Update app.py

app.py

@@ -15,7 +15,6 @@ import tensorflow as tf
 
 DESCRIPTION = "# [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)"
 
-
 def load_sample_image_paths() -> list[pathlib.Path]:
     image_dir = pathlib.Path("images")
     if not image_dir.exists():
@@ -24,24 +23,20 @@ def load_sample_image_paths() -> list[pathlib.Path]:
             f.extractall()
     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")
     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")
     with open(path) as f:
         labels = [line.strip() for line in f.readlines()]
     return labels
 
-
 model = load_model()
 labels = load_labels()
 
-
 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)
@@ -65,12 +60,12 @@ def predict(image: PIL.Image.Image, score_threshold: float) -> tuple[dict[str, f
     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:
     gr.Markdown(DESCRIPTION)
+    
     with gr.Row():
         with gr.Column():
             image = gr.Image(label="Input", type="pil")
@@ -84,6 +79,7 @@ with gr.Blocks(css="style.css") as demo:
                     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],