Update app.py

app.py

@@ -191,12 +191,15 @@ train_images = get_sample_images(TRAIN_FOLDER)
 test_images = get_sample_images(TEST_FOLDER)
 
 description= '''
-    Satellite imagery, powered by advancements in computer vision and GPU technology, plays a crucial 
-    role in urban planning and climate change research. Deep learning, particularly the U-Net model, 
-    automates land cover classification and facilitates monitoring of seagrass habitats in the Mediterranean Sea. 
+    Computer vision deep learning, powered by GPU advancements, plays a potentially 
+    key role in urban planning and climate change research. The U-Net model architecture, that is 
+    commonly used in semantic segmentation, can be applied to automated land cover classification and 
+    seagrass habitat monitoring. 
+    
     Our project, using the DeepGlobe Land Cover Classification Challenge 2018 dataset, trained four models 
-    (basic U-Net, VGG16 U-Net and Resnet50 U-Net) and achieved a validation accuracy of approximately 75% 
-    and a dice score of about 0.6 through an ensemble approach on 803 images with segmentation masks (80/20 split).
+    (basic U-Net, VGG16 U-Net, Resnet50 U-Net, and Efficient Net U-Net) and achieved a validation accuracy of 
+    approximately 75\% and a dice score of about 0.6 through an ensemble approach on 803 images with 
+    segmentation masks (80/20 split).
     '''
 
 # Create the train dataset interface