this should not be so hard

app.py

@@ -47,11 +47,13 @@ def respond(custom_string):
 
         out = model.out(model.decoder(outputs[:i].unsqueeze(0), e_outputs, src_mask, trg_mask))
         out = torch.nn.functional.softmax(out, dim=-1).detach()
-        ix = np.random.choice(np.arange(out[:, -1]), 1, p=out[:, -1])
+        ix = np.random.choice(np.arange(out[:, -1].data), 1, p=out[:, -1].data)
         # val, ix = out[:, -1].data.topk(1)
 
-        outputs[i] = ix[0][0]
-        if ix[0][0] == vocab_token_dict['<eos>']:
+        # outputs[i] = ix[0][0]
+        outputs[i] = ix
+        # if ix[0][0] == vocab_token_dict['<eos>']:
+        if ix == vocab_token_dict['<eos>']:
             break
     return ' '.join([indices_to_tokens[ix] for ix in outputs[1:i]])