Update src/webui.py

src/webui.py

@@ -1,334 +1,353 @@
-import json
-import os
-import shutil
-import urllib.request
-import zipfile
-from argparse import ArgumentParser
-import spaces
-import gradio as gr
-
-from main import song_cover_pipeline
-
-BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-mdxnet_models_dir = os.path.join(BASE_DIR, 'mdxnet_models')
-rvc_models_dir = os.path.join(BASE_DIR, 'rvc_models')
-output_dir = os.path.join(BASE_DIR, 'song_output')
-
-
-def get_current_models(models_dir):
-    models_list = os.listdir(models_dir)
-    items_to_remove = ['hubert_base.pt', 'MODELS.txt', 'public_models.json', 'rmvpe.pt']
-    return [item for item in models_list if item not in items_to_remove]
-
-
-def update_models_list():
-    models_l = get_current_models(rvc_models_dir)
-    return gr.update(choices=models_l)
-
-
-def load_public_models():
-    models_table = []
-    for model in public_models['voice_models']:
-        if not model['name'] in voice_models:
-            model = [model['name'], model['description'], model['credit'], model['url'], ', '.join(model['tags'])]
-            models_table.append(model)
-
-    tags = list(public_models['tags'].keys())
-    return gr.update(value=models_table), gr.update(choices=tags)
-
-
-def extract_zip(extraction_folder, zip_name):
-    os.makedirs(extraction_folder)
-    with zipfile.ZipFile(zip_name, 'r') as zip_ref:
-        zip_ref.extractall(extraction_folder)
-    os.remove(zip_name)
-
-    index_filepath, model_filepath = None, None
-    for root, dirs, files in os.walk(extraction_folder):
-        for name in files:
-            if name.endswith('.index') and os.stat(os.path.join(root, name)).st_size > 1024 * 100:
-                index_filepath = os.path.join(root, name)
-
-            if name.endswith('.pth') and os.stat(os.path.join(root, name)).st_size > 1024 * 1024 * 40:
-                model_filepath = os.path.join(root, name)
-
-    if not model_filepath:
-        raise gr.Error(f'No .pth model file was found in the extracted zip. Please check {extraction_folder}.')
-
-    # move model and index file to extraction folder
-    os.rename(model_filepath, os.path.join(extraction_folder, os.path.basename(model_filepath)))
-    if index_filepath:
-        os.rename(index_filepath, os.path.join(extraction_folder, os.path.basename(index_filepath)))
-
-    # remove any unnecessary nested folders
-    for filepath in os.listdir(extraction_folder):
-        if os.path.isdir(os.path.join(extraction_folder, filepath)):
-            shutil.rmtree(os.path.join(extraction_folder, filepath))
-
-
-def download_online_model(url, dir_name, progress=gr.Progress()):
-    try:
-        progress(0, desc=f'[~] Downloading voice model with name {dir_name}...')
-        zip_name = url.split('/')[-1]
-        extraction_folder = os.path.join(rvc_models_dir, dir_name)
-        if os.path.exists(extraction_folder):
-            raise gr.Error(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.')
-
-        if 'pixeldrain.com' in url:
-            url = f'https://pixeldrain.com/api/file/{zip_name}'
-
-        urllib.request.urlretrieve(url, zip_name)
-
-        progress(0.5, desc='[~] Extracting zip...')
-        extract_zip(extraction_folder, zip_name)
-        return f'[+] {dir_name} Model successfully downloaded!'
-
-    except Exception as e:
-        raise gr.Error(str(e))
-
-
-def upload_local_model(zip_path, dir_name, progress=gr.Progress()):
-    try:
-        extraction_folder = os.path.join(rvc_models_dir, dir_name)
-        if os.path.exists(extraction_folder):
-            raise gr.Error(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.')
-
-        zip_name = zip_path.name
-        progress(0.5, desc='[~] Extracting zip...')
-        extract_zip(extraction_folder, zip_name)
-        return f'[+] {dir_name} Model successfully uploaded!'
+"""Module which defines the code for the "Manage models" tab."""
