Update app.py

app.py

@@ -1,7 +1,4 @@
 import json
-import warnings
-import sys
-
 import torch
 import torch.nn as nn
 from torchvision import transforms
@@ -10,33 +7,6 @@ from PIL import Image
 import gradio as gr
 
 
-# ---- Silence noisy but harmless warnings in some environments ----
-warnings.filterwarnings(
-    "ignore",
-    category=FutureWarning,
-    module=r"spaces\._vendor\.codefind\..*",
-)
-
-# Silence "Exception ignored in: BaseEventLoop.__del__ ... Invalid file descriptor: -1"
-# This is an unraisable exception printed during interpreter shutdown in some stacks.
-def _silence_asyncio_shutdown_unraisable(unraisable):
-    try:
-        msg = str(unraisable.exc_value)
-        obj_name = type(unraisable.object).__name__ if unraisable.object is not None else ""
-        if (
-            unraisable.exc_type is ValueError
-            and msg.startswith("Invalid file descriptor")
-            and "EventLoop" in obj_name
-        ):
-            return
-    except Exception:
-        pass
-    sys.__unraisablehook__(unraisable)
-
-sys.unraisablehook = _silence_asyncio_shutdown_unraisable
-# ---------------------------------------------------------------
-
-
 # ---------- CONFIG ----------
 CKPT_PATH = "best_effnet_twohead.pt"
 LABELS_PATH = "labels.json"
@@ -116,7 +86,6 @@ def predict(image: Image.Image):
 
 EXAMPLE_TEXT = """
 **Taxa (12):** Cacoxnus indagator, Chelostoma florisomne, Coeliopencyrtus, Eumenidae, Heriades, Hylaeus, Megachile, Osmia bicornis, Osmia cornuta, Passaloecus, Psenulus, Trypoxylon
-
 **States (4):** DauLv, DeadLv, Lv, OldFood
 """
 
@@ -130,7 +99,7 @@ STATUS_TABLE = [
 
 theme = gr.themes.Soft()
 
-with gr.Blocks(title="LTN EfficientNet Two-Head Classifier") as demo:
+with gr.Blocks(theme=theme, title="LTN EfficientNet Two-Head Classifier") as demo:
     gr.Markdown(
         "# EfficientNet Two-Head Layer Trap Nest (LTN) Classifier\n"
         "Upload a brood-cell image crop to predict **taxon** and **developmental state**.\n"
@@ -151,7 +120,7 @@ with gr.Blocks(title="LTN EfficientNet Two-Head Classifier") as demo:
                 interactive=False,
                 wrap=True,
                 row_count=(len(STATUS_TABLE), "fixed"),
-                column_count=(2, "fixed"),
+                col_count=(2, "fixed"),
             )
 
         with gr.Column(scale=1):
@@ -167,10 +136,10 @@ with gr.Blocks(title="LTN EfficientNet Two-Head Classifier") as demo:
                 inputs=img,
             )
 
-    #gr.Markdown(
-        #"<small>Note: The status legend is a human-readable mapping. "
-        #"Your model output labels come from `labels.json`.</small>"
-    #)
+    gr.Markdown(
+        "<small>Note: The status legend is a human-readable mapping. "
+        "Your model output labels come from `labels.json`.</small>"
+    )
 
 if __name__ == "__main__":
-    demo.launch(theme=theme, ssr_mode=False, allowed_paths=["."])
+    demo.launch()