remove all but vr arch and return the fully edited script. The script is a gradio with multi tabs I only desire only the tab vr arch and its functionalities, delete the rest, don't bring anything.

import os
import sys
import subprocess
import re
import platform
import torch
import logging
import yt_dlp
import json
import copy
import spaces
import gradio as gr
import urllib.parse
import assets.themes.loadThemes as loadThemes
from audio_separator.separator import Separator
from assets.i18n.i18n import I18nAuto
from argparse import ArgumentParser
from assets.presence.discord_presence import RPCManager, track_presence

i18n = I18nAuto()

now_dir = os.getcwd()
sys.path.append(now_dir)
config_file = os.path.join(now_dir, "assets", "config.json")
models_file = os.path.join(now_dir, "assets", "models.json")
default_settings_file = os.path.join(now_dir, "assets", "default_settings.json")
custom_settings_file = os.path.join(now_dir, "assets", "custom_settings.json")

device = "cuda" if torch.cuda.is_available() else "cpu"
use_autocast = device == "cuda"

if os.path.isdir("env"):
    if platform.system() == "Windows":
        python_location = ".\\env\\python.exe"
        separator_location = ".\\env\\Scripts\\audio-separator.exe"
    elif platform.system() == "Linux":
        python_location = "env/bin/python"
        separator_location = "env/bin/audio-separator"
else:
    python_location = None
    separator_location = "audio-separator"

#=========================#
#     Roformer Models     #
#=========================#
roformer_models = {
    'BS-Roformer-Viperx-1297': 'model_bs_roformer_ep_317_sdr_12.9755.ckpt',
    'BS-Roformer-Viperx-1296': 'model_bs_roformer_ep_368_sdr_12.9628.ckpt',
    'BS-Roformer-Viperx-1053': 'model_bs_roformer_ep_937_sdr_10.5309.ckpt',
    'Mel-Roformer-Viperx-1143': 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt',
    'BS-Roformer-De-Reverb': 'deverb_bs_roformer_8_384dim_10depth.ckpt',
    'Mel-Roformer-Crowd-Aufr33-Viperx': 'mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt',
    'Mel-Roformer-Denoise-Aufr33': 'denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt',
    'Mel-Roformer-Denoise-Aufr33-Aggr' : 'denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt',
    'MelBand Roformer | Denoise-Debleed by Gabox' : 'mel_band_roformer_denoise_debleed_gabox.ckpt',
    'Mel-Roformer-Karaoke-Aufr33-Viperx': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt',
    'MelBand Roformer | Karaoke by Gabox' : 'mel_band_roformer_karaoke_gabox.ckpt',
    'MelBand Roformer | Karaoke by becruily' : 'mel_band_roformer_karaoke_becruily.ckpt',
    'MelBand Roformer | Vocals by Kimberley Jensen' : 'vocals_mel_band_roformer.ckpt',
    'MelBand Roformer Kim | FT by unwa' : 'mel_band_roformer_kim_ft_unwa.ckpt',
    'MelBand Roformer Kim | FT 2 by unwa' : 'mel_band_roformer_kim_ft2_unwa.ckpt',
    'MelBand Roformer Kim | FT 2 Bleedless by unwa' : 'mel_band_roformer_kim_ft2_bleedless_unwa.ckpt',
    'MelBand Roformer Kim | FT 3 by unwa' : 'mel_band_roformer_kim_ft3_unwa.ckpt',
    'MelBand Roformer Kim | Inst V1 by Unwa' : 'melband_roformer_inst_v1.ckpt',
    'MelBand Roformer Kim | Inst V1 Plus by Unwa' : 'melband_roformer_inst_v1_plus.ckpt',
    'MelBand Roformer Kim | Inst V1 (E) by Unwa' : 'melband_roformer_inst_v1e.ckpt',
    'MelBand Roformer Kim | Inst V1 (E) Plus by Unwa' : 'melband_roformer_inst_v1e_plus.ckpt',
    'MelBand Roformer Kim | Inst V2 by Unwa' : 'melband_roformer_inst_v2.ckpt',
    'MelBand Roformer Kim | InstVoc Duality V1 by Unwa' : 'melband_roformer_instvoc_duality_v1.ckpt',
    'MelBand Roformer Kim | InstVoc Duality V2 by Unwa' : 'melband_roformer_instvox_duality_v2.ckpt',
    'MelBand Roformer | Vocals by becruily' : 'mel_band_roformer_vocals_becruily.ckpt',
    'MelBand Roformer | Instrumental by becruily' : 'mel_band_roformer_instrumental_becruily.ckpt',
    'MelBand Roformer | Vocals Fullness by Aname' : 'mel_band_roformer_vocal_fullness_aname.ckpt',
    'BS Roformer | Vocals by Gabox' : 'bs_roformer_vocals_gabox.ckpt',
    'MelBand Roformer | Vocals by Gabox' : 'mel_band_roformer_vocals_gabox.ckpt',
    'MelBand Roformer | Vocals FV1 by Gabox' : 'mel_band_roformer_vocals_fv1_gabox.ckpt',
    'MelBand Roformer | Vocals FV2 by Gabox' : 'mel_band_roformer_vocals_fv2_gabox.ckpt',
    'MelBand Roformer | Vocals FV3 by Gabox' : 'mel_band_roformer_vocals_fv3_gabox.ckpt',
    'MelBand Roformer | Vocals FV4 by Gabox' : 'mel_band_roformer_vocals_fv4_gabox.ckpt',
    'MelBand Roformer | Instrumental by Gabox' : 'mel_band_roformer_instrumental_gabox.ckpt',
    'MelBand Roformer | Instrumental 2 by Gabox' : 'mel_band_roformer_instrumental_2_gabox.ckpt',
    'MelBand Roformer | Instrumental 3 by Gabox' : 'mel_band_roformer_instrumental_3_gabox.ckpt',
    'MelBand Roformer | Instrumental Bleedless V1 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v1_gabox.ckpt',
    'MelBand Roformer | Instrumental Bleedless V2 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v2_gabox.ckpt',
    'MelBand Roformer | Instrumental Bleedless V3 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v3_gabox.ckpt',
    'MelBand Roformer | Instrumental Fullness V1 by Gabox' : 'mel_band_roformer_instrumental_fullness_v1_gabox.ckpt',
    'MelBand Roformer | Instrumental Fullness V2 by Gabox' : 'mel_band_roformer_instrumental_fullness_v2_gabox.ckpt',
    'MelBand Roformer | Instrumental Fullness V3 by Gabox' : 'mel_band_roformer_instrumental_fullness_v3_gabox.ckpt',
    'MelBand Roformer | Instrumental Fullness Noisy V4 by Gabox' : 'mel_band_roformer_instrumental_fullness_noise_v4_gabox.ckpt',
    'MelBand Roformer | INSTV5 by Gabox' : 'mel_band_roformer_instrumental_instv5_gabox.ckpt',
    'MelBand Roformer | INSTV5N by Gabox' : 'mel_band_roformer_instrumental_instv5n_gabox.ckpt',
    'MelBand Roformer | INSTV6 by Gabox' : 'mel_band_roformer_instrumental_instv6_gabox.ckpt',
    'MelBand Roformer | INSTV6N by Gabox' : 'mel_band_roformer_instrumental_instv6n_gabox.ckpt',
    'MelBand Roformer | INSTV7 by Gabox' : 'mel_band_roformer_instrumental_instv7_gabox.ckpt',
    'MelBand Roformer | INSTV7N by Gabox' : 'mel_band_roformer_instrumental_instv7n_gabox.ckpt',
    'MelBand Roformer | INSTV8 by Gabox' : 'mel_band_roformer_instrumental_instv8_gabox.ckpt',
    'MelBand Roformer | INSTV8N by Gabox' : 'mel_band_roformer_instrumental_instv8n_gabox.ckpt',
    'MelBand Roformer | FVX by Gabox' : 'mel_band_roformer_instrumental_fvx_gabox.ckpt',
    'MelBand Roformer | De-Reverb by anvuew' : 'dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt',
    'MelBand Roformer | De-Reverb Less Aggressive by anvuew' : 'dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt',
    'MelBand Roformer | De-Reverb Mono by anvuew' : 'dereverb_mel_band_roformer_mono_anvuew.ckpt',
    'MelBand Roformer | De-Reverb Big by Sucial' : 'dereverb_big_mbr_ep_362.ckpt',
    'MelBand Roformer | De-Reverb Super Big by Sucial' : 'dereverb_super_big_mbr_ep_346.ckpt',
    'MelBand Roformer | De-Reverb-Echo by Sucial' : 'dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt',
    'MelBand Roformer | De-Reverb-Echo V2 by Sucial' : 'dereverb-echo_mel_band_roformer_sdr_13.4843_v2.ckpt',
    'MelBand Roformer | De-Reverb-Echo Fused by Sucial' : 'dereverb_echo_mbr_fused.ckpt',
    'MelBand Roformer Kim | SYHFT by SYH99999' : 'MelBandRoformerSYHFT.ckpt',
    'MelBand Roformer Kim | SYHFT V2 by SYH99999' : 'MelBandRoformerSYHFTV2.ckpt',
    'MelBand Roformer Kim | SYHFT V2.5 by SYH99999' : 'MelBandRoformerSYHFTV2.5.ckpt',
    'MelBand Roformer Kim | SYHFT V3 by SYH99999' : 'MelBandRoformerSYHFTV3Epsilon.ckpt',
    'MelBand Roformer Kim | Big SYHFT V1 by SYH99999' : 'MelBandRoformerBigSYHFTV1.ckpt',
    'MelBand Roformer Kim | Big Beta 4 FT by unwa' : 'melband_roformer_big_beta4.ckpt',
    'MelBand Roformer Kim | Big Beta 5e FT by unwa' : 'melband_roformer_big_beta5e.ckpt',
    'MelBand Roformer | Big Beta 6 by unwa' : 'melband_roformer_big_beta6.ckpt',
    'MelBand Roformer | Big Beta 6X by unwa' : 'melband_roformer_big_beta6x.ckpt',
    'BS Roformer | Chorus Male-Female by Sucial' : 'model_chorus_bs_roformer_ep_267_sdr_24.1275.ckpt',
    'BS Roformer | Male-Female by aufr33' : 'bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt',
    'MelBand Roformer | Aspiration by Sucial' : 'aspiration_mel_band_roformer_sdr_18.9845.ckpt',
    'MelBand Roformer | Aspiration Less Aggressive by Sucial' : 'aspiration_mel_band_roformer_less_aggr_sdr_18.1201.ckpt',
    'MelBand Roformer | Bleed Suppressor V1 by unwa-97chris' : 'mel_band_roformer_bleed_suppressor_v1.ckpt'
}

