Update app.py

app.py

@@ -195,9 +195,8 @@ description= '''
     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. 
     Our project, using the DeepGlobe Land Cover Classification Challenge 2018 dataset, trained four models 
-    (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).
+    (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).
     '''
 
 # Create the train dataset interface