Gradio 6.15 + module-level demo (PaGeR ZeroGPU pattern)

Single on_run handler; backup at backup/app_pre_dap_debug.py

README.md

@@ -4,7 +4,7 @@ emoji: 🌐
 colorFrom: blue
 colorTo: green
 sdk: gradio
-sdk_version: 5.50.0
+sdk_version: 6.15.1
 app_file: app.py
 pinned: false
 license: apache-2.0
@@ -28,7 +28,8 @@ Upload a 360° equirectangular image and click **Compute Depth** for a **scale-i
 This Space is tuned for **ZeroGPU** on `zero-a10g`:
 
 - **Large** checkpoint only (`DA360_large.pth`, ~1.3GB).
-- ZeroGPU: `@spaces.GPU(duration=120) compute_depth` bound **directly** to the Run button (HF requirement).
+- **Gradio 6.15** + module-level `demo` (same pattern as [PaGeR](https://huggingface.co/spaces/prs-eth/PaGeR)).
+- ZeroGPU: `@spaces.GPU(duration=120) compute_depth` bound directly to **Compute Depth**.
 - Weights from [`hljiang/DA360`](https://huggingface.co/hljiang/DA360) are **preloaded** (`preload_from_hub`) and packed on `cuda` at startup.
 
 **Tips**

app.py

@@ -723,68 +723,81 @@ def predict(image_path, model_path, scene_type="Outdoor"):
     return viewer_html, images
 
 
-def resolve_default_model(model_path=None):
-    return DEFAULT_MODEL
+def _startup(download_all=False):
+    """Load Large weights and pack on cuda (module scope — PaGeR / ZeroGPU rule)."""
+    startup_model = DEFAULT_MODEL
+    if IS_HF_SPACE or not os.path.exists(startup_model):
+        bootstrap_models(startup_model, download_all=download_all)
+    elif os.path.exists(startup_model):
+        preload_model_to_cpu(startup_model)
+        if torch.cuda.is_available():
+            move_model_to_gpu()
 
 
-def build_demo(model_path=DEFAULT_MODEL):
-    examples = get_example_images()
+if IS_HF_SPACE:
+    print(
+        f"[startup] SPACE_ID={os.environ.get('SPACE_ID')} "
+        f"SPACES_ZERO_GPU={os.environ.get('SPACES_ZERO_GPU')}",
+        flush=True,
+    )
+    _startup()
+    ensure_viewer_html()
 
-    with gr.Blocks(title="DA360 Demo", css=DEMO_CSS) as demo:
+_EXAMPLE_PATHS = [p[0] for p in get_example_images()]
+
+# PaGeR pattern: module-level Blocks so ZeroGPU startup scan sees @spaces.GPU handlers.
+with gr.Blocks(title="DA360 Demo") as demo:
+    gr.HTML(f"<style>{DEMO_CSS}</style>")
+    gr.Markdown(
+        textwrap.dedent(
+            """
+            # Depth Anything in 360° (debug)
+            **Large** checkpoint only. ZeroGPU inference;
+            **scale-invariant** depth + KITTI colormap (not metric depth).
+
+            [Project Page](https://insta360-research-team.github.io/DA360/) |
+            [Paper](https://arxiv.org/abs/2512.22819) |
+            [GitHub](https://github.com/insta360-research-team/DA360)
+            """
+        ).strip()
+    )
+    if not IS_HF_SPACE:
         gr.Markdown(
-            textwrap.dedent(
-                """
-                # Depth Anything in 360° (debug)
-                **Large** checkpoint only. DAP-style ZeroGPU inference;
-                **scale-invariant** depth + KITTI colormap (not metric depth).
-
-                [Project Page](https://insta360-research-team.github.io/DA360/) |
-                [Paper](https://arxiv.org/abs/2512.22819) |
-                [GitHub](https://github.com/insta360-research-team/DA360)
-                """
-            ).strip()
+            "**Local mode:** expects `./checkpoints/DA360_large.pth`. "
+            "Full UI backup: `backup/app_pre_dap_debug.py`."
         )
-        if not IS_HF_SPACE:
-            gr.Markdown(
-                "**Local mode:** expects `./checkpoints/DA360_large.pth`. "
-                "Full UI backup: `backup/app_pre_dap_debug.py`."
-            )
 
-        panel_height = 360
-
-        with gr.Row():
-            with gr.Column(scale=1):
-                image_input = gr.Image(
-                    label="Panoramic Input",
-                    type="filepath",
-                    height=panel_height,
-                    elem_classes=["panorama-panel"],
-                )
-                run_btn = gr.Button("Compute Depth", variant="primary")
-            with gr.Column(scale=1):
-                depth_output = gr.Image(
-                    label="Depth (KITTI colormap)",
-                    type="numpy",
-                    height=panel_height,
-                    elem_classes=["depth-panel"],
-                )
-
-        # ZeroGPU startup scan only sees @spaces.GPU on the function passed to .click().
-        run_btn.click(
-            fn=compute_depth,
-            inputs=image_input,
-            outputs=depth_output,
-        )
+    panel_height = 360
 
-        if examples:
-            example_paths = [p[0] for p in examples]
-            gr.Examples(
-                examples=example_paths,
-                inputs=image_input,
-                cache_examples=False,
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(
+                label="Panoramic Input",
+                type="filepath",
+                height=panel_height,
+                elem_classes=["panorama-panel"],
             )
+            run_btn = gr.Button("Compute Depth", variant="primary")
+        with gr.Column(scale=1):
+            depth_output = gr.Image(
+                label="Depth (KITTI colormap)",
+                type="numpy",
+                height=panel_height,
+                elem_classes=["depth-panel"],
+            )
+
+    run_btn.click(
+        fn=compute_depth,
+        inputs=image_input,
+        outputs=depth_output,
+    )
 
-    return demo
+    if _EXAMPLE_PATHS:
+        gr.Examples(
+            examples=_EXAMPLE_PATHS,
+            inputs=image_input,
+            cache_examples=False,
+        )
 
 
 def resolve_server_port(preferred_port):
@@ -813,28 +826,15 @@ if __name__ == "__main__":
     parser.add_argument("--download", action="store_true", help="download missing checkpoints")
     args = parser.parse_args()
 
-    startup_model = DEFAULT_MODEL
-    should_fetch = args.download or IS_HF_SPACE or not os.path.exists(startup_model)
-    if should_fetch:
-        bootstrap_models(
-            startup_model,
-            download_all=bool(args.download),
-        )
-    elif os.path.exists(startup_model):
-        preload_model_to_cpu(startup_model)
-        if torch.cuda.is_available():
-            move_model_to_gpu()
+    if not IS_HF_SPACE:
+        _startup(download_all=bool(args.download))
+        ensure_viewer_html()
 
-    demo = build_demo()
-    ensure_viewer_html()
     server_port = resolve_server_port(args.server_port)
-    # ssr_mode=False: older SSR paths could drop HF login identity and fall back
-    # to the tiny anonymous ZeroGPU IP quota (see Gradio >=5.12 ZeroGPU fixes).
-    demo.queue(default_concurrency_limit=1, max_size=16).launch(
+    demo.queue(default_concurrency_limit=1).launch(
         server_name=args.server_name,
         server_port=server_port,
         share=args.share,
         allowed_paths=[os.path.abspath(CACHE_DIR)],
         show_error=True,
-        ssr_mode=False,
     )

requirements.txt

@@ -1,7 +1,7 @@
 torch==2.8.0
 torchvision==0.23.0
 spaces
-gradio==5.50.0
+gradio>=6.15.1
 huggingface_hub>=0.33.5
 matplotlib
 scipy