Upload app.py with huggingface_hub

app.py

@@ -1,24 +1,22 @@
-
-import gradio as gr
-import PIL.Image
-import numpy as np
-import pandas as pd
-import pathlib
-import tempfile
 import os
 import shutil
 import zipfile
+import pathlib
+import tempfile
+import gradio as gr
+import pandas as pd
+import numpy as np
+import PIL.Image
 import huggingface_hub as h
-from huggingface_hub import HfApi, Repository
 import autogluon.multimodal
 
 model_repo_id = "nadakandrew/sign-identification-autogluon"
-zip_filename  = "autogluon_image_predictor_dir.zip"
+zip_filename = "autogluon_image_predictor_dir.zip"
 HF_TOKEN = os.getenv("HF_TOKEN", None)
-cache_dir   = pathlib.Path("hf_assets")
+cache_dir = pathlib.Path("hf_assets")
 extract_dir = cache_dir / "predictor_native"
 
-def prepare_predictor_dir() -> str:
+def prepare_predictor_dir():
     cache_dir.mkdir(parents=True, exist_ok=True)
     local_zip = h.hf_hub_download(
         repo_id=model_repo_id,
@@ -40,7 +38,7 @@ def prepare_predictor_dir() -> str:
 predictor_dir = prepare_predictor_dir()
 predictor = autogluon.multimodal.MultiModalPredictor.load(predictor_dir)
 
-def do_predict(pil_img: PIL.Image.Image, preprocess: bool = True):
+def do_predict(pil_img, preprocess=True):
     if pil_img is None:
         return "No image provided.", None, None
 
@@ -58,11 +56,8 @@ def do_predict(pil_img: PIL.Image.Image, preprocess: bool = True):
         img_path = tmpdir / "input.png"
         pil_img.save(img_path)
 
-
     df = pd.DataFrame({"image": [str(img_path)]})
-
     proba_df = predictor.predict_proba(df)
-
     proba_df = proba_df.rename(columns={0: "class_0", 1: "class_1"})
     row = proba_df.iloc[0]
 
@@ -73,15 +68,8 @@ def do_predict(pil_img: PIL.Image.Image, preprocess: bool = True):
 
     return pretty_dict, original_img, preprocessed_img
 
-
-EXAMPLES = [
-    ["https://universalsigns.com/wp-content/uploads/2022/08/StopSign-3.jpg"],
-    ["https://images.roadtrafficsigns.com/img/pla/K/student-drop-off-area-sign-k-2459_pl.png"],
-    ["https://hansonsign.com/wp-content/uploads/2024/05/donatos-662x646.jpg"]
-]
-
+# Remove external URLs - use local examples or none
 with gr.Blocks() as demo:
-
     gr.Markdown("# Is this a STOP sign or not?")
     gr.Markdown("Upload a photo to see results.")
 
@@ -101,15 +89,7 @@ with gr.Blocks() as demo:
         outputs=[proba_pretty, original_img_out, preprocessed_img_out]
     )
 
-    gr.Examples(
-        examples=EXAMPLES,
-        inputs=[image_in],
-        label="Choose any one",
-        examples_per_page=8,
-        cache_examples=False,
-    )
-
+    # No examples with external URLs to avoid connection errors
 
 if __name__ == "__main__":
     demo.launch()
-