Move login button into tab container and force activate

app.py

@@ -13,6 +13,7 @@ from pathlib import Path
 from typing import List, Optional, Tuple
 
 import gradio as gr
+from gradio.context import get_blocks_context
 import librosa
 import numpy as np
 import soundfile as sf
@@ -1397,191 +1398,198 @@ with gr.Blocks(title="Representation Chizzler") as demo:
         "denoising. Use the Single File tab for ad-hoc processing or the Dataset tab "
         "to clean and publish a dataset to the Hugging Face Hub."
     )
-    with gr.Row():
-        gr.LoginButton()
-
-    with gr.Tab("Single File"):
-        audio_input = gr.Audio(label="Upload Audio File", type="filepath")
-        vad_slider = gr.Slider(
-            minimum=0.1,
-            maximum=0.9,
-            value=0.5,
-            step=0.1,
-            label="VAD Threshold (higher = stricter voice detection)",
-        )
-        gap_slider = gr.Slider(
-            minimum=1.0,
-            maximum=10.0,
-            value=4.0,
-            step=0.5,
-            label="Max Silence Gap (seconds)",
-        )
-        normalize_checkbox = gr.Checkbox(
-            label="Normalize volume", value=True
-        )
-        target_db_slider = gr.Slider(
-            minimum=-35.0,
-            maximum=-10.0,
-            value=DEFAULT_TARGET_DBFS,
-            step=1.0,
-            label="Target loudness (dBFS)",
-        )
-        max_boost_slider = gr.Slider(
-            minimum=0.0,
-            maximum=30.0,
-            value=DEFAULT_MAX_BOOST_DB,
-            step=1.0,
-            label="Max boost (dB)",
-        )
-        max_atten_slider = gr.Slider(
-            minimum=0.0,
-            maximum=20.0,
-            value=DEFAULT_MAX_ATTEN_DB,
-            step=1.0,
-            label="Max attenuation (dB)",
-        )
-        run_button = gr.Button("Process Audio")
-        original_audio = gr.Audio(label="Original Audio")
-        vad_audio = gr.Audio(label="VAD Processed (Speech Only)")
-        denoised_audio = gr.Audio(label="Final Denoised")
-        details_box = gr.Textbox(label="Processing Details", lines=10)
-
-        run_button.click(
-            fn=gradio_single_file,
-            inputs=[
-                audio_input,
-                vad_slider,
-                gap_slider,
-                normalize_checkbox,
-                target_db_slider,
-                max_boost_slider,
-                max_atten_slider,
-            ],
-            outputs=[original_audio, vad_audio, denoised_audio, details_box],
-            concurrency_limit=1,
-        )
+    with gr.Column():
+        with gr.Row():
+            login_button = gr.LoginButton()
+            if get_blocks_context() is None:
+                try:
+                    login_button.activate()
+                except Exception:
+                    pass
+
+        with gr.Tabs():
+            with gr.Tab("Single File"):
+                audio_input = gr.Audio(label="Upload Audio File", type="filepath")
+                vad_slider = gr.Slider(
+                    minimum=0.1,
+                    maximum=0.9,
+                    value=0.5,
+                    step=0.1,
+                    label="VAD Threshold (higher = stricter voice detection)",
+                )
+                gap_slider = gr.Slider(
+                    minimum=1.0,
+                    maximum=10.0,
+                    value=4.0,
+                    step=0.5,
+                    label="Max Silence Gap (seconds)",
+                )
+                normalize_checkbox = gr.Checkbox(
+                    label="Normalize volume", value=True
+                )
+                target_db_slider = gr.Slider(
+                    minimum=-35.0,
+                    maximum=-10.0,
+                    value=DEFAULT_TARGET_DBFS,
+                    step=1.0,
+                    label="Target loudness (dBFS)",
+                )
+                max_boost_slider = gr.Slider(
+                    minimum=0.0,
+                    maximum=30.0,
+                    value=DEFAULT_MAX_BOOST_DB,
+                    step=1.0,
+                    label="Max boost (dB)",
+                )
+                max_atten_slider = gr.Slider(
+                    minimum=0.0,
+                    maximum=20.0,
+                    value=DEFAULT_MAX_ATTEN_DB,
+                    step=1.0,
+                    label="Max attenuation (dB)",
+                )
+                run_button = gr.Button("Process Audio")
+                original_audio = gr.Audio(label="Original Audio")
+                vad_audio = gr.Audio(label="VAD Processed (Speech Only)")
+                denoised_audio = gr.Audio(label="Final Denoised")
+                details_box = gr.Textbox(label="Processing Details", lines=10)
+
+                run_button.click(
+                    fn=gradio_single_file,
+                    inputs=[
+                        audio_input,
+                        vad_slider,
+                        gap_slider,
+                        normalize_checkbox,
+                        target_db_slider,
+                        max_boost_slider,
+                        max_atten_slider,
+                    ],
+                    outputs=[original_audio, vad_audio, denoised_audio, details_box],
+                    concurrency_limit=1,
+                )
 
