            )

            # Save reduced audio to file
            reduced_audio.export(out_path, format=type_output, bitrate=("320k" if type_output == "mp3" else None))
            result.append(out_path)

        except Exception as e:
            traceback.print_exc()
            print(f"Error noisereduce: {str(e)}")
            result.append(audio_path)

    return result


@spaces.GPU()
def convert_now(audio_files, random_tag, converter, type_output, steps):
    for step in range(steps):
        audio_files = converter(
            audio_files,
            random_tag,
            overwrite=False,
            parallel_workers=(2 if IS_COLAB else 8),
            type_output=type_output,
        )

    return audio_files


def run(
    audio_files,
    file_m,
    pitch_alg,
    pitch_lvl,
    file_index,
    index_inf,
    r_m_f,
    e_r,
    c_b_p,
    active_noise_reduce,
    audio_effects,
    type_output,
    steps,
):
    if not audio_files:
        raise ValueError("The audio pls")

    if isinstance(audio_files, str):
        audio_files = [audio_files]

    try:
        duration_base = librosa.get_duration(filename=audio_files[0])
        print("Duration:", duration_base)
    except Exception as e:
        print(e)

    if file_m is not None and (file_m.endswith(".txt") or file_m.endswith(".zip")):
        file_m, file_index = find_my_model(file_m, file_index)
        # print(file_m, file_index)

    random_tag = "USER_"+str(random.randint(10000000, 99999999))

    converter.apply_conf(
        tag=random_tag,
        file_model=file_m,
        pitch_algo=pitch_alg,
        pitch_lvl=pitch_lvl,
        file_index=file_index,
        index_influence=index_inf,
        respiration_median_filtering=r_m_f,
        envelope_ratio=e_r,
        consonant_breath_protection=c_b_p,
        resample_sr=0,
    )
    time.sleep(0.1)

    result = convert_now(audio_files, random_tag, converter, type_output, steps)

    if active_noise_reduce:
        result = apply_noisereduce(result, type_output)

    if audio_effects:
        result = add_audio_effects(result, type_output)

    return result


def clear_player():
    return None


def load_first_audio(output_files, play_audio):
    if not play_audio or not output_files:
        return None
    first_file = output_files[0]
    if isinstance(first_file, dict):
        first_file = first_file.get("name")
    return first_file


def audio_source_conf():
    return gr.Radio(
        choices=["📁 Upload", "🗣️ TTS", "🎙️ Record"],
        value="📁 Upload",
        label="Audio Source",
    )


def mic_conf():
    return gr.Audio(
        sources=["microphone"],
        type="filepath",
        label="Record Audio",
        visible=False,
    )


def audio_conf():
    return gr.File(
        label="Audio files",
        file_count="multiple",
        type="filepath",
        container=True,
        file_types=supported_extensions,
    )


def model_conf():
    return gr.File(
        label="Model file",
        type="filepath",
        height=130,
        file_types=[".txt", ".pth", ".zip"],
    )


def pitch_algo_conf():
    return gr.Dropdown(
        PITCH_ALGO_OPT,
        value=PITCH_ALGO_OPT[4],
        label="Pitch algorithm",
        visible=True,
        interactive=True,
    )


def pitch_lvl_conf():
    return gr.Slider(
        label="Pitch level",
        minimum=-24,
        maximum=24,
        step=1,
        value=0,
        visible=True,
        interactive=True,
    )


def index_conf():
    return gr.File(
        label="Index file",
        type="filepath",
        height=130,
        file_types=[".index", ".txt"],
    )


def index_inf_conf():
    return gr.Slider(
        minimum=0,
        maximum=1,
        label="Index influence",
        value=0.75,
    )


def respiration_filter_conf():
    return gr.Slider(
        minimum=0,
        maximum=7,
        label="Respiration median filtering",
        value=3,
        step=1,
        interactive=True,
    )


def envelope_ratio_conf():
    return gr.Slider(
        minimum=0,
        maximum=1,
        label="Envelope ratio",
        value=0.25,
        interactive=True,
    )


def consonant_protec_conf():
    return gr.Slider(
        minimum=0,
        maximum=0.5,
        label="Consonant breath protection",
        value=0.5,
        interactive=True,
    )


