Update app.py

app.py

@@ -1,6 +1,5 @@
 import os
 import random
-import base64
 import gradio as gr
 import numpy as np
 from PIL import Image
@@ -96,7 +95,7 @@ def vote(filename, vote_type):
         logger.info(f"Updated {vote_type} count for {filename}")
     else:
         logger.warning(f"File {filename} not found in metadata")
-    return image_metadata.values.tolist()
+    return get_image_gallery(), image_metadata.values.tolist()
 
 if torch.cuda.is_available():
     pipe = StableDiffusionXLPipeline.from_pretrained(
@@ -180,13 +179,19 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
     )
 
     delete_all_button.click(fn=delete_all_images, outputs=[image_gallery, metadata_df])
-    image_gallery.select(fn=lambda evt: evt, outputs=selected_image)
+    
+    image_gallery.select(fn=lambda evt, x=None: evt, inputs=None, outputs=selected_image)
+    
     delete_image_button.click(fn=delete_image, inputs=[selected_image], outputs=[image_gallery, metadata_df])
 
     for button, vote_type in [(like_button, 'Likes'), (dislike_button, 'Dislikes'), (heart_button, 'Hearts')]:
-        button.click(fn=lambda x, vt=vote_type: vote(x, vt) if x else image_metadata.values.tolist(), inputs=[selected_image], outputs=[metadata_df])
+        button.click(
+            fn=lambda x, vt=vote_type: vote(x, vt) if x else (get_image_gallery(), image_metadata.values.tolist()),
+            inputs=[selected_image],
+            outputs=[image_gallery, metadata_df]
+        )
 
     demo.load(fn=lambda: (get_image_gallery(), image_metadata.values.tolist()), outputs=[image_gallery, metadata_df])
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch(share=True, debug=False)
+    demo.queue(max_size=20).launch(debug=False)