#=========================#
#      MDX23C Models      #
#=========================#
mdx23c_models = [
    'MDX23C_D1581.ckpt',
    'MDX23C-8KFFT-InstVoc_HQ.ckpt',
    'MDX23C-8KFFT-InstVoc_HQ_2.ckpt',
    'MDX23C-De-Reverb-aufr33-jarredou.ckpt',
    'MDX23C-DrumSep-aufr33-jarredou.ckpt'
]

#=========================#
#     MDXN-NET Models     #
#=========================#
mdxnet_models = [
    'UVR-MDX-NET-Inst_full_292.onnx',
    'UVR-MDX-NET_Inst_187_beta.onnx',
    'UVR-MDX-NET_Inst_82_beta.onnx',
    'UVR-MDX-NET_Inst_90_beta.onnx',
    'UVR-MDX-NET_Main_340.onnx',
    'UVR-MDX-NET_Main_390.onnx',
    'UVR-MDX-NET_Main_406.onnx',
    'UVR-MDX-NET_Main_427.onnx',
    'UVR-MDX-NET_Main_438.onnx',
    'UVR-MDX-NET-Inst_HQ_1.onnx',
    'UVR-MDX-NET-Inst_HQ_2.onnx',
    'UVR-MDX-NET-Inst_HQ_3.onnx',
    'UVR-MDX-NET-Inst_HQ_4.onnx',
    'UVR-MDX-NET-Inst_HQ_5.onnx',
    'UVR_MDXNET_Main.onnx',
    'UVR-MDX-NET-Inst_Main.onnx',
    'UVR_MDXNET_1_9703.onnx',
    'UVR_MDXNET_2_9682.onnx',
    'UVR_MDXNET_3_9662.onnx',
    'UVR-MDX-NET-Inst_1.onnx',
    'UVR-MDX-NET-Inst_2.onnx',
    'UVR-MDX-NET-Inst_3.onnx',
    'UVR_MDXNET_KARA.onnx',
    'UVR_MDXNET_KARA_2.onnx',
    'UVR_MDXNET_9482.onnx',
    'UVR-MDX-NET-Voc_FT.onnx',
    'Kim_Vocal_1.onnx',
    'Kim_Vocal_2.onnx',
    'Kim_Inst.onnx',
    'Reverb_HQ_By_FoxJoy.onnx',
    'UVR-MDX-NET_Crowd_HQ_1.onnx',
    'kuielab_a_vocals.onnx',
    'kuielab_a_other.onnx',
    'kuielab_a_bass.onnx',
    'kuielab_a_drums.onnx',
    'kuielab_b_vocals.onnx',
    'kuielab_b_other.onnx',
    'kuielab_b_bass.onnx',
    'kuielab_b_drums.onnx',
]

#========================#
#     VR-ARCH Models     #
#========================#
vrarch_models = [
    '1_HP-UVR.pth',
    '2_HP-UVR.pth',
    '3_HP-Vocal-UVR.pth',
    '4_HP-Vocal-UVR.pth',
    '5_HP-Karaoke-UVR.pth',
    '6_HP-Karaoke-UVR.pth',
    '7_HP2-UVR.pth',
    '8_HP2-UVR.pth',
    '9_HP2-UVR.pth',
    '10_SP-UVR-2B-32000-1.pth',
    '11_SP-UVR-2B-32000-2.pth',
    '12_SP-UVR-3B-44100.pth',
    '13_SP-UVR-4B-44100-1.pth',
    '14_SP-UVR-4B-44100-2.pth',
    '15_SP-UVR-MID-44100-1.pth',
    '16_SP-UVR-MID-44100-2.pth',
    '17_HP-Wind_Inst-UVR.pth',
    'UVR-De-Echo-Aggressive.pth',
    'UVR-De-Echo-Normal.pth',
    'UVR-DeEcho-DeReverb.pth',
    'UVR-De-Reverb-aufr33-jarredou.pth',
    'UVR-DeNoise-Lite.pth',
    'UVR-DeNoise.pth',
    'UVR-BVE-4B_SN-44100-1.pth',
    'MGM_HIGHEND_v4.pth',
    'MGM_LOWEND_A_v4.pth',
    'MGM_LOWEND_B_v4.pth',
    'MGM_MAIN_v4.pth',
]

#=======================#
#     DEMUCS Models     #
#=======================#
demucs_models = [
    'htdemucs_ft.yaml',
    'htdemucs_6s.yaml',
    'htdemucs.yaml',
    'hdemucs_mmi.yaml',
]

output_format = [
    'wav',
    'flac',
    'mp3',
    'ogg',
    'opus',
    'm4a',
    'aiff',
    'ac3'
]

found_files = []
logs = []
out_dir = "./outputs"
models_dir = "./models"
extensions = (".wav", ".flac", ".mp3", ".ogg", ".opus", ".m4a", ".aiff", ".ac3")

def load_config_presence():
    with open(config_file, "r", encoding="utf8") as file:
        config = json.load(file)
        return config["discord_presence"]

def initialize_presence():
    if load_config_presence():
        RPCManager.start_presence()

initialize_presence()

def download_audio(url, output_dir="ytdl"):

    os.makedirs(output_dir, exist_ok=True)

    ydl_opts = {
        'format': 'bestaudio/best',
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'wav',
            'preferredquality': '32',
        }],
        'outtmpl': os.path.join(output_dir, '%(title)s.%(ext)s'),
        'postprocessor_args': [
            '-acodec', 'pcm_f32le'
        ],
    }

    try:
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            info = ydl.extract_info(url, download=False)
            video_title = info['title']

            ydl.download([url])

            file_path = os.path.join(output_dir, f"{video_title}.wav")

            if os.path.exists(file_path):
                return os.path.abspath(file_path)
            else:
                raise Exception("Something went wrong")

    except Exception as e:
        raise Exception(f"Error extracting audio with yt-dlp: {str(e)}")

def leaderboard(list_filter):
    try:
        if python_location:
            command = [python_location, separator_location, "-l", f"--list_filter={list_filter}"]
        else:
            command = [separator_location, "-l", f"--list_filter={list_filter}"]

        result = subprocess.run(
            command,
            capture_output=True,
            text=True,
        )
        if result.returncode != 0:
            return f"Error: {result.stderr}"

        return "<table border='1'>" + "".join(
            f"<tr style='{'font-weight: bold; font-size: 1.2em;' if i == 0 else ''}'>" +
            "".join(f"<td>{cell}</td>" for cell in re.split(r"\s{2,}", line.strip())) +
            "</tr>"
            for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE))
        ) + "</table>"

    except Exception as e:
        return f"Error: {e}"
    
def get_language_settings():
    with open(config_file, "r", encoding="utf8") as file:
        config = json.load(file)

    if config["lang"]["override"] == False:
        return "Language automatically detected by system"
    else:
        return config["lang"]["selected_lang"]
    
def save_lang_settings(selected_language):
    with open(config_file, "r", encoding="utf8") as file:
        config = json.load(file)

    if selected_language == "Language automatically detected by system":
        config["lang"]["override"] = False
    else:
        config["lang"]["override"] = True
        config["lang"]["selected_lang"] = selected_language

    gr.Info(i18n("Language have been saved. Restart UVR5 UI to apply the changes"))

    with open(config_file, "w", encoding="utf8") as file:
        json.dump(config, file, indent=2)

def alternative_model_downloader(method, key, output_dir="models", progress=gr.Progress()):
    logs.clear()

    with open(models_file, 'r', encoding='utf-8') as file:
        model_data = json.load(file)
    
    if key not in model_data:
        return f"Model '{key}' cannot be found."
    
