Update app.py

app.py

@@ -43,12 +43,10 @@ def load_labels() -> list[str]:
 model = load_model()
 labels = load_labels()
 
-#PIL.Image.Image
-def predict(image: str, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]:
-    _, height, width, _ = model.input_shape
-    response = requests.get(image)
-    image = Image.open(BytesIO(response.content))
 
+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)
     image = tf.image.resize(image, size=(height, width), method=tf.image.ResizeMethod.AREA, preserve_aspect_ratio=True)
     image = image.numpy()
@@ -76,9 +74,13 @@ 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")
+            #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():
@@ -99,7 +101,7 @@ with gr.Blocks(css="style.css") as demo:
 
     run_button.click(
         fn=predict,
-        inputs=[image, score_threshold],
+        inputs=[url, score_threshold],
         outputs=[result, result_json, result_text],
         api_name="predict",
     )