Update app.py

app.py

@@ -8,16 +8,27 @@ import crVps
 def clear_all(image1, image2, output_image, output_json):
   return None, None, None, None
 
-with gr.Blocks(css="""
-    .row { display: flex; flex-direction: row; }
-    .column { flex: 1; }
-""") as demo:
+with gr.Blocks() as demo:
+    demo.style(
+        css="""
+        .gradio-container {
+            max-width: 1200px !important;
+            margin-left: auto !important;
+            margin-right: auto !important;
+        }
+        .contain {
+            display: flex !important;
+            flex-direction: row !important;
+            flex-wrap: nowrap !important;
+        }
+        """
+    )
     gr.Markdown("Choose or upload a dog image and press cpmpare!! the system will retutn the 2 detected faces with the recognition result")
-    with gr.Row():
-        with gr.Column():
+    with gr.Row(equal_height=True, scale=2):
+        with gr.Column(scale=1, min_width=200):
             image1_input = gr.Image(type="pil", label="Image 1",width=200,height=200)
             examples_image1 = gr.Examples(examples=["./dob1.jpg", "./p1.jpg", "./dob3.jpg", "./d1.jpg"], inputs=image1_input)
-        with gr.Column():
+        with gr.Column(scale=1, min_width=200):
             image2_input = gr.Image(type="pil", label="Image 2",width=200,height=200)
             examples_image2 = gr.Examples(examples=["./dob2.jpg", "./p2.jpg", "./dob4.jpg", "./d2.jpg"], inputs=image2_input)
     threshold_input = gr.Slider(minimum=0.0, maximum=1.0, value=0.55, step=0.05, label="Threshold")