 
-    except Exception as e:
-        raise gr.Error(str(e))
+from collections.abc import Sequence
+from functools import partial
 
-
-def filter_models(tags, query):
-    models_table = []
-
-    # no filter
-    if len(tags) == 0 and len(query) == 0:
-        for model in public_models['voice_models']:
-            models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']])
-
-    # filter based on tags and query
-    elif len(tags) > 0 and len(query) > 0:
-        for model in public_models['voice_models']:
-            if all(tag in model['tags'] for tag in tags):
-                model_attributes = f"{model['name']} {model['description']} {model['credit']} {' '.join(model['tags'])}".lower()
-                if query.lower() in model_attributes:
-                    models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']])
-
-    # filter based on only tags
-    elif len(tags) > 0:
-        for model in public_models['voice_models']:
-            if all(tag in model['tags'] for tag in tags):
-                models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']])
-
-    # filter based on only query
-    else:
-        for model in public_models['voice_models']:
-            model_attributes = f"{model['name']} {model['description']} {model['credit']} {' '.join(model['tags'])}".lower()
-            if query.lower() in model_attributes:
-                models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']])
-
-    return gr.update(value=models_table)
-
-
-def pub_dl_autofill(pub_models, event: gr.SelectData):
-    return gr.update(value=pub_models.loc[event.index[0], 'URL']), gr.update(value=pub_models.loc[event.index[0], 'Model Name'])
-
-
-def swap_visibility():
-    return gr.update(visible=True), gr.update(visible=False), gr.update(value=''), gr.update(value=None)
-
-
-def process_file_upload(file):
-    return file.name, gr.update(value=file.name)
-
-
-def show_hop_slider(pitch_detection_algo):
-    if pitch_detection_algo == 'mangio-crepe':
-        return gr.update(visible=True)
+import gradio as gr
+import pandas as pd
+import requests
+
+# Function to search for RVC models on Hugging Face
+def search_rvc_models(query):
+    url = f"https://huggingface.co/api/models?search={query}&library=rvc"
+    response = requests.get(url)
+    if response.status_code == 200:
+        models = response.json()
+        # Create a DataFrame to store the results
+        df = pd.DataFrame(models)
+        # Filter the DataFrame to only include the desired columns
+        df = df[["id", "likes", "downloads"]]
+        # Add a new column for the download URL
+        df["downloadUrl"] = "https://huggingface.co/" + df["id"]
+        # Sort the DataFrame by downloads in descending order
+        df = df.sort_values(by="downloads", ascending=False)
+        return df
     else:
-        return gr.update(visible=False)
-
-
-if __name__ == '__main__':
-    parser = ArgumentParser(description='Generate a AI cover song in the song_output/id directory.', add_help=True)
-    parser.add_argument("--share", action="store_true", dest="share_enabled", default=False, help="Enable sharing")
-    parser.add_argument("--listen", action="store_true", default=False, help="Make the WebUI reachable from your local network.")
-    parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.')
-    parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
-    args = parser.parse_args()
-
-    voice_models = get_current_models(rvc_models_dir)
-    with open(os.path.join(rvc_models_dir, 'public_models.json'), encoding='utf8') as infile:
-        public_models = json.load(infile)
-
-    with gr.Blocks(title='AICoverGenWebUI') as app:
-
-        gr.Label('AICoverGen WebUI ZeroGPU mode created with ❤️', show_label=False)
-        gr.Markdown(
-            """
-            <details>
-                <summary style="font-size: 1.5em;">⚠️ Important (click to expand)</summary>
-                <ul>
-                    <li>🚀 This demo use a Zero GPU, which is available only for a limited time. It's recommended to use audio files that are no longer than 5 minutes. If you want to use it without time restrictions, you can duplicate the 'old CPU space'. ⏳</li>
-                </ul>
-            </details>
-            """
+        return pd.DataFrame({"id": ["No models found"]})
+
+from ultimate_rvc.core.manage.models import (
+    delete_all_models,
+    delete_models,
+    download_model,
+    filter_public_models_table,
+    get_public_model_tags,
+    get_saved_model_names,
+    upload_model,
+)
+from ultimate_rvc.web.common import (
+    PROGRESS_BAR,
+    confirm_box_js,
+    confirmation_harness,
+    exception_harness,
+    render_msg,
+    update_dropdowns,
+)
+from ultimate_rvc.web.typing_extra import DropdownValue
+
+
+def _update_models(
+    num_components: int,
+    value: DropdownValue = None,
+    value_indices: Sequence[int] = [],
+) -> gr.Dropdown | tuple[gr.Dropdown, ...]:
+    """
+    Update the choices of one or more dropdown components to the set of
+    currently saved voice models.
