def post(self):
json_data = request.get_json(force=True)
if not json_data or not 'project_name' in json_data or not 'start_date' in json_data or not 'end_date' in json_data or not 'priority' in json_data or not 'user_id' in json_data:
return {"error" : "Input data missing"}, 400
project = Project.query.filter_by(project_name=json_data['project_name']).first()
if project:
return {'message': 'Project already exists'}, 400
user = User.query.filter_by(id=json_data['user_id']).first()
if not user:
return {'message': 'User doest not exist'}, 400
project = Project(
project_name = json_data['project_name'],
start_date = HelperUtil.stringToDate(self, json_data['start_date']),
end_date = HelperUtil.stringToDate(self, json_data['end_date']),
priority = json_data['priority'],
status = constant.ACTIVE,
user_id = json_data['user_id']
)
db.session.add(project)
db.session.commit()
result = project_schema.dump(project)
return {"message" : "Saved successfully!", "data" : result}, 201
def get_projects():
url = "https://kwork.ru/projects"
headers = {'User-Agent': 'Mozilla/5.0 (Windows; U; Windows NT 6.0; en-GB; rv:1.9.0.11) \
Gecko/2009060215 Firefox/3.0.11 (.NET CLR 3.5.30729)'}
projects = []
response = requests.get(url, headers=headers)
log.info( "Status code: %d. Length: %d " % ( response.status_code, len( response.text ) ) )
html = response.text
soup = BeautifulSoup( html, "html.parser" )
items = soup.select("div[class*=js-card]")
for item in items[:]:
title = item.select_one("div[class*=header-title]")
title = title.text if title else "Error title"
price = item.select_one("div.wants-card__right")
price = re.findall( r"\d{3}|\d{1,2}\s\d{3}", str(price) )
price = " - ".join(price)
description = item.select_one("div.breakwords.hidden")
description = description.text.replace("Скрыть","").strip() if description else "Description error"
if description == "Description error":
description = item.select_one("div.breakwords.first-letter ~ div")
description = description.text if description else "Description error2"
# import pdb;pdb.set_trace()
proposal_count = item.find(lambda tag:tag.name == "span" and "Предложений:" in tag.text)
proposal_count = re.findall(r"\d+", proposal_count.text)[0] if proposal_count else "Prop error"
author = item.select_one("a.v-align-t")
author = author.text if author else "Author error"
link = item.select_one("div.wants-card__header-title a")
link = link['href'] if link else "Link error"
timer = item.find( lambda tag:tag.name == "span" and "Осталось" in tag.text)
timer = timer.text if timer else "timer error"
params = (title, description, author, proposal_count,
price, timer, link)
project = Project( *params )
projects.append( project )
return projects
def infer(args):
data = Data(str(args.test))
model = Project(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()
data_loader = torch.utils.data.DataLoader(
data, batch_size=len(data), num_workers=24
)
for i in range(data_loader):
results = model(data_loader)
file = open(args.testcsv, "w")
file.write("\n".join(results.tolist()))
file.close()
print("Done....")
def main():
parser = argparse.ArgumentParser(description="Process some integers.")
parser.add_argument("--file")
parser.add_argument("--epouch")
parser.add_argument("--batch_size")
parser.add_argument("--num_workers")
args = parser.parse_args()
batch_size = int(args.batch_size)
data = Data(args.file)
validation_split = 0.2
shuffle_dataset = True
random_seed = 42
dataset_size = len(data)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = torch.utils.data.DataLoader(data,
batch_size=batch_size,
sampler=train_sampler,
num_workers=24)
validation_loader = torch.utils.data.DataLoader(
data,
batch_size=len(valid_sampler),
sampler=valid_sampler,
num_workers=24)
net = Project()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
train(net, criterion, int(args.epouch), optimizer, train_loader,
validation_loader)
