Update app.py

app.py

@@ -126,8 +126,7 @@ tab1 = gr.Interface(
     inputs=gr.Image(label='', type="pil"),
     outputs=[gr.Image(type="pil"), gr.Image(type="pil")],
     title='Images with Ground Truth',
-    examples=sample_images,
-    tab="Train"
+    examples=sample_images
 )
 
 # Create the video processing interface
@@ -136,8 +135,7 @@ tab2 = gr.Interface(
     inputs=gr.File(label=""),
     outputs=gr.File(label=""),
     title='Images with Ground Truth',
-    examples=sample_images,
-    tab="Test"
+    examples=sample_images
 )
 
 # Create a Multi Interface with Tabs
@@ -153,7 +151,8 @@ iface = gr.TabbedInterface([tab1, tab2],
     We trained on 803 images and their segmentation masks (with split of 80/20%). For this multilabel segmentation task, we trained 4 models,
     the basic 4-blocks U-net CNN, VGG16 U-Net, Resnet50 U-net and Efficient Net U-net. Then, I built an ensemble model that achieved a 
     validation accuracy of about 75% and dice score of about 0.6.
-    ''')
+    ''',
+    tab_names  = ['Train','Test'])
 
 # Launch the interface
 iface.launch(share=True)