Update app.py

app.py

@@ -75,8 +75,17 @@ def process_image(image):
     pooled_array = encoded_array.max(axis=-1)
     return pooled_array  # Shape: (1, n_features)
     
-def inference(image):
-    input_image = process_image(image)
+def inference(user_image=None, selected_example=None)):
+
+    if user_image is not None:
+        input_image = process_image(user_image)
+    elif selected_example is not None:
+        example_image = load_example(selected_example)
+        input_image = process_image(example_image)
+    else:
+        return "Please upload an image or select an example image."
+
+    # input_image = process_image(image)
 
     nearest_neighbors = find_nearest_neighbors(encoded_images, input_image, top_n=5)
     
@@ -117,10 +126,9 @@ def inference(image):
     return result
 
 def load_example(example_name):
-    if example_name in example_images:
-        image_path = example_images[example_name]
-        image = Image.open(image_path)
-        return image
+    image_path = example_images.get(example_name)
+    if image_path:
+        return Image.open(image_path)
     return None
     
 with gr.Blocks() as demo:
@@ -140,21 +148,14 @@ with gr.Blocks() as demo:
                 label="Click an Example Image",
             )
 
-    with gr.Row():
+    with gr.Column():
         out = gr.Markdown()
 
-    def handle_selection(user_image, selected_example):
-        if user_image is not None:
-            return inference(user_image)
-        elif selected_example is not None:
-            image = load_example(selected_example)
-            return inference(image)
-        else:
-            return "Please upload an image or select an example image."
-
-    inputs = [inp_image, example_selection]
-    outputs = out
-    example_selection.select(handle_selection, inputs, outputs)
+    def handle_inputs(user_image, selected_example):
+        return inference(user_image=user_image, selected_example=selected_example)
+
+    inp_image.change(handle_inputs, inputs=[inp_image, example_selection], outputs=output)
+    example_selection.change(handle_inputs, inputs=[inp_image, example_selection], outputs=output)
 
 if __name__ == "__main__":
     demo.launch()