Update app.py

app.py

@@ -27,35 +27,53 @@ css = """
     height: 62px;
 }
 """
+
 DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+
 model_configs = {
     'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
     'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
     'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
-    'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+    'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]},
 }
+
 encoder2name = {
     'vits': 'Small',
     'vitb': 'Base',
     'vitl': 'Large',
-    'vitg': 'Giant', # we are undergoing company review procedures to release our giant model checkpoint
+    'vitg': 'Giant',
 }
+
 encoder = 'vits'
 model_name = encoder2name[encoder]
+
 model = DepthAnythingV2(**model_configs[encoder])
-filepath = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_name}", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model")
+filepath = hf_hub_download(
+    repo_id=f"depth-anything/Depth-Anything-V2-{model_name}",
+    filename=f"depth_anything_v2_{encoder}.pth",
+    repo_type="model"
+)
 state_dict = torch.load(filepath, map_location="cpu")
 model.load_state_dict(state_dict)
 model = model.to(DEVICE).eval()
 
 title = "# Depth Anything V2"
 description = """Official demo for **Depth Anything V2**.
-Please refer to our [paper](https://arxiv.org/abs/2406.09414), [project page](https://depth-anything-v2.github.io), and [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details."""
+Please refer to our [paper](https://arxiv.org/abs/2406.09414),
+[project page](https://depth-anything-v2.github.io),
+and [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details."""
 
 @spaces.GPU
 def predict_depth(image):
     return model.infer_image(image)
 
+# -------------------------------------
+# OLD GRADIO COMPATIBILITY PATCH
+# -------------------------------------
+if not hasattr(gr.Blocks, "get_api_info"):
+    gr.Blocks.get_api_info = lambda self: {}
+# -------------------------------------
+
 with gr.Blocks(css=css) as demo:
     gr.Markdown(title)
     gr.Markdown(description)
@@ -63,18 +81,20 @@ with gr.Blocks(css=css) as demo:
 
     with gr.Row():
         input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
-        depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
+        depth_image_slider = ImageSlider(
+            label="Depth Map with Slider View",
+            elem_id='img-display-output',
+            position=0.5
+        )
+
     submit = gr.Button(value="Compute Depth")
-    gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download",)
-    raw_file = gr.File(label="16-bit raw output (can be considered as disparity)", elem_id="download",)
+    gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download")
+    raw_file = gr.File(label="16-bit raw output (can be considered as disparity)", elem_id="download")
 
     cmap = matplotlib.colormaps.get_cmap('Spectral_r')
 
     def on_submit(image):
         original_image = image.copy()
-
-        h, w = image.shape[:2]
-
         depth = predict_depth(image[:, :, ::-1])
 
         raw_depth = Image.fromarray(depth.astype('uint16'))
@@ -91,13 +111,22 @@ with gr.Blocks(css=css) as demo:
 
         return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name]
 
-    submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_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(cache_examples=False, examples=example_files, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_file], fn=on_submit)
-
+    submit.click(
+        on_submit,
+        inputs=[input_image],
+        outputs=[depth_image_slider, gray_depth_file, raw_file]
+    )
+
+    if os.path.exists('assets/examples'):
+        example_files = sorted(os.listdir('assets/examples'))
+        example_files = [os.path.join('assets/examples', f) for f in example_files]
+        gr.Examples(
+            cache_examples=False,
+            examples=example_files,
+            inputs=[input_image],
+            outputs=[depth_image_slider, gray_depth_file, raw_file],
+            fn=on_submit
+        )
 
 if __name__ == '__main__':
     demo.queue().launch(share=True)