Update app.py

app.py

@@ -77,50 +77,6 @@ def main(share=True):
         metric_depth[~mask] = metric_depth[mask].max()
         return metric_depth, mask, intrinsics
 
-    with gr.Blocks(css=css) as demo:
-        gr.Markdown(title)
-        gr.Markdown(description)
-        gr.Markdown("### Depth Prediction demo")
-
-        with gr.Row():
-            # Left: input image + settings
-            with gr.Column():
-                input_image = gr.Image(label="Input Image", image_mode="RGB", type='numpy', elem_id='img-display-input')
-                with gr.Accordion(label="Settings", open=False):
-                    denoise_steps = gr.Slider(label="Denoising Steps", minimum=1, maximum=50, value=10, step=1)
-                    apply_filter = gr.Checkbox(label="Apply filter points", value=True)
-                submit_btn = gr.Button(value="Predict Depth")
-
-            # Right: 3D point cloud + depth
-            with gr.Column():
-                with gr.Tabs():
-                    with gr.Tab("3D View"):
-                        model_3d = gr.Model3D(display_mode="solid", label="3D Point Map", clear_color=[1,1,1,1], height="60vh")
-                    with gr.Tab("Depth"):
-                        depth_map = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
-    
-        concat_file = gr.File(label="Concatenated visualization (image+depth)", elem_id="image-depth-download")
-        raw_depth_file = gr.File(label="Raw depth output (saved as .npy)", elem_id="download")
-        pcd_file = gr.File(label="Point Cloud (.ply)", elem_id="download-ply")
-
-        submit_btn.click(
-            on_submit, 
-            inputs=[input_image, denoise_steps, apply_filter], 
-            outputs=[depth_map, model_3d, concat_file, raw_depth_file, pcd_file]
-        )
-
-        example_files = os.listdir('assets/examples')
-        example_files.sort()
-        example_files = [os.path.join('assets/examples', filename) for filename in example_files]
-        examples = gr.Examples(
-            examples=example_files, 
-            inputs=[input_image], 
-            outputs=[depth_map, model_3d, concat_file, raw_depth_file, pcd_file], 
-            fn=on_submit
-        )
-
-    demo.queue().launch(share=share) 
-
     def on_submit(image, denoise_steps, apply_filter):
 
         H, W = image.shape[:2]
@@ -167,6 +123,49 @@ def main(share=True):
 
         return [(image, colored_depth), tmp_ply.name, tmp_concat.name, tmp_raw_depth.name, tmp_ply.name]
 
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(title)
+        gr.Markdown(description)
+        gr.Markdown("### Depth Prediction demo")
+
+        with gr.Row():
+            # Left: input image + settings
+            with gr.Column():
+                input_image = gr.Image(label="Input Image", image_mode="RGB", type='numpy', elem_id='img-display-input')
+                with gr.Accordion(label="Settings", open=False):
+                    denoise_steps = gr.Slider(label="Denoising Steps", minimum=1, maximum=50, value=10, step=1)
+                    apply_filter = gr.Checkbox(label="Apply filter points", value=True)
+                submit_btn = gr.Button(value="Predict Depth")
+
+            # Right: 3D point cloud + depth
+            with gr.Column():
+                with gr.Tabs():
+                    with gr.Tab("3D View"):
+                        model_3d = gr.Model3D(display_mode="solid", label="3D Point Map", clear_color=[1,1,1,1], height="60vh")
+                    with gr.Tab("Depth"):
+                        depth_map = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
+    
+        concat_file = gr.File(label="Concatenated visualization (image+depth)", elem_id="image-depth-download")
+        raw_depth_file = gr.File(label="Raw depth output (saved as .npy)", elem_id="download")
+        pcd_file = gr.File(label="Point Cloud (.ply)", elem_id="download-ply")
+
+        submit_btn.click(
+            on_submit, 
+            inputs=[input_image, denoise_steps, apply_filter], 
+            outputs=[depth_map, model_3d, concat_file, raw_depth_file, pcd_file]
+        )
+
+        example_files = os.listdir('assets/examples')
+        example_files.sort()
+        example_files = [os.path.join('assets/examples', filename) for filename in example_files]
+        examples = gr.Examples(
+            examples=example_files, 
+            inputs=[input_image], 
+            outputs=[depth_map, model_3d, concat_file, raw_depth_file, pcd_file], 
+            fn=on_submit
+        )
+
+    demo.queue().launch(share=share) 
 
 if __name__ == '__main__':
     main(share=True)