Update app.py

app.py

@@ -16,7 +16,7 @@ from monai.transforms import (
 BUNDLE_NAME = 'spleen_ct_segmentation_v0.1.0'
 BUNDLE_PATH = os.path.join(torch.hub.get_dir(), 'bundle', BUNDLE_NAME)
 
-examples = ['examples/BRATS_485.nii.gz']
+#examples = ['examples/BRATS_485.nii.gz']
 
 model, _, _ = bundle.load(
     name = BUNDLE_NAME,
@@ -60,14 +60,14 @@ def predict(input_file, z_axis, model=model, device=device):
     input_image = data['image'].numpy()
     pred_image = data['pred'].cpu().detach().numpy()
     
-    input_t1_image = input_image[0, :, :, z_axis]
+    #input_t1_image = input_image[0, :, :, z_axis]
     input_t1c_image = input_image[1, :, :, z_axis]
-    input_t2_image = input_image[2, :, :, z_axis]
-    input_flair_image = input_image[3, :, :, z_axis]
+    #input_t2_image = input_image[2, :, :, z_axis]
+    #input_flair_image = input_image[3, :, :, z_axis]
     
     pred_1_image = pred_image[0, 0, :, :, z_axis]
-    pred_2_image = pred_image[0, 1, :, :, z_axis]
-    pred_3_image = pred_image[0, 2, :, :, z_axis]
+    #pred_2_image = pred_image[0, 1, :, :, z_axis]
+    #pred_3_image = pred_image[0, 2, :, :, z_axis]
     
     return input_t1c_image, pred_1_image
 
@@ -81,7 +81,7 @@ iface = gr.Interface(
         gr.Image(label='input image'),
         gr.Image(label='segmentation')],
     title='Segment Gliomas using MONAI',
-    examples=examples,
+    #examples=examples,
 )
 
 iface.launch()