def button_conf():
    return gr.Button(
        "Inference",
        variant="primary",
    )


def output_conf():
    return gr.File(
        label="Result",
        file_count="multiple",
        interactive=False,
    )


def tts_voice_conf():
    return gr.Dropdown(
        label="tts voice",
        choices=voices,
        visible=False,
        value="en-US-EmmaMultilingualNeural-Female",
    )


def tts_text_conf():
    return gr.Textbox(
        value="",
        placeholder="Write the text here...",
        label="Text",
        visible=False,
        lines=3,
    )


def tts_button_conf():
    return gr.Button(
        "Process TTS",
        variant="secondary",
        visible=False,
    )


def player_conf():
    return gr.Checkbox(
        False,
        label="Audio Player",
        container=False,
        visible=True,
    )


def sound_gui():
    return gr.Audio(
        value=None,
        type="filepath",
        autoplay=True,
        visible=False,
        interactive=False,
        label="Audio Player",
    )


def steps_conf():
    return gr.Slider(
        minimum=1,
        maximum=3,
        label="Steps",
        value=1,
        step=1,
        interactive=True,
    )


def format_output_gui():
    return gr.Dropdown(
        label="Format output:",
        choices=["wav", "mp3", "flac"],
        value="wav",
    )

def denoise_conf():
    return gr.Checkbox(
        False,
        label="Denoise",
        # info="",
        container=False,
        visible=True,
    )


def effects_conf():
    return gr.Checkbox(
        False,
        label="Reverb",
        # info="",
        container=False,
        visible=True,
    )


def infer_tts_audio(tts_voice, tts_text, play_audio):
    out_dir = "output"
    folder_tts = "USER_"+str(random.randint(10000, 99999))

    os.makedirs(out_dir, exist_ok=True)
    os.makedirs(os.path.join(out_dir, folder_tts), exist_ok=True)
    out_path = os.path.join(out_dir, folder_tts, "tts.mp3")

    asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(out_path))
    player_audio = out_path if play_audio else None
    return [out_path], player_audio


def show_components_source(mode):
    return (
        gr.update(visible=(mode == "🗣️ TTS")),
        gr.update(visible=(mode == "🗣️ TTS")),
        gr.update(visible=(mode == "🗣️ TTS")),
        gr.update(visible=(mode == "🎙️ Record")),
    )


def sync_mic_audio(mic_path):
    if not mic_path:
        return None
    return [mic_path]


def down_active_conf():
    return gr.Checkbox(
        False,
        label="URL-to-Model",
        # info="",
        container=False,
    )


def down_url_conf():
    return gr.Textbox(
        value="",
        placeholder="Write the url here...",
        label="Enter URL",
        visible=False,
        lines=1,
    )


def down_button_conf():
    return gr.Button(
        "Process",
        variant="secondary",
        visible=False,
    )


def show_components_down(value_active):
    return gr.update(
        visible=value_active
    ), gr.update(
        visible=value_active
    ), gr.update(
        visible=value_active
    )


CSS = ""
supported_extensions = get_supported_audio_video_extensions()
print("Supported extensions found:", supported_extensions)


def get_gui(theme):
    with gr.Blocks(theme=theme, css=CSS, fill_width=True, fill_height=False, delete_cache=delete_cache_time) as app:
        gr.Markdown(title)
        gr.Markdown(description)

        audio_source = audio_source_conf()
        with gr.Row():
            with gr.Column(scale=1):
                tts_text = tts_text_conf()
            with gr.Column(scale=2):
                with gr.Row():
                    with gr.Column():
                        with gr.Row():
                            tts_voice = tts_voice_conf()

                tts_button = tts_button_conf()

        mic_aud = mic_conf()
        aud = audio_conf()
        # gr.HTML("<hr>")

        audio_source.change(
            fn=show_components_source,
            inputs=[audio_source],
            outputs=[tts_voice, tts_text, tts_button, mic_aud],
        )

        mic_aud.change(
            fn=sync_mic_audio,
            inputs=[mic_aud],
            outputs=[aud],
        )

