A note: looking at the code, this isn't a seq2seq Keras model. The core model code is a fork of the base Keras text generation example (https://github.com/fchollet/keras/blob/master/examples/lstm_...), which works like a char-rnn where the previous 80 characters will predict the 81st character, then feed the generated characters back into the model. In the server implementation, the server keeps predicting characters until it hits a break character, which will then serve the generated characters to the user.

In a seq2seq implementation, you need to predict all output characters simultaneously (i.e. model input is all characters in a line, model output is all characters in the next line), which in Keras involves using a TimeDistributed(Dense()) layer. (see the Keras example for seq2seq: https://github.com/fchollet/keras/blob/master/examples/addit...) This also requires more sequence ETL and a lot more training time.