Update app.py

app.py

@@ -43,10 +43,11 @@ def load_labels() -> list[str]:
 model = load_model()
 labels = load_labels()
 
-
-def predict(image: PIL.Image.Image, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]:
+#image: PIL.Image.Image
+def predict(url: str, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]:
     _, height, width, _ = model.input_shape
-    
+    response = requests.get(url)
+    image = PIL.Image.open(BytesIO(response.content))
     image = np.asarray(image)
     image = tf.image.resize(image, size=(height, width), method=tf.image.ResizeMethod.AREA, preserve_aspect_ratio=True)
     image = image.numpy()
@@ -79,8 +80,7 @@ with gr.Blocks(css="style.css") as demo:
         with gr.Column():
             #image = gr.Image(label="Input", type="pil")
             url = gr.Textbox("")
-            response = requests.get(url)
-            image = PIL.Image.open(BytesIO(response.content))
+            
             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():
@@ -93,7 +93,7 @@ with gr.Blocks(css="style.css") as demo:
                     result_text = gr.Text(label="Text output", show_label=False, lines=5)
     gr.Examples(
         examples=examples,
-        inputs=[image, score_threshold],
+        inputs=[url, score_threshold],
         outputs=[result, result_json, result_text],
         fn=predict,
         cache_examples=os.getenv("CACHE_EXAMPLES") == "1",