from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.keys import Keys
from time import sleep
import numpy as np
from console_logging.console import Console
console = Console()
import json
import os
curated_lists = []
browser = webdriver.Chrome()
console.info("Initialized Chrome Webdriver.")
def get_repos(pages=10):
console.log("Now entering signup process.")
# Page 1 of Signup
browser.get('https://github.com/')
input('Log in, then press ENTER.')
browser.get(
'https://github.com/search?o=desc&p=1&q=curated+list&s=stars&type=Repositories&utf8=%E2%9C%93'
)
from console_logging.console import Console
console = Console()
console.log("Baixando Dataset...")
root = './'
filename_zip = ag.download(
'https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip',
path=root)
filename = ag.unzip(filename_zip, root=root)
console.log("Criando TASK TRAIN ")
import os
data_root = os.path.join(root, filename)
dataset_train = task.Dataset(data_root, classes=('motorbike', ))
console.info("TRAINING DATA MODEL...")
time_limits = 5 * 60 * 60 # 5 hours
epochs = 30
detector = task.fit(dataset_train,
num_trials=2,
epochs=epochs,
lr=ag.Categorical(5e-4, 1e-4),
ngpus_per_trial=1,
time_limits=time_limits)
console.success("TRAINING DONE !")
console.log("START TEST MODEL ")
dataset_test = task.Dataset(data_root,
index_file_name='test',
classes=('motorbike', ))
test_map = detector.evaluate(dataset_test)
import json
import os
from lxml import html
import requests
import unicodedata
from console_logging.console import Console
console = Console()
job_data = None
with open('jobs.json') as f:
job_data = json.load(f)
console.info("Crawling %d career pages." % len(job_data))
i = 0
for job_entry in job_data:
try:
url = job_entry['link']
page = requests.get(url)
tree = html.fromstring(page.content)
links = tree.xpath('//a')
job_postings = []
for link in links:
job_title = link.text_content().strip().lstrip()
if 'intern' in job_title: # only test if intern position
res = requests.post(
'http://127.0.0.1:8000/predict', json={'title': job_title})
prediction = res.text.strip().lstrip()
if prediction in ['IT/Software Development', 'Engineering']:
job_postings.append(job_title)
job_entry['positions'] = job_postings
except Exception as e:
try:
os.makedirs('./blog')
except:
pass
for file in os.listdir('./blogs'):
ext = file.split('.')[-1]
if ext == 'blog':
blog_id = file.split('.')[0]
with open('./blogs/%s' % file, 'r') as blog_def:
parsed_blog = parse_blog(blog_def.readlines())
blog_html = render_blog(parsed_blog)
with open('./blog/%s.html' % blog_id, 'w') as blog:
blog.write(blog_html)
blog_posts[-1]['url'] = '/blog/%s.html' % blog_id
console.info("Wrote blog id::%s" % blog_id)
blog_posts.sort(key=lambda blog_post: int(blog_post['date'][
'm']) * 32 * 3600 + int(blog_post['date']['d']) * 3600 + int(blog_post[
'date']['y']) * 367 * 3600 + int(blog_post['time']['h']) * 60 +
int(blog_post['time']['m'][:2]))
blog_posts.reverse()
index = []
for blog_post in blog_posts:
index.append('''<div class="col-md-6 item">
<div class="item-in">
<h4>{title}</h4>
<div class="seperator"></div>
<p>{date}: {excerpt}</p>
<a href="{url}">Read More<i class="fa fa-long-arrow-right"></i></a>
</div>
</div>'''.format(
exit(1)
from streaming_event_compliance.objects.variable.globalvar import gVars
from streaming_event_compliance.services import setup
from streaming_event_compliance.services.build_automata import build_automata
from streaming_event_compliance.database import dbtools
dbtools.empty_tables()
setup.init_automata()
if gVars.auto_status == 0:
start = time.clock()
console.secure("Start time: ", start)
try:
ServerLogging().log_info(func_name, "Building automata...")
build_automata.build_automata()
except ReadFileException as ec:
console.error(ec.message)
ServerLogging().log_error(func_name, "Training file cannot be read")
except ThreadException as ec:
ServerLogging().log_error(func_name, "Error with threads")
except Exception as ec:
ServerLogging().log_error(func_name, "Error")
ends = time.clock()
console.secure("[ The Total Time For Training Automata ]", str(ends - start) + "Seconds.")
else:
console.info("Automata have been created in database and read out! You can use it do compliance checking!")