+
+    Optionally updates the default value of one or more of these
+    components.
+
+    Parameters
+    ----------
+    num_components : int
+        Number of dropdown components to update.
+    value : DropdownValue, optional
+        New value for dropdown components.
+    value_indices : Sequence[int], default=[]
+        Indices of dropdown components to update the value for.
+
+    Returns
+    -------
+    gr.Dropdown | tuple[gr.Dropdown, ...]
+        Updated dropdown component or components.
+
+    """
+    return update_dropdowns(get_saved_model_names, num_components, value, value_indices)
+
+
+def _filter_public_models_table(tags: Sequence[str], query: str) -> gr.Dataframe:
+    """
+    Filter table containing metadata of public voice models by tags and
+    a search query.
+
+    Parameters
+    ----------
+    tags : Sequence[str]
+        Tags to filter the metadata table by.
+    query : str
+        Search query to filter the metadata table by.
+
+    Returns
+    -------
+    gr.Dataframe
+        The filtered table rendered in a Gradio dataframe.
+
+    """
+    models_table = filter_public_models_table(tags, query)
+    return gr.Dataframe(value=models_table)
+
+
+def _autofill_model_name_and_url(
+    public_models_table: pd.DataFrame,
+    select_event: gr.SelectData,
+) -> tuple[gr.Textbox, gr.Textbox]:
+    """
+    Autofill two textboxes with respectively the name and URL that is
+    saved in the currently selected row of the public models table.
+
+    Parameters
+    ----------
+    public_models_table : pd.DataFrame
+        The public models table saved in a Pandas dataframe.
+    select_event : gr.SelectData
+        Event containing the index of the currently selected row in the
+        public models table.
+
+    Returns
+    -------
+    name : gr.Textbox
+        The textbox containing the model name.
+
+    url : gr.Textbox
+        The textbox containing the model URL.
+
+    Raises
+    ------
+    TypeError
+        If the index in the provided event is not a sequence.
+
+    """
+    event_index = select_event.index
+    if not isinstance(event_index, Sequence):
+        err_msg = (
+            f"Expected a sequence of indices but got {type(event_index)} from the"
+            " provided event."
         )
-        gr.Markdown("Duplicate the old CPU space for use in private: [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/r3gm/AICoverGen_old_stable_cpu?duplicate=true)\n\n") 
-
-        # main tab
-        with gr.Tab("Generate"):
-            
-            with gr.Accordion('Main Options'):
-                with gr.Row():
-                    with gr.Column():
-                        rvc_model = gr.Dropdown(voice_models, label='Voice Models', info='Models folder "AICoverGen --> rvc_models". After new models are added into this folder, click the refresh button')
-                        ref_btn = gr.Button('Refresh Models 🔁', variant='primary')
-
-                    with gr.Column() as yt_link_col:
-                        song_input = gr.Text(label='Song input', info='Link to a song on YouTube or full path to a local file. For file upload, click the button below. Example: https://www.youtube.com/watch?v=M-mtdN6R3bQ')
-                        show_file_upload_button = gr.Button('Upload file instead')
-
-                    with gr.Column(visible=False) as file_upload_col:
-                        local_file = gr.File(label='Audio file')
-                        song_input_file = gr.UploadButton('Upload 📂', file_types=['audio'], variant='primary')
-                        show_yt_link_button = gr.Button('Paste YouTube link/Path to local file instead')
-                        song_input_file.upload(process_file_upload, inputs=[song_input_file], outputs=[local_file, song_input])
-
-                    with gr.Column():
-                        pitch = gr.Slider(-3, 3, value=0, step=1, label='Pitch Change (Vocals ONLY)', info='Generally, use 1 for male to female conversions and -1 for vice-versa. (Octaves)')
