def inline(c):
chat_id = c.message.chat.id
message_id = c.message.id
if c.data == 'quit':
bot.delete_message(chat_id, message_id)
if c.data == 'see':
main() #parsing
'''Cоздание кнопок'''
news = []
inline_keyboard_news = types.InlineKeyboardMarkup()
for new in main_data:
new = new['news']
news.append(new)
for i, n in enumerate(news, 1):
botton = types.InlineKeyboardButton(f'{i} : {n}',
callback_data=f'{i}')
btn_photo = types.InlineKeyboardButton(
'See photo', callback_data=f'see_photo{i}')
inline_keyboard_news.add(botton)
inline_keyboard_news.add(btn_photo)
'''Отправка новостей'''
msg = bot.send_message(chat_id,
'news:',
reply_markup=inline_keyboard_news)
'''Отправить ссылку на новость'''
enumerate_ = [str(i) for i in range(1, 17)]
if c.data in enumerate_:
try:
msg = main_data[int(c.data) - 1]['link']
bot.send_message(chat_id, f'{msg}')
except IndexError:
pass
'''Отправить фотографию'''
for i in [str(i) for i in range(1, 17)]:
if c.data == f'see_photo{i}':
try:
msg = main_data[int(i) - 1]['photo']
if msg:
bot.send_photo(chat_id, f'{msg}')
else:
bot.send_message(chat_id, 'Sorry, can\'t send you photo')
except IndexError:
pass
def do_it(link):
global previous_sum
previous_sum = -1
while True:
try:
trains = parsing.main(link) #get all information about all trains
check(trains); #choose good seats
except parsing.timeout_error:
print("timeout_error")
except parsing.number_of_trains_convert_error:
print("maybe service does not work")
except ValueError:
print("Value Error")
except:
print("unexpected error")
time.sleep(300) #wait
from glob import glob
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import glob
import tensorflow as tf
import numpy as np
from PIL import Image
from utils import *
from past.builtins import xrange
import glob
from detect_edges_image import CropLayer
import argparse
from parsing import main
import os
main()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
# evaluate prosessing
parsing_dir = 'dataset/parse_cihp'
if os.path.exists(parsing_dir):
shutil.rmtree(parsing_dir)
if not os.path.exists(parsing_dir):
os.makedirs(parsing_dir)
# Iterate over training steps.
for step in range(NUM_STEPS):
parsing_, scores, edge_, _ = sess.run([pred_all, pred_scores, pred_edge, update_op])
if step % 100 == 0:
print('step {:d}'.format(step))
print (image_list[step])
img_split = image_list[step].split('/')
import parsing
from openpyxl import load_workbook
parsing.main()
wb = load_workbook('deputy.xlsx')
ws = wb.active
def get_info(name):
info = []
for row in ws.rows:
if name in row[0].value:
for cell in row:
info.append(cell.value)
a = list(cell.value)
return f'ФИО: {info[0]} \nномер телефона: {info[1]} \nсостоит в {info[2]} \n'
return 'Нет информации. Проверьте правильно ли написали фамилию и затем нажмите на /start'
class Deputy(Base):
__tablename__ = 'deputy'
id = Column(Integer, primary_key=True)
fullname = Column(String)
fraction = Column(String)
commitet = Column(String)
tel = Column(String)
def __init__(self, fullname, fraction, commitet, tel):
self.fullname = fullname
self.fraction = fraction
self.commitet = commitet
self.tel = tel
Base.metadata.create_all(engine)
print('Table created')
from parsing import main
Session = sessionmaker(bind=engine)
session = Session()
data = main()
for one_data in data:
session.add(Deputy(*one_data))
print('successfully added')
session.commit()
def CronParse():
main()