from models.seq2seq_model import Seq2SeqModel
from models.seq2seq_model import Seq2SeqModelAttention
from configuration import get_configuration
from utils import Preprocessing
# Initialize the model
config = get_configuration()
preprocess = Preprocessing(config=config)
model = Seq2SeqModelAttention(config)
checkpoint_file = 'runs/baseline-cornell-twitter-attn-dropout/model-18000'
# Launch chat interface
print(
"*** Hi there. Ask me a question. I will try my best to reply to you with something intelligible.\
If you think that is not happening, enter \"q\" and quit ***")
query = input(">")
while query != "q":
# Tokenize the query
preprocess.initialize_vocabulary()
token_ids = preprocess.sentence_to_token_ids(query)
# Reverse the token ids and feed into the RNN
reverse_token_ids = [list(reversed(token_ids))]
output_tokens = model.infer(checkpoint_file,
reverse_token_ids,
verbose=False)
# Convert token ids back to words and print to output
output = preprocess.token_ids_to_sentence(output_tokens)
print(output[0])
query = input(">")
open((config.data_dir) + '/input_test_triples.pkl', 'rb'))
train_batches = generate_batches(train_data,
batch_size=config.batch_size,
num_epochs=config.n_epochs)
eval_batches = generate_batches(eval_data,
batch_size=config.batch_size,
num_epochs=1)
# model.train(train_batches, eval_batches, verbose=True)
# # Evaluate perplexity of trained model on validation data
# model_dir = 'runs/1496648182'
# model_file = '/model-12000'
# # print(model.evaluate(eval_batches, model_dir=model_dir, model_file=model_file))
#
# Infer outputs on a subset of test data
model_dir = 'runs/149664818'
model_file = '/model-12000'
checkpoint_file = model_dir + model_file
test_data = pickle.load(open((config.data_dir) + '/input_test.pkl',
'rb'))[0][:10]
predicted_outputs = model.infer(checkpoint_file, test_data)
preprocess.initialize_vocabulary()
# Reverse back the test sentences
test_data = [list(reversed(sentence)) for sentence in test_data]
messages = preprocess.token_ids_to_sentence(test_data)
responses = preprocess.token_ids_to_sentence(predicted_outputs)
# Print to file
with open(model_dir + "/model-12000-conversations", 'w') as f:
for idx, message in enumerate(messages):
f.write(message + " ====> " + responses[idx] + "\n")