    total_files = len(model_data[key])
    progress(0, desc="Starting downloads...")

    for i, url in enumerate(model_data[key]):
        filename = os.path.basename(urllib.parse.urlparse(url).path)
        full_name = os.path.join(output_dir, filename)

        if os.path.exists(full_name):
            logs.append(f"{filename} already exists.")
            continue

        progress((i + 0.1) / total_files, desc=f"Starting download of {filename} ({i+1}/{total_files})")

        if method == 'wget':
            cmd = ['wget', '--progress=bar:force', '-O', full_name, url]
        elif method == 'curl':
            cmd = ['curl', '-L', '-#', '-o', full_name, url]

        try:
            process = subprocess.Popen(
                cmd, 
                stdout=subprocess.PIPE, 
                stderr=subprocess.PIPE,
                universal_newlines=True,
                bufsize=1
            )
            
            for line in process.stderr:
                if method == 'wget' and '%' in line:
                    try:
                        percent = int(line.strip().split('%')[0].split()[-1])
                        file_progress = percent / 100.0
                        total_progress = (i + file_progress) / total_files
                        progress(total_progress, desc=f"File {i+1}/{total_files}: {filename} ({percent}%)")
                    except (ValueError, IndexError):
                        pass
                elif method == 'curl' and '##' in line:
                    try:
                        hash_count = line.count('#')
                        file_progress = min(hash_count / 50.0, 1.0)
                        total_progress = (i + file_progress) / total_files
                        percent = int(file_progress * 100)
                        progress(total_progress, desc=f"File {i+1}/{total_files}: {filename} ({percent}%)")
                    except Exception:
                        pass
            
            process.wait()
            if process.returncode != 0:
                logs.append(f"Error downloading {filename}")
            else:
                logs.append(f"{filename} downloaded successfully!")
                progress((i + 1) / total_files, desc=f"File {i+1}/{total_files} completed")
        
        except Exception as e:
            logs.append(f"Error running download command: {str(e)}")
    
    progress(1.0, desc="Download process completed")
    return "\n".join(logs)

def read_main_config():
    try:
        with open(config_file, "r", encoding="utf8") as f:
            return json.load(f)
    except Exception as e:
        print(f"Error reading main config file '{config_file}': {e}")
        gr.Warning(i18n("Error reading main config file"))
    
def write_main_config(data):
    try:
        with open(config_file, "w", encoding="utf8") as f:
            json.dump(data, f, indent=2)
    except Exception as e:
        print(f"Error writing to main config file '{config_file}': {e}")
        gr.Warning(i18n("Error writing to main config file"))

def load_settings_from_file(filepath):
    try:
        with open(filepath, 'r', encoding='utf-8') as f:
            return json.load(f)
    except Exception as e:
        print(f"Error reading settings file '{filepath}': {e}")
        gr.Warning(i18n("Error reading settings file"))
        return None
    
def get_initial_settings():
    main_config = read_main_config()
    load_custom = main_config.get('load_custom_settings', False)

    settings_to_load = {}
    default_settings = load_settings_from_file(default_settings_file)

    if load_custom:
        print("Attempting to load custom settings...")
        custom_settings = load_settings_from_file(custom_settings_file)
        if custom_settings:
            settings_to_load = copy.deepcopy(default_settings)
            for section, params in custom_settings.items():
                if section in settings_to_load:
                    for key, value in params.items():
                        settings_to_load[section][key] = value
                else:
                    settings_to_load[section] = params
            print("Custom settings loaded successfully.")
        else:
            print("Custom settings file not found or invalid. Falling back to default settings.")
            settings_to_load = default_settings
    else:
        print("Loading default settings...")
        settings_to_load = default_settings

    return settings_to_load

initial_settings = get_initial_settings()

def get_all_components(components_dict):
    all_comps = []
    for section in components_dict.values():
        all_comps.extend(section.values())
    return all_comps

def save_current_settings(*values):
    global components
    try:
        current_config_data = {}
        value_index = 0
        for section_name, section_comps in components.items():
            current_config_data[section_name] = {}
            for comp_name in section_comps.keys():
                current_config_data[section_name][comp_name] = values[value_index]
                value_index += 1

        with open(custom_settings_file, 'w', encoding='utf-8') as f:
            json.dump(current_config_data, f, indent=4)

        main_config = read_main_config()
        main_config['load_custom_settings'] = True
        write_main_config(main_config)
        gr.Info(i18n("Current settings saved successfully! They will be loaded next time"))
    except Exception as e:
        print(f"Error saving settings: {e}")
        gr.Warning(i18n("Error saving settings"))

def reset_settings_to_default():
    global components, default_settings_file
    updates = []
    all_comps_flat = get_all_components(components)
    try:
        default_settings = load_settings_from_file(default_settings_file)
        for section_name, section_comps in components.items():
            for comp_name, comp_instance in section_comps.items():
                default_value = default_settings.get(section_name, {}).get(comp_name, None)

                if isinstance(comp_instance, gr.Dropdown) and hasattr(comp_instance, 'choices') and default_value is not None:
                    if default_value not in comp_instance.choices:
                        print(f"Warning: Default value '{default_value}' for '{comp_name}' not in choices {comp_instance.choices}. Setting to None.")
                        default_value = None

                updates.append(gr.update(value=default_value))

        main_config = read_main_config()
        main_config['load_custom_settings'] = False
        write_main_config(main_config)

        gr.Info(i18n("Settings reset to default. Default settings will be loaded next time"))
        return updates

    except Exception as e:
        print(f"Error resetting settings: {e}")
        gr.Warning(i18n("Error resetting settings"))
        return [gr.update() for _ in all_comps_flat]

components = {
    "Roformer": {}, "MDX23C": {}, "MDX-NET": {}, "VR Arch": {}, "Demucs": {}
}

@track_presence("Performing BS/Mel Roformer Separation")
@spaces.GPU(duration=60)
def roformer_separator(audio, model_key, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)):
    roformer_model = roformer_models[model_key]
    model_path = os.path.join(models_dir, roformer_model)
    try:
        if not os.path.exists(model_path):
            gr.Info(f"This is the first time the {model_key} model is being used. The separation will take a little longer because the model needs to be downloaded.")
        
        separator = Separator(
            log_level=logging.WARNING,
            model_file_dir=models_dir,
            output_dir=out_dir,
            output_format=out_format,
            use_autocast=use_autocast,
            normalization_threshold=norm_thresh,
            amplification_threshold=amp_thresh,
            output_single_stem=single_stem,
            mdxc_params={
                "segment_size": segment_size,
                "override_model_segment_size": override_seg_size,
                "batch_size": batch_size,
                "overlap": overlap,
            }
        )
    
        progress(0.2, desc="Loading model...")
        separator.load_model(model_filename=roformer_model)

        progress(0.7, desc="Separating audio...")
        separation = separator.separate(audio)

        stems = [os.path.join(out_dir, file_name) for file_name in separation]

        if single_stem.strip():
            return stems[0], None
        
        return stems[0], stems[1]
    
    except Exception as e:
        raise RuntimeError(f"Roformer separation failed: {e}") from e

