Update demo.py

demo.py

@@ -1,14 +1,20 @@
 from original import *
 import shutil, glob
 from easyfuncs import download_from_url, CachedModels
-os.makedirs("dataset",exist_ok=True)
+import os
+
+os.makedirs("dataset", exist_ok=True)
 model_library = CachedModels()
 
-with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="blue",neutral_hue="zinc")) as app:
+# Helper moved outside to avoid lambda issues in UI definition
+def get_audio_paths(path):
+    return [os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1].lower() in ('.mp3', '.wav', '.flac', '.ogg')]
+
+with gr.Blocks(title="🔊", theme=gr.themes.Base(primary_hue="blue", neutral_hue="zinc")) as app:
     with gr.Tabs():
-        with gr.TabItem("Inference"):
+        with gr.Tab("Inference"):
             with gr.Row():
-                voice_model = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True)
+                voice_model = gr.Dropdown(label="Model Voice", choices=[], value="", interactive=True)
                 refresh_button = gr.Button("Refresh", variant="primary")
                 spk_item = gr.Slider(
                     minimum=0,
@@ -27,39 +33,48 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="blue",neutral_hue=
             with gr.Row():
                 with gr.Column():
                     with gr.Row():
-                        dropbox = gr.Audio(label="Drop your audio here & hit the Reload button.")
+                        # Sources must be a list in Gradio 4+
+                        dropbox = gr.Audio(label="Drop your audio here & hit the Reload button.", sources=["upload"])
                     with gr.Row():
-                        record_button=gr.Audio(sources="microphone", label="OR Record audio.", type="filepath")
+                        record_button = gr.Audio(sources=["microphone"], label="OR Record audio.", type="filepath")
                     with gr.Row():
-                        paths_for_files = lambda path:[os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1].lower() in ('.mp3', '.wav', '.flac', '.ogg')]
                         input_audio0 = gr.Dropdown(
                             label="Input Path",
-                            value=paths_for_files('audios')[0] if len(paths_for_files('audios')) > 0 else '',
-                            choices=paths_for_files('audios'), # Only show absolute paths for audio files ending in .mp3, .wav, .flac or .ogg
+                            value="",
+                            choices=[],
                             allow_custom_value=True
                         )
                     with gr.Row():
                         audio_player = gr.Audio()
+                        
+                        # Updated logic for Gradio 6 (using gr.update)
                         input_audio0.change(
                             inputs=[input_audio0],
                             outputs=[audio_player],
-                            fn=lambda path: {"value":path,"__type__":"update"} if os.path.exists(path) else None
+                            fn=lambda path: path if os.path.exists(path) else None
                         )
-                        record_button.stop_recording(
-                            fn=lambda audio:audio, #TODO save wav lambda
+                        
+                        # Replaced stop_recording (deprecated) with change
+                        record_button.change(
+                            fn=lambda audio: audio,
                             inputs=[record_button], 
-                            outputs=[input_audio0])
+                            outputs=[input_audio0]
+                        )
+                        
+                        # Updated logic assuming audio is path (type="filepath")
                         dropbox.upload(
-                            fn=lambda audio:audio.name,
+                            fn=lambda audio: audio,
                             inputs=[dropbox], 
-                            outputs=[input_audio0])
+                            outputs=[input_audio0]
+                        )
+                
                 with gr.Column():
                     with gr.Accordion("Change Index", open=False):
                         file_index2 = gr.Dropdown(
                             label="Change Index",
-                            choices=sorted(index_paths),
+                            choices=[],
                             interactive=True,
-                            value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else ''
+                            value=""
                         )
                         index_rate1 = gr.Slider(
                             minimum=0,
@@ -110,32 +125,42 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="blue",neutral_hue=
                             step=0.01,
                             interactive=True,
                         )
-                        if voice_model != None: vc.get_vc(voice_model.value,protect0,protect0)
+                    
                     file_index1 = gr.Textbox(
                         label="Index Path",
                         interactive=True,
-                        visible=False#Not used here
+                        visible=False
                     )
+
+                    # Consolidated refresh logic
+                    def refresh_ui():
+                        # Get updated lists
+                        # Assuming change_choices is imported from original or defined elsewhere
+                        # It needs to return (model_choices, index_choices)
