Update app.py

app.py

@@ -1,59 +1,31 @@
 import gradio as gr
-
 from modelscope.pipelines import pipeline
 from modelscope.outputs import OutputKeys
 
 pipe = pipeline(task='image-to-video', model='damo/Image-to-Video', model_revision='v1.1.0')
 
 def infer (image_in):
-    
     # IMG_PATH: your image path (url or local file)
     IMG_PATH = image_in
     output_video_path = pipe(IMG_PATH, output_video='output.mp4')[OutputKeys.OUTPUT_VIDEO]
     print(output_video_path)
-    
     return output_video_path
 
-
 with gr.Blocks() as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("""
-        <p>
-            You are currently viewing a micro-service API meant to be used by robots.<br/>
-            For the human UI, please check out the <a href="https://huggingface.co/spaces/fffiloni/MS-Image2Video">original Space by Sylvain Filoni</a>.
-        </p>
-        
-        """)
-
-        image_in = gr.Image(
-            label = "Source Image",
-            source = "upload", 
-            type = "filepath",
-            elem_id = "image-in"
-        )
-        with gr.Row():
-
-            submit_btn = gr.Button(
-                "Submit"
-            )
-
-        video_out = gr.Video(
-            label = "Video Result",
-            elem_id = "video-out"
-        )
-
-        with gr.Row():
-
-
-
-    submit_btn.click(
-        fn = infer,
-        inputs = [
-            image_in
-        ],
-        outputs = [
-            video_out,
-        ]
+    gr.Markdown("""
+    <p>
+        You are currently viewing a micro-service API meant to be used by robots.<br/>
+        For the human UI, please check out the <a href="https://huggingface.co/spaces/fffiloni/MS-Image2Video">original Space by Sylvain Filoni</a>.
+     </p>
+    """)
+    image_in = gr.Image(
+        label = "Source Image",
+        source = "upload", 
+        type = "filepath",
+        elem_id = "image-in"
     )
+    submit_btn = gr.Button("Submit")
+    video_out = gr.Video(label = "Video Result", elem_id = "video-out")
+    submit_btn.click(fn = infer, inputs = [image_in], outputs = [video_out])
 
 demo.queue(max_size=6).launch()