Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from share_btn import community_icon_html, loading_icon_html, share_js
 import torch
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 from diffusers.utils import export_to_video
@@ -12,7 +13,7 @@ def infer(prompt):
     video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
     video_path = export_to_video(video_frames)
     print(video_path)
-    return video_path
+    return video_path, gr.Group.update(visible=True)
 
 css = """
 #col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
@@ -24,23 +25,27 @@ with gr.Blocks(css=css) as demo:
         gr.Markdown(
             """
             <h1 style="text-align: center;">Zeroscope Text-to-Video</h1>
-            
+            <p style="text-align: center;">
             A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. <br />
-            This zeroscope_v2_576w model was trained using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.<br />
             
             [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg)](https://huggingface.co/spaces/fffiloni/zeroscope?duplicate=true)
-            
+            </p>
             """
         )
 
-        prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves")
+        prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves", elem_id="prompt-in")
         #inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
         submit_btn = gr.Button("Submit")
-        video_result = gr.Video(label="Video Output")
+        video_result = gr.Video(label="Video Output", elem_id="video-output")
+
+        with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
+            community_icon = gr.HTML(community_icon_html)
+            loading_icon = gr.HTML(loading_icon_html)
+            share_button = gr.Button("Share to community", elem_id="share-btn")
 
     submit_btn.click(fn=infer,
                     inputs=[prompt_in],
-                    outputs=[video_result])
+                    outputs=[video_result, share_group])
 
 demo.queue(max_size=12).launch()