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i = np.random.choice(len(input_sentences))
input_seq = encoder_input_sequences[i:i+1]
translation = translate_sentence(input_seq)
print('Input Language : ', input_sentences[i])
print('Actual translation : ', output_sentences[i])
print('French translation : ', translation)

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def translate_sentence(input_seq):
    states_value = encoder_model.predict(input_seq)
    target_seq = np.zeros((1, 1))
    target_seq[0, 0] = word2idx_outputs['<sos>']
    eos = word2idx_outputs['<eos>']
    output_sentence = []

    for _ in range(max_out_len):
        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)
        idx = np.argmax(output_tokens[0, 0, :])

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idx2word_input = {v:k for k, v in word2idx_inputs.items()}
idx2word_target = {v:k for k, v in word2idx_outputs.items()}

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decoder_state_input_h = Input(shape=(LSTM_NODES,))
decoder_state_input_c = Input(shape=(LSTM_NODES,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]

decoder_inputs_single = Input(shape=(1,))
decoder_inputs_single_x = decoder_embedding(decoder_inputs_single)

decoder_outputs, h, c = decoder_lstm(decoder_inputs_single_x, initial_state=decoder_states_inputs)

decoder_states = [h, c]

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encoder_model = Model(encoder_inputs, encoder_states)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.load_weights('seq2seq_eng-fra.h5')

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es = EarlyStopping(monitor='val_loss', mode='min', verbose=1)

history = model.fit([encoder_input_sequences, decoder_input_sequences], decoder_targets_one_hot,
    batch_size=BATCH_SIZE,
    epochs=20,
    callbacks=[es],
    validation_split=0.1,
)

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#Compile
model = Model([encoder_inputs,decoder_inputs], decoder_outputs)
model.compile(
    optimizer='rmsprop',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)
model.summary()

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decoder_inputs = Input(shape=(max_out_len,))
decoder_embedding = Embedding(num_words_output, LSTM_NODES)
decoder_inputs_x = decoder_embedding(decoder_inputs)

decoder_lstm = LSTM(LSTM_NODES, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs_x, initial_state=encoder_states)

#Finally, the output from the decoder LSTM is passed through a dense layer to predict decoder outputs.
decoder_dense = Dense(num_words_output, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

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encoder_inputs = Input(shape=(max_input_len,))
x = embedding_layer(encoder_inputs)
encoder = LSTM(LSTM_NODES, return_state=True)

encoder_outputs, h, c = encoder(x)
encoder_states = [h, c]

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embedding_layer = Embedding(num_words, EMBEDDING_SIZE, weights=[embedding_matrix], input_length=max_input_len)