@track_presence("Performing MDXC Separationn")
@spaces.GPU(duration=60)
def mdxc_separator(audio, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)):
    model_path = os.path.join(models_dir, model)
    try:
        if not os.path.exists(model_path):
            gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")

        separator = Separator(
            log_level=logging.WARNING,
            model_file_dir=models_dir,
            output_dir=out_dir,
            output_format=out_format,
            use_autocast=use_autocast,
            normalization_threshold=norm_thresh,
            amplification_threshold=amp_thresh,
            output_single_stem=single_stem,
            mdxc_params={
                "segment_size": segment_size,
                "override_model_segment_size": override_seg_size,
                "batch_size": batch_size,
                "overlap": overlap,
            }
        )

        progress(0.2, desc="Loading model...")
        separator.load_model(model_filename=model)

        progress(0.7, desc="Separating audio...")
        separation = separator.separate(audio)

        stems = [os.path.join(out_dir, file_name) for file_name in separation]
        
        if single_stem.strip():
            return stems[0], None
        
        return stems[0], stems[1]

    except Exception as e:
        raise RuntimeError(f"MDX23C separation failed: {e}") from e

@track_presence("Performing MDX-NET Separation")
@spaces.GPU(duration=60)
def mdxnet_separator(audio, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)):
    model_path = os.path.join(models_dir, model)
    try:
        if not os.path.exists(model_path):
            gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")

        separator = Separator(
            log_level=logging.WARNING,
            model_file_dir=models_dir,
            output_dir=out_dir,
            output_format=out_format,
            use_autocast=use_autocast,
            normalization_threshold=norm_thresh,
            amplification_threshold=amp_thresh,
            output_single_stem=single_stem,
            mdx_params={
                "hop_length": hop_length,
                "segment_size": segment_size,
                "overlap": overlap,
                "batch_size": batch_size,
                "enable_denoise": denoise,
            }
        )

        progress(0.2, desc="Loading model...")
        separator.load_model(model_filename=model)

        progress(0.7, desc="Separating audio...")
        separation = separator.separate(audio)

        stems = [os.path.join(out_dir, file_name) for file_name in separation]
        
        if single_stem.strip():
            return stems[0], None
        
        return stems[0], stems[1]

    except Exception as e:
        raise RuntimeError(f"MDX-NET separation failed: {e}") from e

@track_presence("Performing VR Arch Separation")
@spaces.GPU(duration=60)
def vrarch_separator(audio, model, out_format, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)):
    model_path = os.path.join(models_dir, model)
    try:
        if not os.path.exists(model_path):
            gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")

        separator = Separator(
            log_level=logging.WARNING,
            model_file_dir=models_dir,
            output_dir=out_dir,
            output_format=out_format,
            use_autocast=use_autocast,
            normalization_threshold=norm_thresh,
            amplification_threshold=amp_thresh,
            output_single_stem=single_stem,
            vr_params={
                "batch_size": batch_size,
                "window_size": window_size,
                "aggression": aggression,
                "enable_tta": tta,
                "enable_post_process": post_process,
                "post_process_threshold": post_process_threshold,
                "high_end_process": high_end_process,
            }
        )

        progress(0.2, desc="Loading model...")
        separator.load_model(model_filename=model)

        progress(0.7, desc="Separating audio...")
        separation = separator.separate(audio)

        stems = [os.path.join(out_dir, file_name) for file_name in separation]
        
        if single_stem.strip():
            return stems[0], None
        
        return stems[0], stems[1]

    except Exception as e:
        raise RuntimeError(f"VR ARCH separation failed: {e}") from e

@track_presence("Performing Demucs Separation")
@spaces.GPU(duration=60)
def demucs_separator(audio, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress(track_tqdm=True)):
    model_path = os.path.join(models_dir, model)
    try:
        if not os.path.exists(model_path):
            gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")

        separator = Separator(
            log_level=logging.WARNING,
            model_file_dir=models_dir,
            output_dir=out_dir,
            output_format=out_format,
            use_autocast=use_autocast,
            normalization_threshold=norm_thresh,
            amplification_threshold=amp_thresh,
            demucs_params={
                "batch_size": batch_size,
                "segment_size": segment_size,
                "shifts": shifts,
                "overlap": overlap,
                "segments_enabled": segments_enabled,
            }
        )

        progress(0.2, desc="Loading model...")
        separator.load_model(model_filename=model)

        progress(0.7, desc="Separating audio...")
        separation = separator.separate(audio)

        stems = [os.path.join(out_dir, file_name) for file_name in separation]
        
        if model == "htdemucs_6s.yaml":
            return stems[0], stems[1], stems[2], stems[3], stems[4], stems[5]
        else:
            return stems[0], stems[1], stems[2], stems[3], None, None

    except Exception as e:
        raise RuntimeError(f"Demucs separation failed: {e}") from e

def update_stems(model):
    if model == "htdemucs_6s.yaml":
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)

@track_presence("Performing BS/Mel Roformer Batch Separation")
@spaces.GPU(duration=60)
def roformer_batch(path_input, path_output, model_key, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()):
    found_files.clear()
    logs.clear()
    roformer_model = roformer_models[model_key]
    model_path = os.path.join(models_dir, roformer_model)

    if not os.path.exists(model_path):
        gr.Info(f"This is the first time the {model_key} model is being used. The separation will take a little longer because the model needs to be downloaded.")

    for audio_files in os.listdir(path_input):
        if audio_files.endswith(extensions):
            found_files.append(audio_files)
    total_files = len(found_files)

    if total_files == 0:
        logs.append("No valid audio files.")
        return "\n".join(logs)
    else:
        logs.append(f"{total_files} audio files found")
        found_files.sort()
        progress(0, desc="Starting processing...")

        for i, audio_files in enumerate(found_files):
            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
            file_path = os.path.join(path_input, audio_files)
            try:
                separator = Separator(
                    log_level=logging.WARNING,
                    model_file_dir=models_dir,
                    output_dir=path_output,
                    output_format=out_format,
                    use_autocast=use_autocast,
                    normalization_threshold=norm_thresh,
                    amplification_threshold=amp_thresh,
                    output_single_stem=single_stem,
                    mdxc_params={
                        "segment_size": segment_size,
                        "override_model_segment_size": override_seg_size,
                        "batch_size": batch_size,
                        "overlap": overlap,
                    }
                )

                logs.append("Loading model...")
                separator.load_model(model_filename=roformer_model)

                logs.append(f"Separating file: {audio_files}")
                separator.separate(file_path)
                logs.append(f"File: {audio_files} separated!")
            except Exception as e:
                raise RuntimeError(f"BS/Mel Roformer batch separation failed: {e}") from e
        
        progress(1.0, desc="Processing complete")
        return "\n".join(logs)

@track_presence("Performing MDXC Batch Separation")
@spaces.GPU(duration=60)
def mdx23c_batch(path_input, path_output, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()):
    found_files.clear()
    logs.clear()
    model_path = os.path.join(models_dir, model)

    if not os.path.exists(model_path):
        gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")

    for audio_files in os.listdir(path_input):
        if audio_files.endswith(extensions):
            found_files.append(audio_files)
    total_files = len(found_files)

    if total_files == 0:
        logs.append("No valid audio files.")
        return "\n".join(logs)
    else:
        logs.append(f"{total_files} audio files found")
        found_files.sort()
        progress(0, desc="Starting processing...")

        for i, audio_files in enumerate(found_files):
            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
            file_path = os.path.join(path_input, audio_files)
            try:
                separator = Separator(
                    log_level=logging.WARNING,
                    model_file_dir=models_dir,
                    output_dir=path_output,
                    output_format=out_format,
                    use_autocast=use_autocast,
                    normalization_threshold=norm_thresh,
                    amplification_threshold=amp_thresh,
                    output_single_stem=single_stem,
                    mdxc_params={
                        "segment_size": segment_size,
                        "override_model_segment_size": override_seg_size,
                        "batch_size": batch_size,
                        "overlap": overlap,
                    }
                )

                logs.append("Loading model...")
                separator.load_model(model_filename=model)

                logs.append(f"Separating file: {audio_files}")
                separator.separate(file_path)
                logs.append(f"File: {audio_files} separated!")
            except Exception as e:
                raise RuntimeError(f"MDXC batch separation failed: {e}") from e
        
        progress(1.0, desc="Processing complete")
        return "\n".join(logs)

@track_presence("Performing MDX-NET Batch Separation")
@spaces.GPU(duration=60)
def mdxnet_batch(path_input, path_output, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()):
    found_files.clear()
    logs.clear()
    model_path = os.path.join(models_dir, model)

    if not os.path.exists(model_path):
        gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")

    for audio_files in os.listdir(path_input):
        if audio_files.endswith(extensions):
            found_files.append(audio_files)
    total_files = len(found_files)

    if total_files == 0:
        logs.append("No valid audio files.")
        return "\n".join(logs)
    else:
        logs.append(f"{total_files} audio files found")
        found_files.sort()
        progress(0, desc="Starting processing...")