-                        pitch_all = gr.Slider(-12, 12, value=0, step=1, label='Overall Pitch Change', info='Changes pitch/key of vocals and instrumentals together. Altering this slightly reduces sound quality. (Semitones)')
-                    show_file_upload_button.click(swap_visibility, outputs=[file_upload_col, yt_link_col, song_input, local_file])
-                    show_yt_link_button.click(swap_visibility, outputs=[yt_link_col, file_upload_col, song_input, local_file])
-
-            with gr.Accordion('Voice conversion options', open=False):
-                with gr.Row():
-                    index_rate = gr.Slider(0, 1, value=0.5, label='Index Rate', info="Controls how much of the AI voice's accent to keep in the vocals")
-                    filter_radius = gr.Slider(0, 7, value=3, step=1, label='Filter radius', info='If >=3: apply median filtering median filtering to the harvested pitch results. Can reduce breathiness')
-                    rms_mix_rate = gr.Slider(0, 1, value=0.25, label='RMS mix rate', info="Control how much to mimic the original vocal's loudness (0) or a fixed loudness (1)")
-                    protect = gr.Slider(0, 0.5, value=0.33, label='Protect rate', info='Protect voiceless consonants and breath sounds. Set to 0.5 to disable.')
-                    with gr.Column():
-                        f0_method = gr.Dropdown(['rmvpe+', 'rmvpe', 'mangio-crepe'], value='rmvpe+', label='Pitch detection algorithm', info='Best option is rmvpe (clarity in vocals), then mangio-crepe (smoother vocals), rmvpe+ use a minimum and maximum allowed pitch values.')
-                        crepe_hop_length = gr.Slider(32, 320, value=128, step=1, visible=False, label='Crepe hop length', info='Lower values leads to longer conversions and higher risk of voice cracks, but better pitch accuracy.')
-                        f0_method.change(show_hop_slider, inputs=f0_method, outputs=crepe_hop_length)
-                keep_files = gr.Checkbox(True, label='Keep intermediate files', info='Keep all audio files generated in the song_output/id directory, e.g. Isolated Vocals/Instrumentals. Leave unchecked to save space')
-
-            with gr.Accordion('Audio mixing options', open=False):
-                gr.Markdown('### Volume Change (decibels)')
-                with gr.Row():
-                    main_gain = gr.Slider(-20, 20, value=0, step=1, label='Main Vocals')
-                    backup_gain = gr.Slider(-20, 20, value=0, step=1, label='Backup Vocals')
-                    inst_gain = gr.Slider(-20, 20, value=0, step=1, label='Music')
-
-                gr.Markdown('### Reverb Control on AI Vocals')
-                with gr.Row():
-                    reverb_rm_size = gr.Slider(0, 1, value=0.15, label='Room size', info='The larger the room, the longer the reverb time')
-                    reverb_wet = gr.Slider(0, 1, value=0.2, label='Wetness level', info='Level of AI vocals with reverb')
-                    reverb_dry = gr.Slider(0, 1, value=0.8, label='Dryness level', info='Level of AI vocals without reverb')
-                    reverb_damping = gr.Slider(0, 1, value=0.7, label='Damping level', info='Absorption of high frequencies in the reverb')
-
-                gr.Markdown('### Audio Output Format')
-                output_format = gr.Dropdown(['mp3', 'wav'], value='mp3', label='Output file type', info='mp3: small file size, decent quality. wav: Large file size, best quality')
-
+        raise TypeError(err_msg)
+    event_index = event_index[0]
+    url = public_models_table.loc[event_index, "URL"]
+    name = public_models_table.loc[event_index, "Name"]
+    if isinstance(url, str) and isinstance(name, str):
+        return gr.Textbox(value=name), gr.Textbox(value=url)
+    err_msg = (
+        "Expected model name and URL to be strings but got"
+        f" {type(name)} and {type(url)} respectively."
