Update app.py

app.py

@@ -24,7 +24,7 @@ class Examples(gr.helpers.Examples):
 
 def load_predictor():
     """Load model predictor using torch.hub"""
-    predictor = torch.hub.load("hugoycj/StableNormal", "StableNormal", trust_repo=True)
+    predictor = torch.hub.load("hugoycj/StableNormal", "StableNormal", trust_repo=True, yoso_version='yoso-normal-v1-7')
     return predictor
 
 def process_image(
@@ -54,8 +54,6 @@ def create_demo():
     
     # Create processing functions for each data type
     process_object = spaces.GPU(functools.partial(process_image, predictor, data_type="object"))
-    process_scene = spaces.GPU(functools.partial(process_image, predictor, data_type="indoor"))
-    process_human = spaces.GPU(functools.partial(process_image, predictor, data_type="object"))
 
     # Define markdown content
     HEADER_MD = """
@@ -149,88 +147,6 @@ def create_demo():
                     examples_per_page=50,
                 )
 
-            # Scene Tab
-            with gr.Tab("Scene"):
-                with gr.Row():
-                    with gr.Column():
-                        scene_input = gr.Image(label="Input Scene Image", type="filepath")
-                        scene_sharpness = gr.Slider(
-                            minimum=1,
-                            maximum=10,
-                            value=DEFAULT_SHARPNESS,
-                            step=1,
-                            label="Sharpness (inference steps)",
-                            info="Higher values produce sharper results but take longer"
-                        )
-                        with gr.Row():
-                            scene_submit_btn = gr.Button("Compute Normal", variant="primary")
-                            scene_reset_btn = gr.Button("Reset")
-                    with gr.Column():
-                        scene_output_slider = ImageSlider(
-                            label="Normal outputs",
-                            type="filepath",
-                            show_download_button=True,
-                            show_share_button=True,
-                            interactive=False,
-                            elem_classes="slider",
-                            position=0.25,
-                        )
-
-                Examples(
-                    fn=process_scene,
-                    examples=sorted([
-                        os.path.join("files", "scene", name)
-                        for name in os.listdir(os.path.join("files", "scene"))
-                        if os.path.exists(os.path.join("files", "scene"))
-                    ]),
-                    inputs=[scene_input],
-                    outputs=[scene_output_slider],
-                    cache_examples=True,
-                    directory_name="examples_scene",
-                    examples_per_page=50,
-                )
-
-            # Human Tab
-            with gr.Tab("Human"):
-                with gr.Row():
-                    with gr.Column():
-                        human_input = gr.Image(label="Input Human Image", type="filepath")
-                        human_sharpness = gr.Slider(
-                            minimum=1,
-                            maximum=10,
-                            value=DEFAULT_SHARPNESS,
-                            step=1,
-                            label="Sharpness (inference steps)",
-                            info="Higher values produce sharper results but take longer"
-                        )
-                        with gr.Row():
-                            human_submit_btn = gr.Button("Compute Normal", variant="primary")
-                            human_reset_btn = gr.Button("Reset")
-                    with gr.Column():
-                        human_output_slider = ImageSlider(
-                            label="Normal outputs",
-                            type="filepath",
-                            show_download_button=True,
-                            show_share_button=True,
-                            interactive=False,
-                            elem_classes="slider",
-                            position=0.25,
-                        )
-
-                Examples(
-                    fn=process_human,
-                    examples=sorted([
-                        os.path.join("files", "human", name)
-                        for name in os.listdir(os.path.join("files", "human"))
-                        if os.path.exists(os.path.join("files", "human"))
-                    ]),
-                    inputs=[human_input],
-                    outputs=[human_output_slider],
-                    cache_examples=True,
-                    directory_name="examples_human",
-                    examples_per_page=50,
-                )
-
         # Event Handlers for Object Tab
         object_submit_btn.click(
             fn=lambda x, _: None if x else gr.Error("Please upload an image"),
@@ -250,44 +166,6 @@ def create_demo():
             queue=False,
         )
 
-        # Event Handlers for Scene Tab
-        scene_submit_btn.click(
-            fn=lambda x, _: None if x else gr.Error("Please upload an image"),
-            inputs=[scene_input, scene_sharpness],
-            outputs=None,
-            queue=False,
-        ).success(
-            fn=process_scene,
-            inputs=[scene_input, scene_sharpness],
-            outputs=[scene_output_slider],
-        )
-
-        scene_reset_btn.click(
-            fn=lambda: (None, DEFAULT_SHARPNESS, None),
-            inputs=[],
-            outputs=[scene_input, scene_sharpness, scene_output_slider],
-            queue=False,
-        )
-
-        # Event Handlers for Human Tab
-        human_submit_btn.click(
-            fn=lambda x, _: None if x else gr.Error("Please upload an image"),
-            inputs=[human_input, human_sharpness],
-            outputs=None,
-            queue=False,
-        ).success(
-            fn=process_human,
-            inputs=[human_input, human_sharpness],
-            outputs=[human_output_slider],
-        )
-
-        human_reset_btn.click(
-            fn=lambda: (None, DEFAULT_SHARPNESS, None),
-            inputs=[],
-            outputs=[human_input, human_sharpness, human_output_slider],
-            queue=False,
-        )
-
     return demo
 
 def main():