Update app.py

app.py

@@ -18,14 +18,6 @@ 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():
-        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("*"))
-
 
 def load_model() -> tf.keras.Model:
     path = huggingface_hub.hf_hub_download("public-data/DeepDanbooru", "model-resnet_custom_v3.h5")
@@ -70,8 +62,6 @@ def predict(url: str, score_threshold: float) -> tuple[dict[str, float], dict[st
     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)
@@ -83,21 +73,7 @@ with gr.Blocks(css="style.css") as demo:
             
             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=[url, score_threshold],
-        outputs=[result, result_json, result_text],
-        fn=predict,
-        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
-    )
+        
 
     run_button.click(
         fn=predict,