def get_users_from_database(data):
try:
users = get_users(db, data['user'])
new_users = filter_dm(users, data['user'])
response = {'users': new_users}
emit('total users', response)
except Exception as e:
print(e)
def post(self):
user = self.get_argument('user', None)
password = self.get_argument('password', None)
# retrieve the user list from the database
users = data.get_users()
# if there's a match, set cookie then redirect
if user in users and users[user] == password:
self.set_secure_cookie("user", user)
print "User " + user + " logged in."
self.redirect('/' + user + '/Fenng/1')
# if there's no match, send 404
else:
error = dict()
error['message'] = "Wrong user name or password."
print "Invalid user " + user + " attempted to log in."
return self.send_error(404, **error)
def post(self):
user = self.get_argument('user', None)
password = self.get_argument('password', None)
# retrieve the user list from the database
users = data.get_users()
# if there's a match, set cookie then redirect
if user in users and users[user] == password:
self.set_secure_cookie("user", user)
print "User " + user + " logged in."
self.redirect('/' + user + '/Fenng/1')
# if there's no match, send 404
else:
error = dict()
error['message'] = "Wrong user name or password."
print "Invalid user " + user + " attempted to log in."
return self.send_error(404, **error)
def get_user_sessions(limit=None, filename="", directory="", version=Version.UNDIAG_2014):
for users in get_users(limit=limit, filename=filename, directory=directory):
users_with_sessions = extract_sessions(users, version=version)
yield users_with_sessions
def users(request):
return data.get_users()
from flask import Flask, render_template, jsonify
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pandas as pd
import numpy as np
from flask import request
# Это просто нужно, для того, чтобы можно было отдавать данные
from flask_cors import CORS, cross_origin
app = Flask(__name__)
CORS(app)
from data import get_users
users = get_users()
#from sklearn.cluster import KMeans
# Импортируем базу данных всех товаров с указанием Наименований, Категорий и пр.
bdd_rms = pd.read_excel('library/BDD.xlsx',
names=[
'product', 'department', 'model_adeo',
'model_name', 'date_created', 'product_name',
'is_stm'
])
################# Импортируем обученные модели ####################
# Стандартная модель, обученная на артикулах
model_products = Word2Vec.load('models/w2v_64m_sg0_i220_window9_size160')
