class ChatBotInit:
def __init__(self):
#TODO move to outside
self.model_name = CHATBOT_MODEL
self.embedding_file = 'embedding_wiki_100d_pt.txt'
self.ds = Dataset()
self.messages = self.ds.import_dataset()
self.questions = self.ds.get_questions(
self.messages) #['why?', 'how?', 'when?, ...']
self.answers = self.ds.get_answers(
self.messages) #['yes', 'no', 'maybe', ...]
self.model = load_model(CHATDATA_DIR + self.model_name)
self.pc_questions = self.ds.get_page_compute(qea=1)
self.pc_answers = self.ds.get_page_compute(qea=0)
self.tokenizer = self.ds.load_tokenizer()
self.p = Prediction(model=self.model,
messages=self.messages,
questions=self.questions,
answers=self.answers,
pc_questions=self.pc_questions,
pc_answers=self.pc_answers,
tokenizer=self.tokenizer)
def init_chat_cmd(self, run_once=False):
m = None
exit = 'exit'
print('I am ready to talk.')
print(' Tip: You can type \"exit\" (lowercase) anytime to leave.')
while (m != exit):
#TODO save conversation to file_timestamp
try:
m = input(YOU_PREFIX)
save_content_to_log_file(YOU_PREFIX + str(m))
except KeyboardInterrupt:
print(BOT_PREFIX + emergency_message())
save_content_to_log(sys.exc_info())
if m != exit:
r = str(self.p.predict(m))
print(BOT_PREFIX + r)
save_content_to_log_file(BOT_PREFIX + str(r))
if run_once == True:
m = exit
print(BOT_PREFIX + 'Bye!')
def get_conversation(self, message):
return str(self.p.predict(message))
mode = sys.argv[1]
if mode == "train" or mode == "onlyexport":
data_file = sys.argv[2]
parameter_file = sys.argv[3]
examples_file = sys.argv[4]
with codecs.open(parameter_file, 'r', 'utf-8') as f:
params = json.load(f)
with codecs.open(examples_file, 'r', 'utf-8') as f:
examples = json.load(f)
t = Training(data_file=data_file, examples=examples)
for p in params:
print("Trying:{}".format(p))
p["only_export"] = (mode == "onlyexport")
t.train(**p)
if mode == "predict":
vocab_file = sys.argv[2]
model_file = sys.argv[3]
text = sys.argv[4]
p = Prediction(model_file, vocab_file)
print(p.predict(text))
if mode == "predict_server":
data_file = sys.argv[2]
model_file = sys.argv[3]
p = Prediction(model_file, data_file)
db = get_database_from_file(data_file)
PredictServer(p, db)