        for i, audio_files in enumerate(found_files):
            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
            file_path = os.path.join(path_input, audio_files)
            try:
                separator = Separator(
                    log_level=logging.WARNING,
                    model_file_dir=models_dir,
                    output_dir=path_output,
                    output_format=out_format,
                    use_autocast=use_autocast,
                    normalization_threshold=norm_thresh,
                    amplification_threshold=amp_thresh,
                    output_single_stem=single_stem,
                    mdx_params={
                        "hop_length": hop_length,
                        "segment_size": segment_size,
                        "overlap": overlap,
                        "batch_size": batch_size,
                        "enable_denoise": denoise,
                    }
                )

                logs.append("Loading model...")
                separator.load_model(model_filename=model)

                logs.append(f"Separating file: {audio_files}")
                separator.separate(file_path)
                logs.append(f"File: {audio_files} separated!")
            except Exception as e:
                raise RuntimeError(f"MDX-NET batch separation failed: {e}") from e
            
        progress(1.0, desc="Processing complete")
        return "\n".join(logs)

@track_presence("Performing VR Arch Batch Separation")
@spaces.GPU(duration=60)
def vrarch_batch(path_input, path_output, model, out_format, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()):
    found_files.clear()
    logs.clear()
    model_path = os.path.join(models_dir, model)

    if not os.path.exists(model_path):
        gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")

    for audio_files in os.listdir(path_input):
        if audio_files.endswith(extensions):
            found_files.append(audio_files)
    total_files = len(found_files)

    if total_files == 0:
        logs.append("No valid audio files.")
        return "\n".join(logs)
    else:
        logs.append(f"{total_files} audio files found")
        found_files.sort()
        progress(0, desc="Starting processing...")

        for i, audio_files in enumerate(found_files):
            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
            file_path = os.path.join(path_input, audio_files)
            try:
                separator = Separator(
                    log_level=logging.WARNING,
                    model_file_dir=models_dir,
                    output_dir=path_output,
                    output_format=out_format,
                    use_autocast=use_autocast,
                    normalization_threshold=norm_thresh,
                    amplification_threshold=amp_thresh,
                    output_single_stem=single_stem,
                    vr_params={
                        "batch_size": batch_size,
                        "window_size": window_size,
                        "aggression": aggression,
                        "enable_tta": tta,
                        "enable_post_process": post_process,
                        "post_process_threshold": post_process_threshold,
                        "high_end_process": high_end_process,
                    }
                )

                logs.append("Loading model...")
                separator.load_model(model_filename=model)

                logs.append(f"Separating file: {audio_files}")
                separator.separate(file_path)
                logs.append(f"File: {audio_files} separated!")
            except Exception as e:
                raise RuntimeError(f"VR Arch batch separation failed: {e}") from e
            
        progress(1.0, desc="Processing complete")
        return "\n".join(logs)

@track_presence("Performing Demucs Batch Separation")
@spaces.GPU(duration=60)
def demucs_batch(path_input, path_output, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress()):
    found_files.clear()
    logs.clear()
    model_path = os.path.join(models_dir, model)

    if not os.path.exists(model_path):
        gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.")

    for audio_files in os.listdir(path_input):
        if audio_files.endswith(extensions):
            found_files.append(audio_files)
    total_files = len(found_files)

    if total_files == 0:
        logs.append("No valid audio files.")
        return "\n".join(logs)
    else:
        logs.append(f"{total_files} audio files found")
        found_files.sort()
        progress(0, desc="Starting processing...")

        for i, audio_files in enumerate(found_files):
            progress((i / total_files), desc=f"Processing file {i+1}/{total_files}")
            file_path = os.path.join(path_input, audio_files)
            try:
                separator = Separator(
                    log_level=logging.WARNING,
                    model_file_dir=models_dir,
                    output_dir=path_output,
                    output_format=out_format,
                    use_autocast=use_autocast,
                    normalization_threshold=norm_thresh,
                    amplification_threshold=amp_thresh,
                    demucs_params={
                        "batch_size": batch_size,
                        "segment_size": segment_size,
                        "shifts": shifts,
                        "overlap": overlap,
                        "segments_enabled": segments_enabled,
                    }
                )

                logs.append("Loading model...")
                separator.load_model(model_filename=model)

                logs.append(f"Separating file: {audio_files}")
                separator.separate(file_path)
                logs.append(f"File: {audio_files} separated!")
            except Exception as e:
                raise RuntimeError(f"Demucs batch separation failed: {e}") from e
            
        progress(1.0, desc="Processing complete")
        return "\n".join(logs)
            
with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 UVR5 UI 🎵") as app:
    gr.Markdown("<h1> 🎵 UVR5 UI 🎵 </h1>")
    gr.Markdown(i18n("If you liked this HF Space you can give me a ❤️"))
    gr.Markdown(i18n("Try UVR5 UI using Colab [here](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)"))
    all_configurable_inputs = []
    with gr.Tabs():
        with gr.TabItem("BS/Mel Roformer"):
            with gr.Row():
                roformer_model = gr.Dropdown(
                    label = i18n("Select the model"),
                    choices = list(roformer_models.keys()),
                    value = initial_settings.get("Roformer", {}).get("model", None),
                    interactive = True
                )
                roformer_output_format = gr.Dropdown(
                    label = i18n("Select the output format"),
                    choices = output_format,
                    value = initial_settings.get("Roformer", {}).get("output_format", None),
                    interactive = True
                )
            with gr.Accordion(i18n("Advanced settings"), open = False):
                with gr.Group():
                    with gr.Row():
                        roformer_segment_size = gr.Slider(
                            label = i18n("Segment size"),
                            info = i18n("Larger consumes more resources, but may give better results"),
                            minimum = 32,
                            maximum = 4000,
                            step = 32,
                            value = initial_settings.get("Roformer", {}).get("segment_size", 256),
                            interactive = True
                        )
                        roformer_override_segment_size = gr.Checkbox(
                            label = i18n("Override segment size"),
                            info = i18n("Override model default segment size instead of using the model default value"),
                            value = initial_settings.get("Roformer", {}).get("override_segment_size", False),
                            interactive = True
                        )
                    with gr.Row():
                        roformer_overlap = gr.Slider(
                            label = i18n("Overlap"),
                            info = i18n("Amount of overlap between prediction windows"),
                            minimum = 2,
                            maximum = 10,
                            step = 1,
                            value = initial_settings.get("Roformer", {}).get("overlap", 8),
                            interactive = True
                        )
                        roformer_batch_size = gr.Slider(
                            label = i18n("Batch size"),
                            info = i18n("Larger consumes more RAM but may process slightly faster"),
                            minimum = 1,
                            maximum = 16,
                            step = 1,
                            value = initial_settings.get("Roformer", {}).get("batch_size", 1),
                            interactive = True
                        )
                    with gr.Row():
                        roformer_normalization_threshold = gr.Slider(
                            label = i18n("Normalization threshold"),
                            info = i18n("The threshold for audio normalization"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("Roformer", {}).get("normalization_threshold", 0.9),
                            interactive = True
                        )
                        roformer_amplification_threshold = gr.Slider(
                            label = i18n("Amplification threshold"),
                            info = i18n("The threshold for audio amplification"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("Roformer", {}).get("amplification_threshold", 0.7),
                            interactive = True
                        )
                    with gr.Row():
                        roformer_single_stem = gr.Textbox(
                            label = i18n("Output only single stem"),
                            placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
                            value = initial_settings.get("Roformer", {}).get("single_stem", ""),
                            interactive = True
                        )

                    components["Roformer"] = {
                        "model": roformer_model,
                        "output_format": roformer_output_format,
                        "segment_size": roformer_segment_size,
                        "override_segment_size": roformer_override_segment_size,
                        "overlap": roformer_overlap,
                        "batch_size": roformer_batch_size,
                        "normalization_threshold": roformer_normalization_threshold,
                        "amplification_threshold": roformer_amplification_threshold,
                        "single_stem": roformer_single_stem
                    }
                    all_configurable_inputs.extend(components["Roformer"].values())

            with gr.Row():
                roformer_audio = gr.Audio(
                    label = i18n("Input audio"),
                    type = "filepath",
                    interactive = True
                )
            with gr.Accordion(i18n("Separation by link"), open = False):
                with gr.Row():
                    roformer_link = gr.Textbox(
                        label = i18n("Link"),
                        placeholder = i18n("Paste the link here"),
                        interactive = True
                    )
                with gr.Row():
                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
                with gr.Row():
                    roformer_download_button = gr.Button(
                        i18n("Download!"),
                        variant = "primary"
                    )

            roformer_download_button.click(download_audio, [roformer_link], [roformer_audio])