+    )
+    raise TypeError(err_msg)
+
+
+def render(
+    model_delete: gr.Dropdown,
+    model_1click: gr.Dropdown,
+    model_multi: gr.Dropdown,
+) -> None:
+    """
+
+    Render "Manage models" tab.
+
+    Parameters
+    ----------
+    model_delete : gr.Dropdown
+        Dropdown for selecting voice models to delete in the
+        "Delete models" tab.
+    model_1click : gr.Dropdown
+        Dropdown for selecting a voice model to use in the
+        "One-click generation" tab.
+    model_multi : gr.Dropdown
+        Dropdown for selecting a voice model to use in the
+        "Multi-step generation" tab.
+
+    """
+    # Download tab
+
+    dummy_checkbox = gr.Checkbox(visible=False)
+    with gr.Tab("Download model"):
+        with gr.Accordion("View public models table", open=False):
+            gr.Markdown("")
+            gr.Markdown("*HOW TO USE*")
+            gr.Markdown(
+                "- Filter voice models by selecting one or more tags and/or providing a"
+                " search query.",
+            )
+            gr.Markdown(
+                "- Select a row in the table to autofill the name and"
+                " URL for the given voice model in the form fields below.",
+            )
+            gr.Markdown("")
             with gr.Row():
-                clear_btn = gr.ClearButton(value='Clear', components=[song_input, rvc_model, keep_files, local_file])
-                generate_btn = gr.Button("Generate", variant='primary')
-                ai_cover = gr.Audio(label='AI Cover', show_share_button=False)
-
-            ref_btn.click(update_models_list, None, outputs=rvc_model)
-            is_webui = gr.Number(value=1, visible=False)
-            generate_btn.click(song_cover_pipeline,
-                               inputs=[song_input, rvc_model, pitch, keep_files, is_webui, main_gain, backup_gain,
-                                       inst_gain, index_rate, filter_radius, rms_mix_rate, f0_method, crepe_hop_length,
-                                       protect, pitch_all, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping,
-                                       output_format],
-                               outputs=[ai_cover])
-            clear_btn.click(lambda: [0, 0, 0, 0, 0.5, 3, 0.25, 0.33, 'rmvpe+', 128, 0, 0.15, 0.2, 0.8, 0.7, 'mp3', None],
-                            outputs=[pitch, main_gain, backup_gain, inst_gain, index_rate, filter_radius, rms_mix_rate,
-                                     protect, f0_method, crepe_hop_length, pitch_all, reverb_rm_size, reverb_wet,
-                                     reverb_dry, reverb_damping, output_format, ai_cover])
-
-        # Download tab
-        with gr.Tab('Download model'):
-
-            with gr.Tab('From HuggingFace/Pixeldrain URL'):
-                with gr.Row():
-                    model_zip_link = gr.Text(label='Download link to model', info='Should be a zip file containing a .pth model file and an optional .index file.')
-                    model_name = gr.Text(label='Name your model', info='Give your new model a unique name from your other voice models.')
