Update app.py

app.py

@@ -116,22 +116,44 @@ sample_images = get_sample_images('example_images')
 gr.Interface(
     predict,
     title='Land Cover Segmentation',
-    description=
+    inputs=[gr.Image()],
+    outputs=[gr.Image()],
+    examples=sample_images
+).launch(debug=True, share=True)
+
+tab1 = gr.Interface(
+    fn=predict,
+    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"
+)
+
+# Create the video processing interface
+tab2 = gr.Interface(
+    fn=process_video,
+    inputs=gr.File(label=""),
+    outputs=gr.File(label=""),
+    title='Images with Ground Truth',
+    examples=sample_images,
+    tab="Test"
+)
+
+# Create a Multi Interface with Tabs
+iface = gr.TabbedInterface([tab1, tab2], 
+                           title='Land Cover Segmentation',
+                          description=
     '''
     The DeepGlobe Land Cover Classification Challenge offers the first public dataset containing high resolution 
     satellite imagery focusing on rural areas. As there are multiple land cover types and high density of annotations,
     this dataset is more challenging than its counterparts launched in 2018. All satellite images contain RGB pixels, 
     with a pixel resolution of 50 cm. The total size of the total area of the dataset is equivalent to 10716.9 square kilometers.
     
-    This deep learning project was conducted while I collaborated with the Omdena Team on Seagrass detection challenge in 2024, 
-    I trained on 803 images and their segmentation masks (with split of 80/20%). For this multilabel segmentation task, we trained 4 models,
+    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.
-    ''',
-    inputs=[gr.Image()],
-    outputs=[gr.Image()],
-    examples=sample_images
-).launch(debug=True, share=True)
+    ''')
 
 # Launch the interface
 iface.launch(share=True)