Update app.py

app.py

@@ -43,10 +43,10 @@ model = load_model()
 labels = load_labels()
 
 #image: PIL.Image.Image
-def predict(img: bytes, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]:
+def predict(image: PIL.Image.Image, score_threshold: float) -> tuple[dict[str, float], dict[str, float], str]:
     _, height, width, _ = model.input_shape
     #response = requests.get(url)
-    image = PIL.Image.open(img)
+    #image = PIL.Image.open(img)
 
     image = np.asarray(image)
     image = tf.image.resize(image, size=(height, width), method=tf.image.ResizeMethod.AREA, preserve_aspect_ratio=True)
@@ -77,9 +77,9 @@ with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         with gr.Column():
-            #image = gr.Image(label="Input", type="filepath")
+            image = gr.Image(label="Input", type="pil")
             #url = gr.Textbox("https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png")
-            img = gr.File(type="binary")
+            #img = gr.File(type="binary")
             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:
     
     run_button.click(
         fn=predict,
-        inputs=[img, score_threshold],
+        inputs=[image, score_threshold],
         outputs=[result, result_json, result_text],
         api_name="predict",
     )