-
-                with gr.Row():
-                    download_btn = gr.Button('Download 🌐', variant='primary', scale=19)
-                    dl_output_message = gr.Text(label='Output Message', interactive=False, scale=20)
-
-                download_btn.click(download_online_model, inputs=[model_zip_link, model_name], outputs=dl_output_message)
-
-                gr.Markdown('## Input Examples')
-                gr.Examples(
-                    [
-                        ['https://huggingface.co/phant0m4r/LiSA/resolve/main/LiSA.zip', 'Lisa'],
-                        ['https://pixeldrain.com/u/3tJmABXA', 'Gura'],
-                        ['https://huggingface.co/Kit-Lemonfoot/kitlemonfoot_rvc_models/resolve/main/AZKi%20(Hybrid).zip', 'Azki']
-                    ],
-                    [model_zip_link, model_name],
-                    [],
-                    download_online_model,
-                    cache_examples=False,
+                search_query = gr.Textbox(label="Search query")
+                tags = gr.CheckboxGroup(
+                    value=[],
+                    label="Tags",
+                    choices=get_public_model_tags(),
+                )
+            with gr.Row():
+                public_models_table = gr.Dataframe(
+                    value=_filter_public_models_table,
+                    inputs=[tags, search_query],
+                    headers=["Name", "Description", "Tags", "Credit", "Added", "URL"],
+                    label="Public models table",
+                    interactive=False,
                 )
 
-            with gr.Tab('From Public Index'):
-
-                gr.Markdown('## How to use')
-                gr.Markdown('- Click Initialize public models table')
-                gr.Markdown('- Filter models using tags or search bar')
-                gr.Markdown('- Select a row to autofill the download link and model name')
-                gr.Markdown('- Click Download')
-
-                with gr.Row():
-                    pub_zip_link = gr.Text(label='Download link to model')
-                    pub_model_name = gr.Text(label='Model name')
-
-                with gr.Row():
-                    download_pub_btn = gr.Button('Download 🌐', variant='primary', scale=19)
-                    pub_dl_output_message = gr.Text(label='Output Message', interactive=False, scale=20)
-
-                filter_tags = gr.CheckboxGroup(value=[], label='Show voice models with tags', choices=[])
-                search_query = gr.Text(label='Search')
-                load_public_models_button = gr.Button(value='Initialize public models table', variant='primary')
-
-                public_models_table = gr.DataFrame(value=[], headers=['Model Name', 'Description', 'Credit', 'URL', 'Tags'], label='Available Public Models', interactive=False)
-                public_models_table.select(pub_dl_autofill, inputs=[public_models_table], outputs=[pub_zip_link, pub_model_name])
-                load_public_models_button.click(load_public_models, outputs=[public_models_table, filter_tags])
-                search_query.change(filter_models, inputs=[filter_tags, search_query], outputs=public_models_table)
-                filter_tags.change(filter_models, inputs=[filter_tags, search_query], outputs=public_models_table)
-                download_pub_btn.click(download_online_model, inputs=[pub_zip_link, pub_model_name], outputs=pub_dl_output_message)
-
-        # Upload tab
-        with gr.Tab('Upload model'):
-            gr.Markdown('## Upload locally trained RVC v2 model and index file')
-            gr.Markdown('- Find model file (weights folder) and optional index file (logs/[name] folder)')
-            gr.Markdown('- Compress files into zip file')
-            gr.Markdown('- Upload zip file and give unique name for voice')
-            gr.Markdown('- Click Upload model')
+        with gr.Row():
+            model_url = gr.Textbox(
+                label="Model URL",
+                info=(
+                    "Should point to a zip file containing a .pth model file and"
+                    " optionally also an .index file."
+                ),
+            )
+            model_name = gr.Textbox(
+                label="Model name",
+                info="Enter a unique name for the voice model.",
+            )
+
+        with gr.Row(equal_height=True):
+            download_btn = gr.Button("Download 🌐", variant="primary", scale=19)
+            download_msg = gr.Textbox(
+                label="Output message",
+                interactive=False,
+                scale=20,
+            )
+
+        public_models_table.select(
+            _autofill_model_name_and_url,
+            inputs=public_models_table,
+            outputs=[model_name, model_url],
+            show_progress="hidden",
+        )
 
-            with gr.Row():
-                with gr.Column():
-                    zip_file = gr.File(label='Zip file')
+        download_btn_click = download_btn.click(
+            partial(
+                exception_harness(download_model),
+                progress_bar=PROGRESS_BAR,
+            ),