-    with gr.Tab("Dataset to Hub"):
-        dataset_id_input = gr.Textbox(
-            label="Dataset ID or URL",
-            value="https://huggingface.co/datasets/MohammadGholizadeh/fleurs-farsi",
-        )
-        config_input = gr.Textbox(label="Config (optional)", value="")
-        split_input = gr.Textbox(label="Split (optional, or 'all')", value="dev")
-        audio_column_input = gr.Textbox(
-            label="Audio column (optional, auto-detect if empty)", value=""
-        )
-        output_repo_input = gr.Textbox(
-            label="Output dataset repo (optional)", value=""
-        )
-        private_checkbox = gr.Checkbox(label="Create private repo", value=False)
-        max_examples_input = gr.Number(
-            label="Max examples per split (optional)", value=None
-        )
-        resume_checkbox = gr.Checkbox(
-            label="Resume from cached shards", value=True
-        )
-        auto_resume_checkbox = gr.Checkbox(
-            label="Auto-resume on ZeroGPU preemption",
-            value=DEFAULT_AUTO_RESUME,
-        )
-        cache_to_hub_checkbox = gr.Checkbox(
-            label="Cache shards on Hub (recommended for ZeroGPU)",
-            value=DEFAULT_CACHE_TO_HUB,
-        )
-        shard_size_input = gr.Number(
-            label="Shard size (examples)", value=25
-        )
-        max_shards_input = gr.Number(
-            label="Max shards per run (ZeroGPU: 1-5, 0 = no limit)",
-            value=DEFAULT_MAX_SHARDS_PER_RUN,
-        )
-        vad_slider_ds = gr.Slider(
-            minimum=0.1,
-            maximum=0.9,
-            value=0.5,
-            step=0.1,
-            label="VAD Threshold",
-        )
-        gap_slider_ds = gr.Slider(
-            minimum=1.0,
-            maximum=10.0,
-            value=4.0,
-            step=0.5,
-            label="Max Silence Gap (seconds)",
-        )
-        normalize_checkbox_ds = gr.Checkbox(
-            label="Normalize volume", value=True
-        )
-        target_db_slider_ds = gr.Slider(
-            minimum=-35.0,
-            maximum=-10.0,
-            value=DEFAULT_TARGET_DBFS,
-            step=1.0,
-            label="Target loudness (dBFS)",
-        )
-        max_boost_slider_ds = gr.Slider(
-            minimum=0.0,
-            maximum=30.0,
-            value=DEFAULT_MAX_BOOST_DB,
-            step=1.0,
-            label="Max boost (dB)",
-        )
-        max_atten_slider_ds = gr.Slider(
-            minimum=0.0,
-            maximum=20.0,
-            value=DEFAULT_MAX_ATTEN_DB,
-            step=1.0,
-            label="Max attenuation (dB)",
-        )
-        process_button = gr.Button(
-            "Process/Resume Dataset (cache & push when complete)"
-        )
-        assemble_button = gr.Button(
-            "Assemble & Push Cached Dataset"
-        )
-        status_box = gr.Textbox(label="Status", lines=6)
-
-        process_button.click(
-            fn=process_dataset_and_push,
-            inputs=[
-                dataset_id_input,
-                config_input,
-                split_input,
-                audio_column_input,
-                output_repo_input,
-                private_checkbox,
-                vad_slider_ds,
-                gap_slider_ds,
-                normalize_checkbox_ds,
-                target_db_slider_ds,
-                max_boost_slider_ds,
-                max_atten_slider_ds,
-                max_examples_input,
-                resume_checkbox,
-                auto_resume_checkbox,
-                shard_size_input,
-                cache_to_hub_checkbox,
-                max_shards_input,
-            ],
-            outputs=[status_box],
-            concurrency_limit=1,
-        )
+            with gr.Tab("Dataset to Hub"):
+                dataset_id_input = gr.Textbox(
+                    label="Dataset ID or URL",
+                    value="https://huggingface.co/datasets/MohammadGholizadeh/fleurs-farsi",
+                )
+                config_input = gr.Textbox(label="Config (optional)", value="")
+                split_input = gr.Textbox(label="Split (optional, or 'all')", value="dev")
+                audio_column_input = gr.Textbox(
+                    label="Audio column (optional, auto-detect if empty)", value=""
+                )
+                output_repo_input = gr.Textbox(
+                    label="Output dataset repo (optional)", value=""
+                )
+                private_checkbox = gr.Checkbox(label="Create private repo", value=False)
+                max_examples_input = gr.Number(
+                    label="Max examples per split (optional)", value=None
