Update app.py

app.py

@@ -147,6 +147,18 @@ def predict_on_test(image):
 
 sample_images = get_sample_images('example_images')
 
+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.
+    
+    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.
+    '''
+
 # Launch Gradio Interface (Single Tab interface)
 # gr.Interface(
 #     predict,
@@ -161,6 +173,7 @@ tab1 = gr.Interface(
     inputs=gr.Image(),
     outputs=[gr.Image(), gr.Image()],
     title='Images with Ground Truth',
+    description=description,
     examples=sample_images
 )
 
@@ -170,24 +183,14 @@ tab2 = gr.Interface(
     inputs=gr.Image(),
     outputs=gr.Image(),
     title='Images with Ground Truth',
+    description=description,
     examples=sample_images
 )
 
 # 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.
-    
-    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'])
+                           tab_names  = ['Train','Test'])
 
 # Launch the interface
 iface.launch(share=True)