+            inputs=[model_url, model_name],
+            outputs=download_msg,
+        ).success(
+            partial(
+                render_msg,
+                "[+] Succesfully downloaded voice model!",
+            ),
+            inputs=model_name,
+            outputs=download_msg,
+            show_progress="hidden",
+        )
 
-                local_model_name = gr.Text(label='Model name')
+    # Upload tab
+    with gr.Tab("Upload model"):
+        with gr.Accordion("HOW TO USE"):
+            gr.Markdown("")
+            gr.Markdown(
+                "1. Find the .pth file for a locally trained RVC model (e.g. in your"
+                " local weights folder) and optionally also a corresponding .index file"
+                " (e.g. in your logs/[name] folder)",
+            )
+            gr.Markdown(
+                "2. Upload the files directly or save them to a folder, then compress"
+                " that folder and upload the resulting .zip file",
+            )
+            gr.Markdown("3. Enter a unique name for the uploaded model")
+            gr.Markdown("4. Click 'Upload'")
+
+        with gr.Row():
+            model_files = gr.File(
+                label="Files",
+                file_count="multiple",
+                file_types=[".zip", ".pth", ".index"],
+            )
+
+            local_model_name = gr.Textbox(label="Model name")
+
+        with gr.Row(equal_height=True):
+            upload_btn = gr.Button("Upload", variant="primary", scale=19)
+            upload_msg = gr.Textbox(
+                label="Output message",
+                interactive=False,
+                scale=20,
+            )
+            upload_btn_click = upload_btn.click(
+                partial(exception_harness(upload_model), progress_bar=PROGRESS_BAR),
+                inputs=[model_files, local_model_name],
+                outputs=upload_msg,
+            ).success(
+                partial(
+                    render_msg,
+                    "[+] Successfully uploaded voice model!",
+                ),
+                inputs=local_model_name,
+                outputs=upload_msg,
+                show_progress="hidden",
+            )
+
+    with gr.Tab("Delete models"):
+        with gr.Row():
+            with gr.Column():
+                model_delete.render()
+                delete_btn = gr.Button("Delete selected", variant="secondary")
+                delete_all_btn = gr.Button("Delete all", variant="primary")
+            with gr.Column():
+                delete_msg = gr.Textbox(label="Output message", interactive=False)
+        delete_btn_click = delete_btn.click(
+            partial(confirmation_harness(delete_models), progress_bar=PROGRESS_BAR),
+            inputs=[dummy_checkbox, model_delete],
+            outputs=delete_msg,
+            js=confirm_box_js(
+                "Are you sure you want to delete the selected voice models?",
+            ),
+        ).success(
+            partial(render_msg, "[-] Successfully deleted selected voice models!"),
+            outputs=delete_msg,
+            show_progress="hidden",
+        )
 
-            with gr.Row():
-                model_upload_button = gr.Button('Upload model', variant='primary', scale=19)
-                local_upload_output_message = gr.Text(label='Output Message', interactive=False, scale=20)
-                model_upload_button.click(upload_local_model, inputs=[zip_file, local_model_name], outputs=local_upload_output_message)
-
-    app.launch(
-        share=args.share_enabled,
-        # enable_queue=True,
-        server_name=None if not args.listen else (args.listen_host or '0.0.0.0'),
-        server_port=args.listen_port,
-    )
+        delete_all_btn_click = delete_all_btn.click(
+            partial(
+                confirmation_harness(delete_all_models),
+                progress_bar=PROGRESS_BAR,
+            ),
+            inputs=dummy_checkbox,
+            outputs=delete_msg,
+            js=confirm_box_js("Are you sure you want to delete all voice models?"),
+        ).success(
+            partial(render_msg, "[-] Successfully deleted all voice models!"),
+            outputs=delete_msg,
+            show_progress="hidden",
+        )
+        
+        
+    with gr.Tab("Search models"): 
+        # Textbox for user to enter search query
+        query = gr.Textbox(label="Search for RVC models", placeholder="Enter your search query here")
+
+            # Button to trigger the search
+        search_button = gr.Button("Search")
+    
+         # Output for displaying the search results as a DataFrame
+        results = gr.Dataframe(label="Search Results")
+    
+        # Event listener for the search button
+        search_button.click(fn=search_rvc_models, inputs=query, outputs=results)
+
+    for click_event in [
+        download_btn_click,
+        upload_btn_click,
+        delete_btn_click,
+        delete_all_btn_click,
+    ]:
+        click_event.success(
+            partial(_update_models, 3, [], [2]),
+            outputs=[model_1click, model_multi, model_delete],
+            show_progress="hidden",
+        )