Update app.py

app.py

@@ -191,14 +191,13 @@ train_images = get_sample_images(TRAIN_FOLDER)
 test_images = get_sample_images(TEST_FOLDER)
 
 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.
+    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. 
+    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).
     '''
 
 # Create the train dataset interface