# Выполняем привязку обработчиков
bot.add_event_handler(u'weather_forecast', on_weather_forecast)
bot.add_event_handler(u'check_emails', on_check_emails)
bot.add_event_handler(u'alarm_clock', on_alarm_clock)
print_tech_banner()
bot.start_conversation(user_id)
while True:
print('\n')
# В самом начале диалога, когда еще не было ни одной реплики,
# бот может сгенерировать некое приветствие или вопрос для
# завязывания беседы. Поэтому сразу извлечем сгенерированные фразы из
# буфера и покажем их.
while True:
answer = bot.pop_phrase(user_id)
if len(answer) == 0:
break
print_answer(u'B:>', answer)
question = input_kbd('H:>').lower()
if len(question) > 0:
if question in ('\exit', '\q', '\quit'):
break
bot.push_phrase(user_id, question)
parser.add_argument('--token', type=str, default='', help='Telegram token for bot')
parser.add_argument('--data_folder', type=str, default='../data')
parser.add_argument('--w2v_folder', type=str, default='../data')
parser.add_argument('--facts_folder', type=str, default='../data', help='path to folder containing knowledgebase files')
parser.add_argument('--models_folder', type=str, default='../tmp', help='path to folder with pretrained models')
args = parser.parse_args()
facts_folder = os.path.expanduser(args.facts_folder)
models_folder = os.path.expanduser(args.models_folder)
data_folder = os.path.expanduser(args.data_folder)
w2v_folder = os.path.expanduser(args.w2v_folder)
telegram_token = args.token
if len(telegram_token) == 0:
telegram_token = input_kbd('Enter Telegram token:')
# -------------------------------------------------------
bot = telegram.Bot(token=telegram_token)
print(bot.getMe())
logging.info('Loading answering machine models...')
text_utils = TextUtils()
text_utils.load_dictionaries(data_folder)
facts_storage = Files3FactsStorage(text_utils=text_utils, facts_folder=facts_folder)
answering_machine = SimpleAnsweringMachine( facts_storage=facts_storage, text_utils=text_utils)
bot.load_models(models_folder, w2v_folder)
text_utils.load_dictionaries(data_folder)
bot = AnswerBuilder()
bot.load_models(models_folder)
word_embeddings = WordEmbeddings()
word_embeddings.load_wc2v_model(os.path.join(models_folder, 'wordchar2vector.dat'))
for p in bot.get_w2v_paths():
word_embeddings.load_w2v_model(os.path.join(w2v_folder, os.path.basename(p)))
max_nb_premises = 1
while True:
print('\n')
phrases = []
while True:
phrase = input_kbd(u'H:>')
if len(phrase) > 0:
phrases.append(phrase)
if phrase[-1] == u'?':
break
elif len(phrases) == max_nb_premises+1:
break
elif len(phrases) > 1:
break
if len(phrases) < 1:
print_error('At least 1 phrase expected!')
continue
premises = phrases[:-1]
question = phrases[-1]