def apply_for_the_job(driver, iter):
if (iter >= iter_max):
return
else:
item = 1
elements = functions.find_classes(driver, job_row_class)
for element in elements:
if (check_if_job(element)):
item = item + 1
try:
print('Opening new job tab')
functions.open_in_new_tab(
functions.find_class(element, link_class), driver)
functions.timeout(2)
print('Going to new window')
driver.switch_to_window(driver.window_handles[iter + 1])
functions.timeout(2)
print('Getting details of the job')
get_details_of_opening(driver)
# Apply for the jobs
make_application(driver)
# Go into iteration
apply_for_the_job(driver, iter + 1)
print('closing the job')
functions.close_browser(driver)
#switch back to original window
functions.timeout(2)
print('going to prev window')
driver.switch_to_window(driver.window_handles[iter])
except:
item = item - 1
if (item > 10):
break
return 0
for i in range(0, len(login_details)):
if login_details.loc[i, 'website'] == portal:
loginid = login_details.loc[i, 'loginid']
password = login_details.loc[i, 'password']
# Open website
driver.get(website)
# Login into the website
print("Logging into the website")
driver = functions.login_function(driver, login_placeholder_class, loginid,
password_placeholder_class, password)
functions.timeout(15)
print('Finding elements to connect')
elements = functions.find_classes(driver, element_class)
iter = 0
for element in elements:
try:
connect_button = functions.find_class(element,
connect_button_class)
print(connect_button.text)
if (connect_button.text == 'Connect'):
connect_button.click()
iter = iter + 1
functions.timeout(1.5)
if (iter >= 100):
break
except:
continue
argparser.add_argument('--path', type=str, default='../data/person_detection/')
argparser.add_argument('--labels', default=np.array(['car_side', 'cellphone', 'person']))
argparser.add_argument('--n_epochs', type=int, default=2)
argparser.add_argument('--batch_size', type=int, default=16)
argparser.add_argument('--criterion', default=nn.CrossEntropyLoss())
argparser.add_argument('--momentum', type=float, default=0.9)
argparser.add_argument('--transform', default=transform)
argparser.add_argument('--print_every', type=int, default=2)
argparser.add_argument('--learning_rate', type=float, default=0.001)
argparser.add_argument('--save_model', type=bool, default=False)
argparser.add_argument('--feature_extract', type=bool, default=True)
args = argparser.parse_args()
print(args)
data_set = torchvision.datasets.ImageFolder(root=args.path, transform=args.transform)
classes = func.find_classes(args.path)
# print(data_set.targets)
print(data_set.class_to_idx)
train_size = len(data_set) // 2
test_size = len(data_set) - train_size
data_train, data_test = random_split(data_set, [train_size, test_size])
train_loader = DataLoader(data_train, batch_size=args.batch_size, shuffle=False, sampler=None, num_workers=0)
test_loader = DataLoader(data_test, batch_size=args.batch_size, shuffle=False, sampler=None, num_workers=0)
print('Train sample: ', train_size)
print('Test sample: ', test_size)
model = alexnet(pretrained=True)
model = func.set_parameter_requires_grad(model, args.feature_extract)
model.classifier[6] = nn.Linear(in_features=4096, out_features=len(args.labels))
def get_details_of_opening(driver):
# Declaring variables
jd = ''
job_title = ''
hashtag = ''
last_login = ''
postedBy = ''
postedIn = ''
jobCode = ''
Location = ''
postedOn = ''
views = ''
applications = ''
position = ''
recruiterAction = ''
jd = functions.find_class(driver, job_description_class).text
job_title = functions.find_class(driver, title_class).text
hashtag = functions.find_class(driver, hastag_class).text
elements = functions.find_classes(driver, job_details_class)
for element in elements:
try:
sections = element.text.split('\n')
if (sections[0] == 'Last Login:'******'Posted by'):
postedBy = sections[1]
position = sections[2]
elif (sections[0] == 'Posted in'):
postedIn = sections[1]
elif (sections[0] == 'Job Code'):
jobCode = sections[1]
elif (sections[0] == 'Location'):
Location = sections[1]
elif (sections[0] == 'Posted On'):
postedOn = sections[1]
elif (sections[0] == 'Views'):
views = sections[1]
elif (sections[0] == 'Applications'):
applications = sections[1]
elif (sections[0] == 'Recruiter Actions (what does this mean?)'):
recruiterAction = sections[1]
else:
continue
except:
continue
print(element)
outfile = output_file + '.csv'
dict_data.append({
'Job Code': jobCode,
'Title': job_title,
'Hashtag': hashtag,
'Description': jd,
'Last Login': last_login,
'Posted By': postedBy,
'Position': position,
'Posted In': postedIn,
'Location': Location,
'Views': views,
'Applications': applications,
'Recruiter Actions': recruiterAction
})
functions.write_to_csv(outfile, dict_data, csv_columns)
return 1
def apply_filter(driver):
functions.find_classes(driver, workex_filter_class)[1].click()
dropdown_menu = functions.find_class(driver, workex_chooser_class)
functions.find_x_path(dropdown_menu, "//a[@rel='2']").click()
functions.find_class(driver, filter_button_class).click()
functions.timeout(2)