Update slider output in app.py

app.py

@@ -18,10 +18,12 @@ BUNDLE_PATH = os.path.join(torch.hub.get_dir(), 'bundle', BUNDLE_NAME)
 
 title = "Segment Brain Tumors with MONAI!"
 description = """
+A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data.
+
 
 """
 
-examples = 'examples/'
+#examples = 'examples/'
 
 model, _, _ = bundle.load(
     name = BUNDLE_NAME,
@@ -65,16 +67,16 @@ 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_t1c_image = input_image[1, :, :, z_axis]
+    input_t1c_image = input_image[0, :, :, z_axis]
+    #input_t1_image = input_image[1, :, :, 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_tc_image = pred_image[0, 0, :, :, z_axis]
+    #pred_et_image = pred_image[0, 1, :, :, z_axis]
+    #pred_wt_image = pred_image[0, 2, :, :, z_axis]
     
-    return input_t1c_image, pred_1_image, z_axis
+    return input_t1c_image, pred_tc_image, z_axis
 
 
 iface = gr.Interface(
@@ -89,7 +91,7 @@ iface = gr.Interface(
         gr.Slider(0, 200, label='z-axis', value=100)],
     title=title,
     description=description,
-    examples=examples,
+    #examples=examples,
 )
 
 iface.launch()