ServerLogging().log_info(func_name, "Automata have been created in database and read out")
app.debug = False
app.run(host="0.0.0.0", port=5000, debug=True, use_reloader=False, threaded=True)
} for line in lines]
return data
data_save_path = os.path.join(os.getcwd(), 'data/data.sav')
if os.path.exists(data_save_path):
console.log("Reading from save file...")
data = pkl.load(open(data_save_path, 'rb'))
console.success("Finished reading data from save.")
else:
console.log("Did not find a save file.")
data = load_data()
pkl.dump(data, open(data_save_path, 'wb'))
console.success("Created save file.")
console.info("First data is sentence \"%s\" with emotion \'%s\'" %
(data[0]['raw'], data[0]['emotion']))
def make_wordlists(data):
wordlist = set()
mentions = set()
uppercase = set()
for datapoint in data:
words = re.sub('[ ]{1,10}', ',', datapoint['raw'])
words = re.sub('[?!]', '', words).split(',')
for word in words:
if len(word) > 0:
if word[0] == '@':
mentions.add(word[1:])
else:
if word.isupper():
my_voiceit = VoiceIt2(apiKey,apiToken)
id_user = '******'
cadastro_img = "https://observatoriodocinema.uol.com.br/wp-content/uploads/2021/01/Renato-Aragao-1.jpg"
verifica_img = "https://stcotvfoco.com.br/2021/01/renato-aragao-didi-carreira-trapalhoes-filmes-1.jpg"
image_fake = "https://conexao.segurosunimed.com.br/wp-content/uploads/2021/01/Capa-idoso-2.0.jpg"
voz_url = "https://to-vivo-app.s3.amazonaws.com/users/usr_54fbb7f880214222958ce92aef0f22f2/output+(2).flac"
#print(my_voiceit.check_user_exists(id_user))
#print(my_voiceit.create_face_enrollment_by_url(id_user, cadastro_img))
console.info("Verifica...do......")
r = my_voiceit.face_verification_by_url(id_user, verifica_img)
console.info(r['faceConfidence'])
console.info("Verificando image fake...")
fake = my_voiceit.face_verification_by_url(id_user, image_fake)
console.info(fake['faceConfidence'])
console.info("Verificando voz......")
my_voiceit.voice_verification_by_url(id_user, "pt-BR", "Juan Manoel Marinho Nascimento", voz_url)
# -------------------------------------------------
@app.route('/predict', methods=['POST'])
async def predict(request):
try:
return text(str(pipe.predict([request.json['title']])[0]))
except Exception as e:
console.error(e)
return text(e, status=500)
@app.route('/predict_many', methods=['POST'])
async def predict_many(request):
try:
return json(list(pipe.predict(request.json['titles'])))
except Exception as e:
console.error(e)
return text(e, status=500)
@app.route('/log')
async def log(request):
try:
return text(str(train_jobtitle.get_analytics()))
except Exception as e:
console.error(e)
return text(e, status=500)
console.info("Starting server...")
app.run()
import utils
from classifiers import JobTitle
from console_logging.console import Console
console = Console()
train = utils.load_dataset('features')
console.info("Loaded training dataset.")
test = utils.load_dataset('test')
console.info("Loaded testing dataset.")
pipe = JobTitle.pipe(train)
console.success("Finished training pipe.")
t = [_['title'] for _ in test]
e = [_['categories'][0] for _ in test]
accuracy = utils.evaluate(pipe, t, e)
console.success("%f accuracy" % accuracy)
def get_analytics():
analytics = utils.analyze(pipe, t, e, utils.categories(test))
# console.log('\n'+str(analytics))
return analytics
def __init__(self, connection, queues):
self.connection = connection
self.queues = queues
def on_message(self, body, message):
data = pickle.loads(body)
print(data['id_camera'])
tabela.insert(
dict(id_camera=data['id_camera'],
data_=data['data_'],
dt=data['dt'],
evento=data['evento']))
message.ack()
def get_consumers(self, Consumer, channel):
return [Consumer(queues=self.queues, callbacks=[self.on_message])]
def run():
console.info("[ CONSUMER - WORKER ] QUEUE: %s " % queue)
queues = [Queue(queue, exchange_, routing_key=routing_key)]
with Connection(rabbit_url, heartbeat=80) as conn:
worker = Worker(conn, queues)
console.info("[ CONSUMER - WORKER ] WORKER RUNNING ")
worker.run()
if __name__ == "__main__":
console.info("[ CONSUMER - WORKER ] ....STARTED.... ")
run()
from sanic import Sanic
import json as j
app = Sanic()
from sanic.response import json
from console_logging.console import Console
console = Console()
routing_table = dict()
with open('paths.json') as f:
for d in j.load(f):
routing_table[d["passkey"]] = d["url"]
console.info("Compiled routing table of %d routes." %
len(routing_table.keys()))
@app.middleware('response')
async def all_cors(r, s):
s.headers['Access-Control-Allow-Origin'] = '*'
s.headers['Access-Control-Allow-Headers'] = '*'
@app.route("/knock", methods=['POST', 'OPTIONS'])
async def whos_there(r):
if r.method == 'OPTIONS': return json({}, status=200)
if 'name' not in r.json.keys(): return json({}, status=500)
console.log("%s@%s is knocking." % (r.json['name'], r.ip))
if r.json['name'] in routing_table.keys():
p = routing_table[r.json['name']]
console.log("%s is answering." % p)
return json({"url": p}, status=200)