            with gr.Accordion(i18n("Batch separation"), open = False):
                with gr.Row():
                    roformer_input_path = gr.Textbox(
                        label = i18n("Input path"),
                        placeholder = i18n("Place the input path here"),
                        interactive = True
                    )
                    roformer_output_path = gr.Textbox(
                        label = i18n("Output path"),
                        placeholder = i18n("Place the output path here"),
                        interactive = True
                    )
                with gr.Row():
                    roformer_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
                with gr.Row():
                    roformer_info = gr.Textbox(
                        label = i18n("Output information"),
                        interactive = False
                    )

            roformer_bath_button.click(roformer_batch, [roformer_input_path, roformer_output_path, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_info])

            with gr.Row():
                roformer_button = gr.Button(i18n("Separate!"), variant = "primary")
            with gr.Row():
                roformer_stem1 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    label = i18n("Stem 1"),
                    type = "filepath"
                )
                roformer_stem2 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    label = i18n("Stem 2"),
                    type = "filepath"
                )

            roformer_button.click(roformer_separator, [roformer_audio, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_stem1, roformer_stem2])

        with gr.TabItem("MDX23C"):
            with gr.Row():
                mdx23c_model = gr.Dropdown(
                    label = i18n("Select the model"),
                    choices = mdx23c_models,
                    value = initial_settings.get("MDX23C", {}).get("model", None),
                    interactive = True
                )
                mdx23c_output_format = gr.Dropdown(
                    label = i18n("Select the output format"),
                    choices = output_format,
                    value = initial_settings.get("MDX23C", {}).get("output_format", None),
                    interactive = True
                )
            with gr.Accordion(i18n("Advanced settings"), open = False):
                with gr.Group():
                    with gr.Row():
                        mdx23c_segment_size = gr.Slider(
                            minimum = 32,
                            maximum = 4000,
                            step = 32,
                            label = i18n("Segment size"),
                            info = i18n("Larger consumes more resources, but may give better results"),
                            value = initial_settings.get("MDX23C", {}).get("segment_size", 256),
                            interactive = True
                        )
                        mdx23c_override_segment_size = gr.Checkbox(
                            label = i18n("Override segment size"),
                            info = i18n("Override model default segment size instead of using the model default value"),
                            value = initial_settings.get("MDX23C", {}).get("override_segment_size", False),
                            interactive = True
                        )
                    with gr.Row():
                        mdx23c_overlap = gr.Slider(
                            minimum = 2,
                            maximum = 50,
                            step = 1,
                            label = i18n("Overlap"),
                            info = i18n("Amount of overlap between prediction windows"),
                            value = initial_settings.get("MDX23C", {}).get("overlap", 8),
                            interactive = True
                        )
                        mdx23c_batch_size = gr.Slider(
                            label = i18n("Batch size"),
                            info = i18n("Larger consumes more RAM but may process slightly faster"),
                            minimum = 1,
                            maximum = 16,
                            step = 1,
                            value = initial_settings.get("MDX23C", {}).get("batch_size", 1),
                            interactive = True
                        )
                    with gr.Row():
                        mdx23c_normalization_threshold = gr.Slider(
                            label = i18n("Normalization threshold"),
                            info = i18n("The threshold for audio normalization"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("MDX23C", {}).get("normalization_threshold", 0.9),
                            interactive = True
                        )
                        mdx23c_amplification_threshold = gr.Slider(
                            label = i18n("Amplification threshold"),
                            info = i18n("The threshold for audio amplification"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("MDX23C", {}).get("amplification_threshold", 0.7),
                            interactive = True
                        )
                    with gr.Row():
                        mdx23c_single_stem = gr.Textbox(
                            label = i18n("Output only single stem"),
                            placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
                            value = initial_settings.get("MDX23C", {}).get("single_stem", ""),
                            interactive = True
                        )

                    components["MDX23C"] = {
                        "model": mdx23c_model,
                        "output_format": mdx23c_output_format,
                        "segment_size": mdx23c_segment_size,
                        "override_segment_size": mdx23c_override_segment_size,
                        "overlap": mdx23c_overlap,
                        "batch_size": mdx23c_batch_size,
                        "normalization_threshold": mdx23c_normalization_threshold,
                        "amplification_threshold": mdx23c_amplification_threshold,
                        "single_stem": mdx23c_single_stem
                    }
                    all_configurable_inputs.extend(components["MDX23C"].values())

            with gr.Row():
                mdx23c_audio = gr.Audio(
                    label = i18n("Input audio"),
                    type = "filepath",
                    interactive = True
                )
            with gr.Accordion(i18n("Separation by link"), open = False):
                with gr.Row():
                    mdx23c_link = gr.Textbox(
                        label = i18n("Link"),
                        placeholder = i18n("Paste the link here"),
                        interactive = True
                    )
                with gr.Row():
                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
                with gr.Row():
                    mdx23c_download_button = gr.Button(
                        i18n("Download!"),
                        variant = "primary"
                    )

            mdx23c_download_button.click(download_audio, [mdx23c_link], [mdx23c_audio])

            with gr.Accordion(i18n("Batch separation"), open = False):
                with gr.Row():
                    mdx23c_input_path = gr.Textbox(
                        label = i18n("Input path"),
                        placeholder = i18n("Place the input path here"),
                        interactive = True
                    )
                    mdx23c_output_path = gr.Textbox(
                        label = i18n("Output path"),
                        placeholder = i18n("Place the output path here"),
                        interactive = True
                    )
                with gr.Row():
                    mdx23c_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
                with gr.Row():
                    mdx23c_info = gr.Textbox(
                        label = i18n("Output information"),
                        interactive = False
                    )

            mdx23c_bath_button.click(mdx23c_batch, [mdx23c_input_path, mdx23c_output_path, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_info])

            with gr.Row():
                mdx23c_button = gr.Button(i18n("Separate!"), variant = "primary")
            with gr.Row():
                mdx23c_stem1 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    label = i18n("Stem 1"),
                    type = "filepath"
                )
                mdx23c_stem2 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    label = i18n("Stem 2"),
                    type = "filepath"
                )

            mdx23c_button.click(mdxc_separator, [mdx23c_audio, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_stem1, mdx23c_stem2])
                
        with gr.TabItem("MDX-NET"):
            with gr.Row():
                mdxnet_model = gr.Dropdown(
                    label = i18n("Select the model"),
                    choices = mdxnet_models,
                    value = initial_settings.get("MDX-NET", {}).get("model", None),
                    interactive = True
                )
                mdxnet_output_format = gr.Dropdown(
                    label = i18n("Select the output format"),
                    choices = output_format,
                    value = initial_settings.get("MDX-NET", {}).get("output_format", None),
                    interactive = True
                )
            with gr.Accordion(i18n("Advanced settings"), open = False):
                with gr.Group():
                    with gr.Row():
                        mdxnet_hop_length = gr.Slider(
                            label = i18n("Hop length"),
                            info = i18n("Usually called stride in neural networks; only change if you know what you're doing"),
                            minimum = 32,
                            maximum = 2048,
                            step = 32,
                            value = initial_settings.get("MDX-NET", {}).get("hop_length", 1024),
                            interactive = True
                        )
                        mdxnet_segment_size = gr.Slider(
                            minimum = 32,
                            maximum = 4000,
                            step = 32,
                            label = i18n("Segment size"),
                            info = i18n("Larger consumes more resources, but may give better results"),
                            value = initial_settings.get("MDX-NET", {}).get("segment_size", 256),
                            interactive = True
                        )
                        mdxnet_denoise = gr.Checkbox(
                            label = i18n("Denoise"),
                            info = i18n("Enable denoising during separation"),
                            value = initial_settings.get("MDX-NET", {}).get("denoise", True),
                            interactive = True
                        )
                    with gr.Row():
                        mdxnet_overlap = gr.Slider(
                            label = i18n("Overlap"),
                            info = i18n("Amount of overlap between prediction windows"),
                            minimum = 0.001,
                            maximum = 0.999,
                            step = 0.001,
                            value = initial_settings.get("MDX-NET", {}).get("overlap", 0.25),
                            interactive = True
                        )
                        mdxnet_batch_size = gr.Slider(
                            label = i18n("Batch size"),
                            info = i18n("Larger consumes more RAM but may process slightly faster"),
                            minimum = 1,
                            maximum = 16,
                            step = 1,
                            value = initial_settings.get("MDX-NET", {}).get("batch_size", 1),
                            interactive = True
                        )
                    with gr.Row():
                        mdxnet_normalization_threshold = gr.Slider(
                            label = i18n("Normalization threshold"),
                            info = i18n("The threshold for audio normalization"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("MDX-NET", {}).get("normalization_threshold", 0.9),
                            interactive = True
                        )
                        mdxnet_amplification_threshold = gr.Slider(
                            label = i18n("Amplification threshold"),
                            info = i18n("The threshold for audio amplification"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("MDX-NET", {}).get("amplification_threshold", 0.7),
                            interactive = True
                        )
                    with gr.Row():
                        mdxnet_single_stem = gr.Textbox(
                            label = i18n("Output only single stem"),
                            placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
                            value = initial_settings.get("MDX-NET", {}).get("single_stem", ""),
                            interactive = True
                        )

