from flask import Flask, render_template, request
from flask_cors import CORS
from flask_socketio import SocketIO, emit
from logger import getlogger
from settings import Settings
import nltk
from flickrapi import FlickrAPI
import sys
import timeit
import urllib.request
import json
logger = getlogger(__name__)
settings = Settings
app = Flask(__name__,
template_folder='html/templates',
static_folder='html/static')
CORS(app)
socketio = SocketIO(app)
app.debug = False
flickr = FlickrAPI('c6a2c45591d4973ff525042472446ca2',
'202ffe6f387ce29b',
format='parsed-json')
@app.route('/nltk', methods=['POST'])
def nltk_process():
import os, sys, pickle, gzip, json, shutil, base64
#import umsgpack
try:
from StringIO import StringIO as BytesIO
except ImportError:
from io import StringIO
from io import BytesIO
from PySide.QtCore import QByteArray, QIODevice, QBuffer
from ghost import Ghost
import logging
from logger import getlogger
logger = getlogger()
appdir = os.path.abspath(
os.path.join(os.path.dirname(__file__), "..")
)
def main(url, output, option={}):
result = {
"error":[],
"page":{},
"resources":[],
"capture":None,
}
#savedir = appdir + "/artifacts/ghost/" + output
#dump = savedir + "/ghost.pkl"
savedir = os.path.join(appdir, "artifacts/ghost")
if args.fsr_enabled:
print('Training Feature Super Resolution GAN model')
print('\tLow resolution scaling = {} x {}'.format(args.low_ratio, args.low_ratio))
# FSR-GAN model
generator = FSR_Generator()
discriminator = FSR_Discriminator()
optimizer_G = optim.Adam(generator.parameters(), lr=args.lr)
optimizer_D = optim.Adam(discriminator.parameters(), lr=args.lr)
generator.cuda()
discriminator.cuda()
# Logger
logger = getlogger(args.log_dir + '/FSR-GAN_{}_LOW_{}'
.format(args.dataset, str(args.low_ratio)))
for arg in vars(args):
logger.debug('{} - {}'.format(str(arg), str(getattr(args, arg))))
logger.debug(
'\nTraining FSR-GAN model, Low resolution of {}x{}'.format(str(args.low_ratio),
str(args.low_ratio)))
logger.debug('\t on ' + args.dataset.upper() + ' dataset, with hyper parameters above\n\n')
# training_FSR(net, generator, discriminator, optimizer_G, optimizer_D, args.focal_loss_r,
# args.classes, args.lr, args.lr_decay, args.epochs, args.ten_batch_eval,
# train_loader, eval_train_loader, eval_validation_loader, num_training, num_validation,
# args.low_ratio, args.result, logger, args.vgg_gap, args.save)
training_Disc(teacher_net, net, optimizer, discriminator, optimizer_D, args.w_clip,
args.lr, args.lr_decay, args.epochs, args.ten_batch_eval,
train_loader, eval_train_loader, eval_validation_loader, num_training, num_validation,
import logger
log=logger.getlogger()
log.info('jeevan')
import logger
import textfile
import time
import directmove
import xmlhelper
import os
log = logger.getlogger('mylogger')
rootdir = "/alfresco_content/OneSearch/Physical_Resources/LMS"
# rootdir = raw_input("Enter Path:")
# if len(rootdir) <= 0:
# rootdir = "/alfresco_content/OneSearch/Physical_Resources/LMS"
print ("1. Move by Textfile")
print ("2. Move directly")
print ("3. Generate sample metadata files")
choice = raw_input("enter choice:")
if choice == "1":
txtfile = raw_input("Enter text file:")
if len(txtfile) <= 0:
txtfile = "textfiles/alf_path.txt"
start = time.time()
counter = textfile.move_by_textfile(txtfile, rootdir)
end = time.time()
speed = counter / (end - start)
log.info(txtfile + " processed :" + str(counter) + " Speed: " + str(int(speed)) + "/second")
elif choice == "2":
start = time.time()
#encoding:utf-8
"""
Some celery tasks here
"""
import celery
import celeryconfig
from logger import getlogger
logger = getlogger(__name__)
app = celery.Celery()
app.config_from_object(celeryconfig)
@app.task
def add(x, y):
"""
return x + y
"""
z = x + y
logger.info('{} + {} = {}'.format(x, y, z))
return 'done', z
import os, sys, pickle, gzip, json, shutil, base64
#import umsgpack
try:
from StringIO import StringIO as BytesIO
except ImportError:
from io import StringIO
from io import BytesIO
from PySide.QtCore import QByteArray, QIODevice, QBuffer
from ghost import Ghost
import logging
from logger import getlogger
logger = getlogger()
appdir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
def main(url, output, option={}):
result = {
"error": [],
"page": {},
"resources": [],
"capture": None,
}
#savedir = appdir + "/artifacts/ghost/" + output
#dump = savedir + "/ghost.pkl"
savedir = os.path.join(appdir, "artifacts/ghost")
dump = savedir + "/" + output