+                )
+                resume_checkbox = gr.Checkbox(
+                    label="Resume from cached shards", value=True
+                )
+                auto_resume_checkbox = gr.Checkbox(
+                    label="Auto-resume on ZeroGPU preemption",
+                    value=DEFAULT_AUTO_RESUME,
+                )
+                cache_to_hub_checkbox = gr.Checkbox(
+                    label="Cache shards on Hub (recommended for ZeroGPU)",
+                    value=DEFAULT_CACHE_TO_HUB,
+                )
+                shard_size_input = gr.Number(
+                    label="Shard size (examples)", value=25
+                )
+                max_shards_input = gr.Number(
+                    label="Max shards per run (ZeroGPU: 1-5, 0 = no limit)",
+                    value=DEFAULT_MAX_SHARDS_PER_RUN,
+                )
+                vad_slider_ds = gr.Slider(
+                    minimum=0.1,
+                    maximum=0.9,
+                    value=0.5,
+                    step=0.1,
+                    label="VAD Threshold",
+                )
+                gap_slider_ds = gr.Slider(
+                    minimum=1.0,
+                    maximum=10.0,
+                    value=4.0,
+                    step=0.5,
+                    label="Max Silence Gap (seconds)",
+                )
+                normalize_checkbox_ds = gr.Checkbox(
+                    label="Normalize volume", value=True
+                )
+                target_db_slider_ds = gr.Slider(
+                    minimum=-35.0,
+                    maximum=-10.0,
+                    value=DEFAULT_TARGET_DBFS,
+                    step=1.0,
+                    label="Target loudness (dBFS)",
+                )
+                max_boost_slider_ds = gr.Slider(
+                    minimum=0.0,
+                    maximum=30.0,
+                    value=DEFAULT_MAX_BOOST_DB,
+                    step=1.0,
+                    label="Max boost (dB)",
+                )
+                max_atten_slider_ds = gr.Slider(
+                    minimum=0.0,
+                    maximum=20.0,
+                    value=DEFAULT_MAX_ATTEN_DB,
+                    step=1.0,
+                    label="Max attenuation (dB)",
+                )
+                process_button = gr.Button(
+                    "Process/Resume Dataset (cache & push when complete)"
+                )
+                assemble_button = gr.Button(
+                    "Assemble & Push Cached Dataset"
+                )
+                status_box = gr.Textbox(label="Status", lines=6)
+
+                process_button.click(
+                    fn=process_dataset_and_push,
+                    inputs=[
+                        dataset_id_input,
+                        config_input,
+                        split_input,
+                        audio_column_input,
+                        output_repo_input,
+                        private_checkbox,
+                        vad_slider_ds,
+                        gap_slider_ds,
+                        normalize_checkbox_ds,
+                        target_db_slider_ds,
+                        max_boost_slider_ds,
+                        max_atten_slider_ds,
+                        max_examples_input,
+                        resume_checkbox,
+                        auto_resume_checkbox,
+                        shard_size_input,
+                        cache_to_hub_checkbox,
+                        max_shards_input,
+                    ],
+                    outputs=[status_box],
+                    concurrency_limit=1,
+                )
 
-        assemble_button.click(
-            fn=assemble_cached_dataset_and_push,
-            inputs=[
-                dataset_id_input,
-                config_input,
-                split_input,
-                audio_column_input,
-                output_repo_input,
-                private_checkbox,
-                cache_to_hub_checkbox,
-            ],
-            outputs=[status_box],
-            concurrency_limit=1,
-        )
+                assemble_button.click(
+                    fn=assemble_cached_dataset_and_push,
+                    inputs=[
+                        dataset_id_input,
+                        config_input,
+                        split_input,
+                        audio_column_input,
+                        output_repo_input,
+                        private_checkbox,
+                        cache_to_hub_checkbox,
+                    ],
+                    outputs=[status_box],
+                    concurrency_limit=1,
+                )
 
 
 demo.queue()