Update app.py

app.py

@@ -13,31 +13,26 @@ import numpy as np
 import PIL.Image
 import tensorflow as tf
 
-DESCRIPTION = '# [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)'
+DESCRIPTION = "# [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)"
 
 
 def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
+    image_dir = pathlib.Path("images")
     if not image_dir.exists():
-        path = huggingface_hub.hf_hub_download(
-            'public-data/sample-images-TADNE',
-            'images.tar.gz',
-            repo_type='dataset')
+        path = huggingface_hub.hf_hub_download("public-data/sample-images-TADNE", "images.tar.gz", repo_type="dataset")
         with tarfile.open(path) as f:
             f.extractall()
-    return sorted(image_dir.glob('*'))
+    return sorted(image_dir.glob("*"))
 
 
 def load_model() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
-                                           'model-resnet_custom_v3.h5')
+    path = huggingface_hub.hf_hub_download("public-data/DeepDanbooru", "model-resnet_custom_v3.h5")
     model = tf.keras.models.load_model(path)
     return model
 
 
 def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
-                                           'tags.txt')
+    path = huggingface_hub.hf_hub_download("public-data/DeepDanbooru", "tags.txt")
     with open(path) as f:
         labels = [line.strip() for line in f.readlines()]
     return labels
@@ -47,18 +42,13 @@ model = load_model()
 labels = load_labels()
 
 
-def predict(
-        image: PIL.Image.Image, score_threshold: float
-) -> tuple[dict[str, float], dict[str, float], str]:
+def predict(image: PIL.Image.Image, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]:
     _, height, width, _ = model.input_shape
     image = np.asarray(image)
-    image = tf.image.resize(image,
-                            size=(height, width),
-                            method=tf.image.ResizeMethod.AREA,
-                            preserve_aspect_ratio=True)
+    image = tf.image.resize(image, size=(height, width), method=tf.image.ResizeMethod.AREA, preserve_aspect_ratio=True)
     image = image.numpy()
     image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.
+    image = image / 255.0
     probs = model.predict(image[None, ...])[0]
     probs = probs.astype(float)
 
@@ -72,45 +62,35 @@ def predict(
         if prob < score_threshold:
             break
         result_threshold[label] = prob
-    result_text = ', '.join(result_all.keys())
+    result_text = ", ".join(result_all.keys())
     return result_threshold, result_all, result_text
 
 
 image_paths = load_sample_image_paths()
 examples = [[path.as_posix(), 0.5] for path in image_paths]
 
-with gr.Blocks(css='style.css') as demo:
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         with gr.Column():
-            image = gr.Image(label='Input', type='pil')
-            score_threshold = gr.Slider(label='Score threshold',
-                                        minimum=0,
-                                        maximum=1,
-                                        step=0.05,
-                                        value=0.5)
-            run_button = gr.Button('Run')
+            image = gr.Image(label="Input", type="pil")
+            score_threshold = gr.Slider(label="Score threshold", minimum=0, maximum=1, step=0.05, value=0.5)
+            run_button = gr.Button("Run")
         with gr.Column():
             with gr.Tabs():
-                with gr.Tab(label='Output'):
-                    result = gr.Label(label='Output', show_label=False)
-                with gr.Tab(label='JSON'):
-                    result_json = gr.JSON(label='JSON output',
-                                          show_label=False)
-                with gr.Tab(label='Text'):
-                    result_text = gr.Text(label='Text output',
-                                          show_label=False,
-                                          lines=5)
-#    gr.Examples(examples=examples,
-#                inputs=[image, score_threshold],
-#                outputs=[result, result_json, result_text],
-#                fn=predict,
-#                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-
+                with gr.Tab(label="Output"):
+                    result = gr.Label(label="Output", show_label=False)
+                with gr.Tab(label="JSON"):
+                    result_json = gr.JSON(label="JSON output", show_label=False)
+                with gr.Tab(label="Text"):
+                    result_text = gr.Text(label="Text output", show_label=False, lines=5)
+    
     run_button.click(
         fn=predict,
         inputs=[image, score_threshold],
         outputs=[result, result_json, result_text],
-        api_name='predict',
+        api_name="predict",
     )
-demo.queue().launch()
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()