this should not be hard

app.py

@@ -47,6 +47,8 @@ def respond(input):
 
         out = model.out(model.decoder(outputs[:i].unsqueeze(0), e_outputs, src_mask, trg_mask))
         out = torch.nn.functional.softmax(out, dim=-1).detach()
+        print(out[:, -1].data[0])
+        print(out[:, -1].data[0].shape)
         print(np.sum(out[:, -1].data[0]))
         ix = np.random.choice(np.arange(len(out[:, -1].data[0])), 1, p=out[:, -1].data[0])
         # val, ix = out[:, -1].data.topk(1)