                    components["MDX-NET"] = {
                        "model": mdxnet_model,
                        "output_format": mdxnet_output_format,
                        "hop_length": mdxnet_hop_length,
                        "segment_size": mdxnet_segment_size,
                        "denoise": mdxnet_denoise,
                        "overlap": mdxnet_overlap,
                        "batch_size": mdxnet_batch_size,
                        "normalization_threshold": mdxnet_normalization_threshold,
                        "amplification_threshold": mdxnet_amplification_threshold,
                        "single_stem": mdxnet_single_stem
                    }
                    all_configurable_inputs.extend(components["MDX-NET"].values())

            with gr.Row():
                mdxnet_audio = gr.Audio(
                    label = i18n("Input audio"),
                    type = "filepath",
                    interactive = True
                )
            with gr.Accordion(i18n("Separation by link"), open = False):
                with gr.Row():
                    mdxnet_link = gr.Textbox(
                        label = i18n("Link"),
                        placeholder = i18n("Paste the link here"),
                        interactive = True
                    )
                with gr.Row():
                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
                with gr.Row():
                    mdxnet_download_button = gr.Button(
                        i18n("Download!"),
                        variant = "primary"
                    )
                
            mdxnet_download_button.click(download_audio, [mdxnet_link], [mdxnet_audio])

            with gr.Accordion(i18n("Batch separation"), open = False):
                with gr.Row():
                    mdxnet_input_path = gr.Textbox(
                        label = i18n("Input path"),
                        placeholder = i18n("Place the input path here"),
                        interactive = True
                    )
                    mdxnet_output_path = gr.Textbox(
                        label = i18n("Output path"),
                        placeholder = i18n("Place the output path here"),
                        interactive = True
                    )
                with gr.Row():
                    mdxnet_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
                with gr.Row():
                    mdxnet_info = gr.Textbox(
                        label = i18n("Output information"),
                        interactive = False
                    )

            mdxnet_bath_button.click(mdxnet_batch, [mdxnet_input_path, mdxnet_output_path, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_info])

            with gr.Row():
                mdxnet_button = gr.Button(i18n("Separate!"), variant = "primary")
            with gr.Row():
                mdxnet_stem1 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    label = i18n("Stem 1"),
                    type = "filepath"
                )
                mdxnet_stem2 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    label = i18n("Stem 2"),
                    type = "filepath"
                )

            mdxnet_button.click(mdxnet_separator, [mdxnet_audio, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_stem1, mdxnet_stem2])

        with gr.TabItem("VR ARCH"):
            with gr.Row():
                vrarch_model = gr.Dropdown(
                    label = i18n("Select the model"),
                    choices = vrarch_models,
                    value = initial_settings.get("VR Arch", {}).get("model", None),
                    interactive = True
                )
                vrarch_output_format = gr.Dropdown(
                    label = i18n("Select the output format"),
                    choices = output_format,
                    value = initial_settings.get("VR Arch", {}).get("output_format", None),
                    interactive = True
                )
            with gr.Accordion(i18n("Advanced settings"), open = False):
                with gr.Group():
                    with gr.Row():
                        vrarch_window_size = gr.Slider(
                            label = i18n("Window size"),
                            info = i18n("Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality"),
                            minimum=320,
                            maximum=1024,
                            step=32,
                            value = initial_settings.get("VR Arch", {}).get("window_size", 512),
                            interactive = True
                        )
                        vrarch_agression = gr.Slider(
                            minimum = 1,
                            maximum = 50,
                            step = 1,
                            label = i18n("Agression"),
                            info = i18n("Intensity of primary stem extraction"),
                            value = initial_settings.get("VR Arch", {}).get("aggression", 5),
                            interactive = True
                        )
                        vrarch_tta = gr.Checkbox(
                            label = i18n("TTA"),
                            info = i18n("Enable Test-Time-Augmentation; slow but improves quality"),
                            value = initial_settings.get("VR Arch", {}).get("tta", True),
                            visible = True,
                            interactive = True
                        )
                    with gr.Row():
                        vrarch_post_process = gr.Checkbox(
                            label = i18n("Post process"),
                            info = i18n("Identify leftover artifacts within vocal output; may improve separation for some songs"),
                            value = initial_settings.get("VR Arch", {}).get("post_process", False),
                            visible = True,
                            interactive = True
                        )
                        vrarch_post_process_threshold = gr.Slider(
                            label = i18n("Post process threshold"),
                            info = i18n("Threshold for post-processing"),
                            minimum = 0.1,
                            maximum = 0.3,
                            step = 0.1,
                            value = initial_settings.get("VR Arch", {}).get("post_process_threshold", 0.2),
                            interactive = True
                        )
                    with gr.Row():
                        vrarch_high_end_process = gr.Checkbox(
                            label = i18n("High end process"),
                            info = i18n("Mirror the missing frequency range of the output"),
                            value = initial_settings.get("VR Arch", {}).get("high_end_process", False),
                            visible = True,
                            interactive = True,
                        )
                        vrarch_batch_size = gr.Slider(
                            label = i18n("Batch size"),
                            info = i18n("Larger consumes more RAM but may process slightly faster"),
                            minimum = 1,
                            maximum = 16,
                            step = 1,
                            value = initial_settings.get("VR Arch", {}).get("batch_size", 1),
                            interactive = True
                        )
                    with gr.Row():
                        vrarch_normalization_threshold = gr.Slider(
                            label = i18n("Normalization threshold"),
                            info = i18n("The threshold for audio normalization"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("VR Arch", {}).get("normalization_threshold", 0.9),
                            interactive = True
                        )
                        vrarch_amplification_threshold = gr.Slider(
                            label = i18n("Amplification threshold"),
                            info = i18n("The threshold for audio amplification"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("VR Arch", {}).get("amplification_threshold", 0.7),
                            interactive = True
                        )
                    with gr.Row():
                        vrarch_single_stem = gr.Textbox(
                            label = i18n("Output only single stem"),
                            placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
                            value = initial_settings.get("VR Arch", {}).get("single_stem", ""),
                            interactive = True
                        )

                    components["VR Arch"] = {
                        "model": vrarch_model,
                        "output_format": vrarch_output_format,
                        "window_size": vrarch_window_size,
                        "aggression": vrarch_agression,
                        "tta": vrarch_tta,
                        "post_process": vrarch_post_process,
                        "post_process_threshold": vrarch_post_process_threshold,
                        "high_end_process": vrarch_high_end_process,
                        "batch_size": vrarch_batch_size,
                        "normalization_threshold": vrarch_normalization_threshold,
                        "amplification_threshold": vrarch_amplification_threshold,
                        "single_stem": vrarch_single_stem
                    }
                    all_configurable_inputs.extend(components["VR Arch"].values())

            with gr.Row():
                vrarch_audio = gr.Audio(
                    label = i18n("Input audio"),
                    type = "filepath",
                    interactive = True
                )
            with gr.Accordion(i18n("Separation by link"), open = False):
                with gr.Row():
                    vrarch_link = gr.Textbox(
                        label = i18n("Link"),
                        placeholder = i18n("Paste the link here"),
                        interactive = True
                    )
                with gr.Row():
                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
                with gr.Row():
                    vrarch_download_button = gr.Button(
                        i18n("Download!"),
                        variant = "primary"
                )

            vrarch_download_button.click(download_audio, [vrarch_link], [vrarch_audio])
            
            with gr.Accordion(i18n("Batch separation"), open = False):
                with gr.Row():
                    vrarch_input_path = gr.Textbox(
                        label = i18n("Input path"),
                        placeholder = i18n("Place the input path here"),
                        interactive = True
                    )
                    vrarch_output_path = gr.Textbox(
                        label = i18n("Output path"),
                        placeholder = i18n("Place the output path here"),
                        interactive = True
                    )
                with gr.Row():
                    vrarch_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
                with gr.Row():
                    vrarch_info = gr.Textbox(
                        label = i18n("Output information"),
                        interactive = False
                    )

            vrarch_bath_button.click(vrarch_batch, [vrarch_input_path, vrarch_output_path, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_info])

