Update app.py

app.py

@@ -39,17 +39,17 @@ import matplotlib.pyplot as plt
 from skimage import exposure 
 
 cdl_color_map = [{'value': 1, 'label': 'Natural vegetation', 'rgb': (233,255,190)},
-                 {'value': 2, 'label': 'Forest', 'rgb': (149,206,147)},
-                 {'value': 3, 'label': 'Corn', 'rgb': (255,212,0)},
+                 {'value': 2, 'label': 'Fruits(oranges,chicu)', 'rgb': (149,206,147)},
+                 {'value': 3, 'label': 'lenties Dal', 'rgb': (255,212,0)},
                  {'value': 4, 'label': 'Soybeans', 'rgb': (38,115,0)},
                  {'value': 5, 'label': 'Wetlands', 'rgb': (128,179,179)},
                  {'value': 6, 'label': 'Developed/Barren', 'rgb': (156,156,156)},
                  {'value': 7, 'label': 'Open Water', 'rgb': (77,112,163)},
-                 {'value': 8, 'label': 'Winter Wheat', 'rgb': (168,112,0)},
-                 {'value': 9, 'label': 'Alfalfa', 'rgb': (255,168,227)},
+                 {'value': 8, 'label': 'Wheat', 'rgb': (168,112,0)},
+                 {'value': 9, 'label': 'Rice', 'rgb': (255,168,227)},
                  {'value': 10, 'label': 'Fallow/Idle cropland', 'rgb': (191,191,122)},
                  {'value': 11, 'label': 'Cotton', 'rgb':(255,38,38)},
-                 {'value': 12, 'label': 'Sorghum', 'rgb':(255,158,15)},
+                 {'value': 12, 'label': 'Gram Dal', 'rgb':(255,158,15)},
                  {'value': 13, 'label': 'Other', 'rgb':(0,175,77)}]
 
 
@@ -234,8 +234,8 @@ func = partial(inference_on_file, model=model, custom_test_pipeline=custom_test_
 
 with gr.Blocks() as demo:
    
-    gr.Markdown(value='# Prithvi multi temporal crop classification')
-    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. This demo showcases how the model was finetuned to classify crop and other land use categories using multi temporal data. More detailes can be found [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification).\n
+    gr.Markdown(value='# multi temporal crop classification')
+    gr.Markdown(value='''this is a first-of-its-kind temporal Vision transformer Harmonised Landsat Sentinel 2 (HLS) data. This demo showcases how the model was finetuned to classify crop and other land use categories using multi temporal data. More detailes can be found [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification).\n
     The user needs to provide an HLS geotiff image, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order.
     ''')
     with gr.Row():