+                        model_choices, index_choices = change_choices()
+                        
+                        audio_paths = get_audio_paths('audios')
+                        default_audio = audio_paths[0] if audio_paths else ""
+                        default_model = model_choices[0] if model_choices else ""
+                        default_index = index_choices[0] if index_choices else ""
+
+                        return (
+                            gr.update(choices=model_choices, value=default_model), # voice_model
+                            gr.update(choices=index_choices, value=default_index), # file_index2
+                            gr.update(choices=audio_paths, value=default_audio)    # input_audio0
+                        )
+
                     refresh_button.click(
-                        fn=change_choices,
+                        fn=refresh_ui,
                         inputs=[],
-                        outputs=[voice_model, file_index2],
+                        outputs=[voice_model, file_index2, input_audio0],
                         api_name="infer_refresh",
                     )
-                    refresh_button.click(
-                        fn=lambda:{"choices":paths_for_files('audios'),"__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac'
-                        inputs=[],
-                        outputs = [input_audio0],   
-                    )
-                    refresh_button.click(
-                        fn=lambda:{"value":paths_for_files('audios')[0],"__type__":"update"} if len(paths_for_files('audios')) > 0 else {"value":"","__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac'
-                        inputs=[],
-                        outputs = [input_audio0],   
-                    )
+            
             with gr.Row():
                 f0_file = gr.File(label="F0 Path", visible=False)
             with gr.Row():
-                vc_output1 = gr.Textbox(label="Information", placeholder="Welcome!",visible=False)
+                vc_output1 = gr.Textbox(label="Information", placeholder="Welcome!", visible=False)
                 but0.click(
                     vc.vc_single,  
                     [
@@ -161,28 +186,30 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="blue",neutral_hue=
                     outputs=[spk_item, protect0, protect0, file_index2, file_index2],
                     api_name="infer_change_voice",
                 )
-        with gr.TabItem("Download Models"):
+
+        with gr.Tab("Download Models"):
             with gr.Row():
-                url_input = gr.Textbox(label="URL to model", value="",placeholder="https://...", scale=6)
-                name_output = gr.Textbox(label="Save as", value="",placeholder="MyModel",scale=2)
-                url_download = gr.Button(value="Download Model",scale=2)
+                url_input = gr.Textbox(label="URL to model", value="", placeholder="https://...", scale=6)
+                name_output = gr.Textbox(label="Save as", value="", placeholder="MyModel", scale=2)
+                url_download = gr.Button(value="Download Model", scale=2)
                 url_download.click(
-                    inputs=[url_input,name_output],
+                    inputs=[url_input, name_output],
                     outputs=[url_input],
                     fn=download_from_url,
                 )
             with gr.Row():
-                model_browser = gr.Dropdown(choices=list(model_library.models.keys()),label="OR Search Models (Quality UNKNOWN)",scale=5)
-                download_from_browser = gr.Button(value="Get",scale=2)
+                model_browser = gr.Dropdown(choices=list(model_library.models.keys()), label="OR Search Models (Quality UNKNOWN)", scale=5)
+                download_from_browser = gr.Button(value="Get", scale=2)
                 download_from_browser.click(
                     inputs=[model_browser],
                     outputs=[model_browser],
-                    fn=lambda model: download_from_url(model_library.models[model],model),
+                    fn=lambda model: download_from_url(model_library.models[model], model),
                 )
-        with gr.TabItem("Train"):
+
+        with gr.Tab("Train"):
             with gr.Row():
                 with gr.Column():
-                    training_name = gr.Textbox(label="Name your model", value="My-Voice",placeholder="My-Voice")
+                    training_name = gr.Textbox(label="Name your model", value="My-Voice", placeholder="My-Voice")
                     np7 = gr.Slider(
                         minimum=0,
                         maximum=config.n_cpu,
@@ -215,14 +242,17 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="blue",neutral_hue=
                     dataset_folder = gr.Textbox(
                         label="dataset folder", value='dataset'
                     )
-                    easy_uploader = gr.Files(label="Drop your audio files here",file_types=['audio'])
+                    # Replaced gr.Files with gr.File(file_count="multiple")
+                    easy_uploader = gr.File(label="Drop your audio files here", file_count="multiple", file_types=["audio"])
                     but1 = gr.Button("1. Process", variant="primary")
-                    info1 = gr.Textbox(label="Information", value="",visible=True)
-                    easy_uploader.upload(inputs=[dataset_folder],outputs=[],fn=lambda folder:os.makedirs(folder,exist_ok=True))
+                    info1 = gr.Textbox(label="Information", value="", visible=True)
