Update app.py

app.py

@@ -6,20 +6,17 @@ import segmentation_models as sm
 from matplotlib import pyplot as plt
 import random
 
-
 from keras import backend as K
-from keras.models import load_model 
-
+from keras.models import load_model
 
 import gradio as gr
 
-
 def jaccard_coef(y_true, y_pred):
   y_true_flatten = K.flatten(y_true)
   y_pred_flatten = K.flatten(y_pred)
   intersection = K.sum(y_true_flatten * y_pred_flatten)
   final_coef_value = (intersection + 1.0) / (K.sum(y_true_flatten) + K.sum(y_pred_flatten) - intersection + 1.0)
-  return final_coef_value
+  return final_coef_value    
 
 weights = [0.2,0.2,0.2,0.2,0.2]
 dice_loss = sm.losses.DiceLoss(class_weights = weights)
@@ -29,6 +26,7 @@ total_loss = dice_loss + (1 * focal_loss)
 satellite_model = load_model('model/C:/Users/sa/Desktop/Model_Training/satellite_segmentation_full.h5',
                          custom_objects=({'dice_loss_plus_1focal_loss': total_loss,|'jaccard_coef': jaccard_coef}))
 
+
 def process_input_image(image_source):
   image = np.expand_dims(image_source, 0)
 
@@ -39,10 +37,8 @@ def process_input_image(image_source):
   predicted_colored = predicted_colored * 50
   return 'Predicted Masked Image', predicted_colored
 
-
 my_app = gr.Blocks()
 
-
 with my_app:
   gr.Markdown("Statellite Image Segmentation Application UI with Gradio")
   with gr.Tabs():
@@ -55,15 +51,14 @@ with my_app:
             output_label = gr.Label(label="Image Info")
             img_output = gr.Image(label="Image Output")
     source_image_loader.click(
-        process_input_image,
-        [
-            img_source
-        ],
-        [
-            output_label,
-            img_output
-        ]
-    )
-
+       process_input_image,
+       [
+         img_source
+       ],
+       [
+         output_label,
+         img_output
+       ]
+     )
 
 my_app.launch(debug=True)