            with gr.Row():
                vrarch_button = gr.Button(i18n("Separate!"), variant = "primary")
            with gr.Row():
                vrarch_stem1 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    type = "filepath",
                    label = i18n("Stem 1")
                )
                vrarch_stem2 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    type = "filepath",
                    label = i18n("Stem 2")
                )

            vrarch_button.click(vrarch_separator, [vrarch_audio, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_stem1, vrarch_stem2])

        with gr.TabItem("Demucs"):
            with gr.Row():
                demucs_model = gr.Dropdown(
                    label = i18n("Select the model"),
                    choices = demucs_models,
                    value = initial_settings.get("Demucs", {}).get("model", None),
                    interactive = True
                )
                demucs_output_format = gr.Dropdown(
                    label = i18n("Select the output format"),
                    choices = output_format,
                    value = initial_settings.get("Demucs", {}).get("output_format", None),
                    interactive = True
                )
            with gr.Accordion(i18n("Advanced settings"), open = False):
                with gr.Group():
                    with gr.Row():
                        demucs_shifts = gr.Slider(
                            label = i18n("Shifts"),
                            info = i18n("Number of predictions with random shifts, higher = slower but better quality"),
                            minimum = 1,
                            maximum = 20,
                            step = 1,
                            value = initial_settings.get("Demucs", {}).get("shifts", 2),
                            interactive = True
                        )
                        demucs_segment_size = gr.Slider(
                            label = i18n("Segment size"),
                            info = i18n("Size of segments into which the audio is split. Higher = slower but better quality"),
                            minimum = 1,
                            maximum = 100,
                            step = 1,
                            value = initial_settings.get("Demucs", {}).get("segment_size", 40),
                            interactive = True
                        )
                        demucs_segments_enabled = gr.Checkbox(
                            label = i18n("Segment-wise processing"),
                            info = i18n("Enable segment-wise processing"),
                            value = initial_settings.get("Demucs", {}).get("segments_enabled", True),
                            interactive = True
                        )
                    with gr.Row():
                        demucs_overlap = gr.Slider(
                            label = i18n("Overlap"),
                            info = i18n("Overlap between prediction windows. Higher = slower but better quality"),
                            minimum=0.001,
                            maximum=0.999,
                            step=0.001,
                            value = initial_settings.get("Demucs", {}).get("overlap", 0.25),
                            interactive = True
                        )
                        demucs_batch_size = gr.Slider(
                            label = i18n("Batch size"),
                            info = i18n("Larger consumes more RAM but may process slightly faster"),
                            minimum = 1,
                            maximum = 16,
                            step = 1,
                            value = initial_settings.get("Demucs", {}).get("batch_size", 1),
                            interactive = True
                        )
                    with gr.Row():
                        demucs_normalization_threshold = gr.Slider(
                            label = i18n("Normalization threshold"),
                            info = i18n("The threshold for audio normalization"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("Demucs", {}).get("normalization_threshold", 0.9),
                            interactive = True
                        )
                        demucs_amplification_threshold = gr.Slider(
                            label = i18n("Amplification threshold"),
                            info = i18n("The threshold for audio amplification"),
                            minimum = 0.1,
                            maximum = 1,
                            step = 0.1,
                            value = initial_settings.get("Demucs", {}).get("amplification_threshold", 0.7),
                            interactive = True
                        )

                    components["Demucs"] = {
                        "model": demucs_model,
                        "output_format": demucs_output_format,
                        "shifts": demucs_shifts,
                        "segment_size": demucs_segment_size,
                        "segments_enabled": demucs_segments_enabled,
                        "overlap": demucs_overlap,
                        "batch_size": demucs_batch_size,
                        "normalization_threshold": demucs_normalization_threshold,
                        "amplification_threshold": demucs_amplification_threshold
                    }
                    all_configurable_inputs.extend(components["Demucs"].values())

            with gr.Row():
                demucs_audio = gr.Audio(
                    label = i18n("Input audio"),
                    type = "filepath",
                    interactive = True
                )
            with gr.Accordion(i18n("Separation by link"), open = False):
                with gr.Row():
                    demucs_link = gr.Textbox(
                        label = i18n("Link"),
                        placeholder = i18n("Paste the link here"),
                        interactive = True
                    )
                with gr.Row():
                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
                with gr.Row():
                    demucs_download_button = gr.Button(
                        i18n("Download!"),
                        variant = "primary"
                    )

            demucs_download_button.click(download_audio, [demucs_link], [demucs_audio])

            with gr.Accordion(i18n("Batch separation"), open = False):
                with gr.Row():
                    demucs_input_path = gr.Textbox(
                        label = i18n("Input path"),
                        placeholder = i18n("Place the input path here"),
                        interactive = True
                    )
                    demucs_output_path = gr.Textbox(
                        label = i18n("Output path"),
                        placeholder = i18n("Place the output path here"),
                        interactive = True
                    )
                with gr.Row():
                    demucs_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
                with gr.Row():
                    demucs_info = gr.Textbox(
                        label = i18n("Output information"),
                        interactive = False
                    )

            demucs_bath_button.click(demucs_batch, [demucs_input_path, demucs_output_path, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_info])

            with gr.Row():
                demucs_button = gr.Button(i18n("Separate!"), variant = "primary")
            with gr.Row():
                demucs_stem1 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    type = "filepath",
                    label = i18n("Stem 1")
                )
                demucs_stem2 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    type = "filepath",
                    label = i18n("Stem 2")
                )
            with gr.Row():
                demucs_stem3 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    type = "filepath",
                    label = i18n("Stem 3")
                )
                demucs_stem4 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    type = "filepath",
                    label = i18n("Stem 4")
                )
            with gr.Row(visible=False) as stem6:
                demucs_stem5 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    type = "filepath",
                    label = i18n("Stem 5")
                )
                demucs_stem6 = gr.Audio(
                    show_download_button = True,
                    interactive = False,
                    type = "filepath",
                    label = i18n("Stem 6")
                )

            demucs_model.change(update_stems, inputs=[demucs_model], outputs=stem6)
                
            demucs_button.click(demucs_separator, [demucs_audio, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_stem1, demucs_stem2, demucs_stem3, demucs_stem4, demucs_stem5, demucs_stem6])

        with gr.TabItem(i18n("Leaderboard")):
            with gr.Group():
                with gr.Row(equal_height=True):
                    list_filter = gr.Dropdown(
                        label = i18n("List filter"),
                        info = i18n("Filter and sort the model list by stem"),
                        choices = ["vocals", "instrumental", "reverb", "echo", "noise", "crowd", "dry", "aspiration", "male", "woodwinds", "kick", "drums", "bass", "guitar", "piano", "other"],
                        value = lambda : None
                    )
                    list_button = gr.Button(i18n("Show list!"), variant = "primary")
            output_list = gr.HTML(label = i18n("Leaderboard"))

            list_button.click(leaderboard, inputs=list_filter, outputs=output_list)

        with gr.TabItem(i18n("Themes")):
            themes_select = gr.Dropdown(
                label = i18n("Theme"),
                info = i18n("Select the theme you want to use. (Requires restarting the App)"),
                choices = loadThemes.get_list(),
                value = loadThemes.read_json(),
                interactive = True
            )

            themes_select.change(
                fn = loadThemes.select_theme,
                inputs = themes_select,
                outputs = []
            )

        with gr.TabItem(i18n("Credits")):
            gr.Markdown(
                """
                UVR5 UI created by **[Eddycrack 864](https://github.com/Eddycrack864).** Join **[AI HUB](https://discord.gg/aihub)** community.
                * python-audio-separator by [beveradb](https://github.com/beveradb).
                * Special thanks to [Ilaria](https://github.com/TheStingerX) for hosting this space and help.
                * Thanks to [Mikus](https://github.com/cappuch) for the help with the code.
                * Thanks to [Nick088](https://huggingface.co/Nick088) for the help to fix roformers.
                * Thanks to [yt_dlp](https://github.com/yt-dlp/yt-dlp) devs.
                * Separation by link source code and improvements by [NeoDev](https://github.com/TheNeodev).
                * Thanks to [ArisDev](https://github.com/aris-py) for porting UVR5 UI to Kaggle and improvements.
                * Thanks to [Bebra777228](https://github.com/Bebra777228)'s code for guiding me to improve my code.
                * Thanks to Nick088, MrM0dZ, Ryouko-Yamanda65777, lucinamari, perariroswe, Enes, Léo and the_undead0 for helping translate UVR5 UI.
                * Thanks to vadigr123 for creating the images for the Discord Rich Presence.
                
                You can donate to the original UVR5 project here:
                [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/uvr5)
                """
            )

app.queue()
app.launch()