+                    
+                    easy_uploader.upload(inputs=[dataset_folder], outputs=[], fn=lambda folder: os.makedirs(folder, exist_ok=True))
                     easy_uploader.upload(
-                        fn=lambda files,folder: [shutil.copy2(f.name,os.path.join(folder,os.path.split(f.name)[1])) for f in files] if folder != "" else gr.Warning('Please enter a folder name for your dataset'),
+                        fn=lambda files, folder: [shutil.copy2(f.name, os.path.join(folder, os.path.split(f.name)[1])) for f in files] if folder != "" else gr.Warning('Please enter a folder name for your dataset'),
                         inputs=[easy_uploader, dataset_folder], 
-                        outputs=[])
+                        outputs=[]
+                    )
                     gpus6 = gr.Textbox(
                         label="Enter the GPU numbers to use separated by -, (e.g. 0-1-2)",
                         value=gpus,
@@ -247,6 +277,7 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="blue",neutral_hue=
                         [info1],
                         api_name="train_preprocess",
                     ) 
+                
                 with gr.Column():
                     f0method8 = gr.Radio(
                         label="F0 extraction method",
@@ -281,6 +312,7 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="blue",neutral_hue=
                         [info2],
                         api_name="train_extract_f0_feature",
                     )
+                
                 with gr.Column():
                     total_epoch11 = gr.Slider(
                         minimum=2,
@@ -336,102 +368,117 @@ with gr.Blocks(title="🔊",theme=gr.themes.Base(primary_hue="blue",neutral_hue=
                             interactive=True,
                         )
                         with gr.Accordion(label="Change pretrains", open=False):
-                            pretrained = lambda sr, letter: [os.path.abspath(os.path.join('assets/pretrained_v2', file)) for file in os.listdir('assets/pretrained_v2') if file.endswith('.pth') and sr in file and letter in file]
+                            # Replaced lambda in value definition
+                            def get_pretrained_choices(sr, letter):
+                                return [os.path.abspath(os.path.join('assets/pretrained_v2', file)) for file in os.listdir('assets/pretrained_v2') if file.endswith('.pth') and sr in file and letter in file]
+
                             pretrained_G14 = gr.Dropdown(
                                 label="pretrained G",
-                                # Get a list of all pretrained G model files in assets/pretrained_v2 that end with .pth
-                                choices = pretrained(sr2.value, 'G'),
-                                value=pretrained(sr2.value, 'G')[0] if len(pretrained(sr2.value, 'G')) > 0 else '',
+                                choices=[],
+                                value="",
                                 interactive=True,
                                 visible=True
                             )
                             pretrained_D15 = gr.Dropdown(
                                 label="pretrained D",
-                                choices = pretrained(sr2.value, 'D'),
-                                value= pretrained(sr2.value, 'D')[0] if len(pretrained(sr2.value, 'G')) > 0 else '',
+                                choices=[],
+                                value="",
                                 visible=True,
                                 interactive=True
                             )
+
+                            def update_pretrained_dropdowns(sr, f0, ver):
+                                g_choices = get_pretrained_choices(sr, 'G')
+                                d_choices = get_pretrained_choices(sr, 'D')
+                                return (
+                                    gr.update(choices=g_choices, value=g_choices[0] if g_choices else ""),
+                                    gr.update(choices=d_choices, value=d_choices[0] if d_choices else "")
+                                )
+                            
+                            # Bind update function to changes in sr2 or version19
+                            sr2.change(fn=update_pretrained_dropdowns, inputs=[sr2, if_f0_3, version19], outputs=[pretrained_G14, pretrained_D15])
+                            version19.change(fn=update_pretrained_dropdowns, inputs=[sr2, if_f0_3, version19], outputs=[pretrained_G14, pretrained_D15])
+
                     with gr.Row():
                         download_model = gr.Button('5.Download Model')
                     with gr.Row():
-                        model_files = gr.Files(label='Your Model and Index file can be downloaded here:')
+                        # Replaced gr.Files with gr.File
+                        model_files = gr.File(label='Your Model and Index file can be downloaded here:', file_count="multiple")
                         download_model.click(
                             fn=lambda name: os.listdir(f'assets/weights/{name}') + glob.glob(f'logs/{name.split(".")[0]}/added_*.index'),
                             inputs=[training_name], 
-                            outputs=[model_files, info3])
-                    with gr.Row():
-                        sr2.change(
-                            change_sr2,
-                            [sr2, if_f0_3, version19],
-                            [pretrained_G14, pretrained_D15],
