fix bug

app.py

@@ -395,15 +395,10 @@ def preprocess_example(example_list):
 with gr.Blocks() as demo:
     
     gr.Markdown(value='# Prithvi image reconstruction demo')
-    gr.Markdown(value='''Prithvi is a first-of-its-kind temporal Vision transformer pretrained by the IBM and NASA team on continental US Harmonised 
-    Landsat Sentinel 2 (HLS) data. Particularly, the model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder 
-    learning strategy, with a MSE as a loss function. The model includes spatial attention across multiple patchies and also temporal attention for 
-    each patch. More info about the model and its weights are available [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M).\n
-    This demo showcases the image reconstracting over three timestamps, with the user providing a set of three HLS images and the model randomly masking 
-    out some proportion of the images and then reconstructing them based on the not masked portion of the images.\n
-    The user needs to provide three HLS geotiff images, including the following channels in reflectance units: Blue, Green, Red, NIRa, SWIR, SWIR 2.
-
-    ''')
+    gr.Markdown(value='''Prithvi is a first-of-its-kind temporal Vision transformer pretrained by the IBM and NASA team on continental US Harmonised Landsat Sentinel 2 (HLS) data. Particularly, the model adopts a self-supervised encoder developed with a ViT architecture and Masked AutoEncoder learning strategy, with a MSE as a loss function. The model includes spatial attention across multiple patchies and also temporal attention for each patch. More info about the model and its weights are available [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M).\n
+This demo showcases the image reconstracting over three timestamps, with the user providing a set of three HLS images and the model randomly masking out some proportion of the images and then reconstructing them based on the not masked portion of the images.\n
+The user needs to provide three HLS geotiff images, including the following channels in reflectance units: Blue, Green, Red, NIRa, SWIR, SWIR 2.
+''')
     with gr.Row():
         with gr.Column():
             inp_files = gr.Files(elem_id='files')