import os
import sys
import subprocess
import re
import platform
import torch
import logging
import json
import copy
import spaces
from audio_separator.separator import Separator
from variable import *
import yt_dlp


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)