-                        )
-                        version19.change(
-                            change_version19,
-                            [sr2, if_f0_3, version19],
-                            [pretrained_G14, pretrained_D15, sr2],
-                        )
-                        if_f0_3.change(
-                            change_f0,
-                            [if_f0_3, sr2, version19],
-                            [f0method8, pretrained_G14, pretrained_D15],
-                        )
-                    with gr.Row():
-                        but5 = gr.Button("1 Click Training", variant="primary", visible=False)
-                        but3.click(
-                            click_train,
-                            [
-                                training_name,
-                                sr2,
-                                if_f0_3,
-                                spk_id5,
-                                save_epoch10,
-                                total_epoch11,
-                                batch_size12,
-                                if_save_latest13,
-                                pretrained_G14,
-                                pretrained_D15,
-                                gpus16,
-                                if_cache_gpu17,
-                                if_save_every_weights18,
-                                version19,
-                            ],
-                            info3,
-                            api_name="train_start",
-                        )
-                        but4.click(train_index, [training_name, version19], info3)
-                        but5.click(
-                            train1key,
-                            [
-                                training_name,
-                                sr2,
-                                if_f0_3,
-                                dataset_folder,
-                                spk_id5,
-                                np7,
-                                f0method8,
-                                save_epoch10,
-                                total_epoch11,
-                                batch_size12,
-                                if_save_latest13,
-                                pretrained_G14,
-                                pretrained_D15,
-                                gpus16,
-                                if_cache_gpu17,
-                                if_save_every_weights18,
-                                version19,
-                                gpus_rmvpe,
-                            ],
-                            info3,
-                            api_name="train_start_all",
+                            outputs=[model_files, info3]
                         )
+                    
+                    if_f0_3.change(
+                        change_f0,
+                        [if_f0_3, sr2, version19],
+                        [f0method8, pretrained_G14, pretrained_D15],
+                    )
+                    
+                    but5 = gr.Button("1 Click Training", variant="primary", visible=False)
+                    but3.click(
+                        click_train,
+                        [
+                            training_name,
+                            sr2,
+                            if_f0_3,
+                            spk_id5,
+                            save_epoch10,
+                            total_epoch11,
+                            batch_size12,
+                            if_save_latest13,
+                            pretrained_G14,
+                            pretrained_D15,
+                            gpus16,
+                            if_cache_gpu17,
+                            if_save_every_weights18,
+                            version19,
+                        ],
+                        info3,
+                        api_name="train_start",
+                    )
+                    but4.click(train_index, [training_name, version19], info3)
+                    but5.click(
+                        train1key,
+                        [
+                            training_name,
+                            sr2,
+                            if_f0_3,
+                            dataset_folder,
+                            spk_id5,
+                            np7,
+                            f0method8,
+                            save_epoch10,
+                            total_epoch11,
+                            batch_size12,
+                            if_save_latest13,
+                            pretrained_G14,
+                            pretrained_D15,
+                            gpus16,
+                            if_cache_gpu17,
+                            if_save_every_weights18,
+                            version19,
+                            gpus_rmvpe,
+                        ],
+                        info3,
+                        api_name="train_start_all",
+                    )
+
+    # Populate UI on load instead of using lambdas in value
+    app.load(
+        fn=refresh_ui,
+        inputs=[],
+        outputs=[voice_model, file_index2, input_audio0]
+    )
 
     if config.iscolab:
-        app.queue().launch(share=True)
+        app.launch(share=True, queue=True)
     else:
-        app.queue().launch(
+        app.launch(
             server_name="0.0.0.0",
             inbrowser=not config.noautoopen,
             server_port=config.listen_port,
             quiet=True,
-        )
+            queue=True
+        )