Update

README.md

@@ -4,7 +4,7 @@ emoji: 🏃
 colorFrom: red
 colorTo: green
 sdk: gradio
-sdk_version: 3.19.1
+sdk_version: 3.34.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -18,13 +18,9 @@ sys.path.insert(0, 'bizarre-pose-estimator')
 
 from _util.twodee_v0 import I as ImageWrapper
 
-TITLE = 'ShuhongChen/bizarre-pose-estimator (tagger)'
-DESCRIPTION = 'This is an unofficial demo for https://github.com/ShuhongChen/bizarre-pose-estimator.'
+DESCRIPTION = '# [ShuhongChen/bizarre-pose-estimator (tagger)](https://github.com/ShuhongChen/bizarre-pose-estimator)'
 
-HF_TOKEN = os.getenv('HF_TOKEN')
-MODEL_REPO = 'hysts/bizarre-pose-estimator-models'
-MODEL_FILENAME = 'tagger.pth'
-LABEL_FILENAME = 'tags.txt'
+MODEL_REPO = 'public-data/bizarre-pose-estimator-models'
 
 
 def load_sample_image_paths() -> list[pathlib.Path]:
@@ -33,17 +29,14 @@ def load_sample_image_paths() -> list[pathlib.Path]:
         dataset_repo = 'hysts/sample-images-TADNE'
         path = huggingface_hub.hf_hub_download(dataset_repo,
                                                'images.tar.gz',
-                                               repo_type='dataset',
-                                               use_auth_token=HF_TOKEN)
+                                               repo_type='dataset')
         with tarfile.open(path) as f:
             f.extractall()
     return sorted(image_dir.glob('*'))
 
 
 def load_model(device: torch.device) -> torch.nn.Module:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           MODEL_FILENAME,
-                                           use_auth_token=HF_TOKEN)
+    path = huggingface_hub.hf_hub_download(MODEL_REPO, 'tagger.pth')
     state_dict = torch.load(path)
     model = torchvision.models.resnet50(num_classes=1062)
     model.load_state_dict(state_dict)
@@ -53,9 +46,7 @@ def load_model(device: torch.device) -> torch.nn.Module:
 
 
 def load_labels() -> list[str]:
-    label_path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                                 LABEL_FILENAME,
-                                                 use_auth_token=HF_TOKEN)
+    label_path = huggingface_hub.hf_hub_download(MODEL_REPO, 'tags.txt')
     with open(label_path) as f:
         labels = [line.strip() for line in f.readlines()]
     return labels
@@ -88,20 +79,27 @@ device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 model = load_model(device)
 labels = load_labels()
 
-func = functools.partial(predict, device=device, model=model, labels=labels)
-
-gr.Interface(
-    fn=func,
-    inputs=[
-        gr.Image(label='Input', type='pil'),
-        gr.Slider(label='Score Threshold',
-                  minimum=0,
-                  maximum=1,
-                  step=0.05,
-                  value=0.5),
-    ],
-    outputs=gr.Label(label='Output'),
-    examples=examples,
-    title=TITLE,
-    description=DESCRIPTION,
-).queue().launch(show_api=False)
+fn = functools.partial(predict, device=device, model=model, labels=labels)
+
+with gr.Blocks(css='style.css') as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label='Input', type='pil')
+            threshold = gr.Slider(label='Score Threshold',
+                                  minimum=0,
+                                  maximum=1,
+                                  step=0.05,
+                                  value=0.5)
+            run_button = gr.Button('Run')
+        with gr.Column():
+            result = gr.Label(label='Output')
+
+    inputs = [image, threshold]
+    gr.Examples(examples=examples,
+                inputs=inputs,
+                outputs=result,
+                fn=fn,
+                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
+    run_button.click(fn=fn, inputs=inputs, outputs=result, api_name='predict')
+demo.queue(max_size=15).launch()

style.css

@@ -0,0 +1,3 @@
+h1 {
+  text-align: center;
+}