update full-screen

app.py

@@ -880,7 +880,7 @@ def make_dataset_images_section(advanced=False, is_random=False):
     
 def make_output_images_section():
     gr.Markdown('### Output Images')
-    output_gallery = gr.Gallery(value=[], label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto")
+    output_gallery = gr.Gallery(value=[], label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=False, show_share_button=True)
     return output_gallery
 
 def make_parameters_section(is_lisa=False):
@@ -1075,13 +1075,13 @@ with demo:
         with gr.Row():
             with gr.Column(scale=5, min_width=200):
                 gr.Markdown('### Output (Recursion #1)')
-                l1_gallery = gr.Gallery(value=[], label="Recursion #1", show_label=False, elem_id="ncut_l1", columns=[3], rows=[5], object_fit="contain", height="auto")
+                l1_gallery = gr.Gallery(value=[], label="Recursion #1", show_label=False, elem_id="ncut_l1", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True)
             with gr.Column(scale=5, min_width=200):
                 gr.Markdown('### Output (Recursion #2)')
-                l2_gallery = gr.Gallery(value=[], label="Recursion #2", show_label=False, elem_id="ncut_l2", columns=[3], rows=[5], object_fit="contain", height="auto")
+                l2_gallery = gr.Gallery(value=[], label="Recursion #2", show_label=False, elem_id="ncut_l2", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True)
             with gr.Column(scale=5, min_width=200):
                 gr.Markdown('### Output (Recursion #3)')
-                l3_gallery = gr.Gallery(value=[], label="Recursion #3", show_label=False, elem_id="ncut_l3", columns=[3], rows=[5], object_fit="contain", height="auto")
+                l3_gallery = gr.Gallery(value=[], label="Recursion #3", show_label=False, elem_id="ncut_l3", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True)
         with gr.Row():
             with gr.Column(scale=5, min_width=200):
                 input_gallery, submit_button, clear_images_button = make_input_images_section()
@@ -1200,15 +1200,15 @@ with demo:
         with gr.Row():
             with gr.Column(scale=5, min_width=200):
                 gr.Markdown('### Output (Prompt #1)')
-                l1_gallery = gr.Gallery(value=[], label="Prompt #1", show_label=False, elem_id="ncut_p1", columns=[3], rows=[5], object_fit="contain", height="auto")
+                l1_gallery = gr.Gallery(value=[], label="Prompt #1", show_label=False, elem_id="ncut_p1", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True)
                 prompt1 = gr.Textbox(label="Input Prompt #1", elem_id="prompt1", value="where is the person, include the clothes, don't include the guitar and chair", lines=3)
             with gr.Column(scale=5, min_width=200):
                 gr.Markdown('### Output (Prompt #2)')
-                l2_gallery = gr.Gallery(value=[], label="Prompt #2", show_label=False, elem_id="ncut_p2", columns=[3], rows=[5], object_fit="contain", height="auto")
+                l2_gallery = gr.Gallery(value=[], label="Prompt #2", show_label=False, elem_id="ncut_p2", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True)
                 prompt2 = gr.Textbox(label="Input Prompt #2", elem_id="prompt2", value="where is the Gibson Les Pual guitar", lines=3)
             with gr.Column(scale=5, min_width=200):
                 gr.Markdown('### Output (Prompt #3)')
-                l3_gallery = gr.Gallery(value=[], label="Prompt #3", show_label=False, elem_id="ncut_p3", columns=[3], rows=[5], object_fit="contain", height="auto")
+                l3_gallery = gr.Gallery(value=[], label="Prompt #3", show_label=False, elem_id="ncut_p3", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True)
                 prompt3 = gr.Textbox(label="Input Prompt #3", elem_id="prompt3", value="where is the floor", lines=3)
                 
         with gr.Row():
@@ -1288,7 +1288,7 @@ with demo:
         #         for i_layer in range(1, 13):
         #             with gr.Column(scale=5, min_width=200):
         #                 gr.Markdown(f'### {model_name} Layer {i_layer}')
-        #                 output_gallery = gr.Gallery(value=[], label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto")
+        #                 output_gallery = gr.Gallery(value=[], label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True)
         #                 galleries.append(output_gallery)
                 
 
@@ -1316,7 +1316,7 @@ with demo:
         def add_one_model(i_model=1):
             with gr.Column(scale=5, min_width=200) as col:
                 gr.Markdown(f'### Output Images')
-                output_gallery = gr.Gallery(value=[], label="NCUT Embedding", show_label=False, elem_id=f"ncut{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto")
+                output_gallery = gr.Gallery(value=[], label="NCUT Embedding", show_label=False, elem_id=f"ncut{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True)
                 submit_button = gr.Button("🔴 RUN", elem_id=f"submit_button{i_model}", variant='primary')
                 [
                     model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
@@ -1385,7 +1385,7 @@ with demo:
         buttons[-1].click(fn=lambda x: gr.update(visible=False), outputs=buttons[-1])
     
     
-    with gr.Tab('About'):
+    with gr.Tab('📄About'):
         gr.Markdown("##### This demo is for python package `ncut-pytorch`, please visit the [Documentation](https://ncut-pytorch.readthedocs.io/) ")
         gr.Markdown("---")
         gr.Markdown("**Normalized Cuts**, aka. spectral clustering, is a graphical method to analyze data grouping in the affinity eigenvector space. It has been widely used for unsupervised segmentation in the 2000s.")