how:

app.py

@@ -46,7 +46,7 @@ def respond(input):
         trg_mask = torch.autograd.Variable(torch.from_numpy(trg_mask) == 0).to(device)
 
         out = model.out(model.decoder(outputs[:i].unsqueeze(0), e_outputs, src_mask, trg_mask))
-        out = torch.nn.functional.softmax(out, dim=-1)[:, -1].detach().numpy()
+        out = torch.nn.functional.softmax(out, dim=-1)[:, -1].squeeze().detach().numpy()
         print(out.shape)
         print(np.sum(out))
         ix = np.random.choice(np.arange(len(out)), 1, p=out)