Update app.py

app.py

@@ -43,13 +43,13 @@ model7 = torch.load("model7.pt", map_location=torch.device('cpu'))
 
 def fn(glycan, model):
     # Draw graph
-    graph = glycan_to_nxGraph(glycan)
-    node_labels = nx.get_node_attributes(graph, 'string_labels')
-    labels = {i:node_labels[i] for i in range(len(graph.nodes))}
-    graph = nx.relabel_nodes(graph, labels)
-    graph = nx.drawing.nx_pydot.to_pydot(graph)
-    graph.set_prog("dot")
-    graph.write_png("graph.png")
+    #graph = glycan_to_nxGraph(glycan)
+    #node_labels = nx.get_node_attributes(graph, 'string_labels')
+    #labels = {i:node_labels[i] for i in range(len(graph.nodes))}
+    #graph = nx.relabel_nodes(graph, labels)
+    #graph = nx.drawing.nx_pydot.to_pydot(graph)
+    #graph.set_prog("dot")
+    #graph.write_png("graph.png")
     # write_dot(graph, "graph.dot")
     # graph=pgv.AGraph("graph.dot")  
     # graph.layout(prog='dot')
@@ -96,13 +96,13 @@ def fn(glycan, model):
     pred = np.exp(pred)/sum(np.exp(pred)) # Softmax 
     pred = [float(x) for x in pred]
     pred = {class_list[i]:pred[i] for i in range(15)}
-    return pred, "graph.png"
+    return pred
 
 
 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", "Random edge deletion", "Ensemble", "Bootstrap ensemble", "Hierarchy substitution", "Adjusted class weights"])],
-    outputs=[gr.Label(num_top_classes=15, label="Prediction"), gr.Image(label="Glycan graph")],
+    outputs=[gr.Label(num_top_classes=15, label="Prediction")],
     allow_flagging="never",
     title="SweetNet demo",
     examples=[