upload app.py

app.py

@@ -56,8 +56,8 @@ model.eval()
 
 def segment(input):
     inp = input
-    x = inp.transpose([2, 0, 1])  #  channels-first
-    x = np.expand_dims(x, axis=0)  # adding a batch dimension    
+    x = inp.transpose([2, 0, 1]) 
+    x = np.expand_dims(x, axis=0)     
     
     mean = x.mean(axis=(0, 2, 3))
     std = x.std(axis=(0, 2, 3))
@@ -89,13 +89,12 @@ examples = [["TCGA_CS_5395_19981004_12.png"],
 
 title = "Sistem Segmentasi Citra MRI Otak berbasis Artificial Intelligence"
 description = "This system is designed to help automate the process of accurately and efficiently segmenting brain MRIs into regions of interest. It does this by using a UBNet-Seg Architecture that has been trained on a large dataset of manually annotated brain images."
-# videos. Accurate evaluation of the motion and size of the left ventricle is crucial for the assessment of cardiac function and ejection fraction. In this interface, the user inputs apical-4-chamber images from echocardiography videos and the model will output a prediction of the localization of the left ventricle in blue. This model was trained on the publicly released EchoNet-Dynamic dataset of 10k echocardiogram videos with 20k expert annotations of the left ventricle and published as part of ‘Video-based AI for beat-to-beat assessment of cardiac function’ by Ouyang et al. in Nature, 2020."
 
 #thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-echonet/master/thumbnail.png"
 
-#gr.Interface(segment, i, o, examples=examples, allow_flagging=False, analytics_enabled=False, thumbnail=thumbnail).launch()
 gr.Interface(segment, i, o, 
     allow_flagging = False, 
     description = description,
+    title=title,
     examples = examples,
     analytics_enabled = False).launch()