        down_active_gui = down_active_conf()
        down_info = gr.Markdown(
            f"Provide a link to a zip file, like this one: `https://huggingface.co/MrDawg/ToothBrushing/resolve/main/ToothBrushing.zip?download=true`, or separate links with a comma for the .pth and .index files, like this: `{test_model}`",
            visible=False
        )
        with gr.Row():
            with gr.Column(scale=3):
                down_url_gui = down_url_conf()
            with gr.Column(scale=1):
                down_button_gui = down_button_conf()

        with gr.Column():
            with gr.Row():
                model = model_conf()
                indx = index_conf()

        down_active_gui.change(
            show_components_down,
            [down_active_gui],
            [down_info, down_url_gui, down_button_gui]
        )

        down_button_gui.click(
            get_my_model,
            [down_url_gui],
            [model, indx]
        )

        with gr.Accordion(label="Advanced settings", open=False):
            algo = pitch_algo_conf()
            algo_lvl = pitch_lvl_conf()
            indx_inf = index_inf_conf()
            res_fc = respiration_filter_conf()
            envel_r = envelope_ratio_conf()
            const = consonant_protec_conf()
            steps_gui = steps_conf()
            format_out = format_output_gui()
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        denoise_gui = denoise_conf()
                        effects_gui = effects_conf()
                        player_gui = player_conf()

        player_audio = sound_gui()

        player_gui.change(
            fn=lambda val: gr.update(visible=val),
            inputs=[player_gui],
            outputs=[player_audio],
        )

        tts_button.click(
            fn=infer_tts_audio,
            inputs=[tts_voice, tts_text, player_gui],
            outputs=[aud, player_audio],
        )

        button_base = button_conf()
        output_base = output_conf()

        button_base.click(
            run,
            inputs=[
                aud,
                model,
                algo,
                algo_lvl,
                indx,
                indx_inf,
                res_fc,
                envel_r,
                const,
                denoise_gui,
                effects_gui,
                format_out,
                steps_gui,
            ],
            outputs=[output_base],
        ).success(
            clear_player,
            inputs=None,
            outputs=[player_audio],
            queue=False,
        ).success(
            load_first_audio,
            inputs=[output_base, player_gui],
            outputs=[player_audio],
        )

        gr.Examples(
            examples=[
                [
                    ["./test.ogg"],
                    "./model.pth",
                    "rmvpe+",
                    0,
                    "./model.index",
                    0.75,
                    3,
                    0.25,
                    0.50,
                ],
                [
                    ["./example2/test2.ogg"],
                    "./example2/model_link.txt",
                    "rmvpe+",
                    0,
                    "./example2/index_link.txt",
                    0.75,
                    3,
                    0.25,
                    0.50,
                ],
                [
                    ["./example3/test3.wav"],
                    "./example3/zip_link.txt",
                    "rmvpe+",
                    0,
                    None,
                    0.75,
                    3,
                    0.25,
                    0.50,
                ],

            ],
            fn=run,
            inputs=[
                aud,
                model,
                algo,
                algo_lvl,
                indx,
                indx_inf,
                res_fc,
                envel_r,
                const,
            ],
            outputs=[output_base],
            cache_examples=False,
        )
        gr.Markdown(RESOURCES)

    return app


if __name__ == "__main__":
    try:
        tts_voice_list = asyncio.new_event_loop().run_until_complete(get_voices_list(proxy=None))
        voices = sorted([
            (" - ".join(reversed(v["FriendlyName"].split("-"))).replace("Microsoft ", "").replace("Online (Natural)", f"({v['Gender']})").strip(), f"{v['ShortName']}-{v['Gender']}")
            for v in tts_voice_list
        ])
    except Exception as e:
        print(f"Warning: Could not retrieve online voices ({e}). Using default preset.")
        voices = [("English (United States) - EmmaMultilingual (Female)", "en-US-EmmaMultilingualNeural-Female")]
    
    app = get_gui(theme)

    app.queue(default_concurrency_limit=40)

    app.launch(
        max_threads=40,
        share=IS_COLAB,
        show_error=True,
        quiet=False,
        debug=IS_COLAB,
        ssr_mode=False,
    )