Update app.py

app.py

@@ -37,6 +37,7 @@ model1 = torch.load("model1.pt", map_location=torch.device('cpu'))
 model2 = torch.load("model2.pt", map_location=torch.device('cpu'))
 model3 = torch.load("model3.pt", map_location=torch.device('cpu'))
 model4 = torch.load("model4.pt", map_location=torch.device('cpu'))
+model5 = torch.load("model5.pt", map_location=torch.device('cpu'))
 
 def fn(glycan, model):
     # Draw graph
@@ -61,6 +62,9 @@ def fn(glycan, model):
     elif model == "Bootstrap Ensemble":
       model_pred = model4
       model_pred.eval()
+    elif model == "Random edge deletion":
+      mode_pred = model5
+      model_pred.eval()
     else:
       model_pred = model2
       model_pred.eval()
@@ -89,7 +93,7 @@ def fn(glycan, model):
 
 demo = gr.Interface(
     fn=fn,
-    inputs=[gr.Textbox(label="Glycan sequence", value="Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"), gr.Radio(label="Model",choices=["No data augmentation", "Random node deletion", "Ensemble", "Bootstrap Ensemble"])],
+    inputs=[gr.Textbox(label="Glycan sequence", value="Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"), gr.Radio(label="Model",choices=["No data augmentation", "Random node deletion", "Random edge deletion", "Ensemble", "Bootstrap Ensemble"])],
     outputs=[gr.Label(num_top_classes=15, label="Prediction"), gr.Image(label="Glycan graph")],
     allow_flagging="never",
     title="SweetNet demo",