def write_html(data, domain, word_occurrence, filename):
user_counter = 0
for d in data:
for k, v in d.items():
user_counter += 1
filename = filename + '.html'
with open(filename, 'w') as f:
title = 'Linky: % s' % domain
f.write(html.header(title))
headers = [
'picture', 'fullname', 'firstname', 'middlename', 'surname',
'email', 'current role', 'current company'
]
f.write(html.h3_span(['Users', user_counter]))
f.write(html.p('Click the users image to view their LinkedIn!'))
f.write(html.table_head(headers))
for d in data:
for k, v in d.items():
fullname = k
profile_url = v[0]
picture = v[1]
if picture == None:
picture = False
firstname = v[2]
middlename = v[3]
surname = v[4]
email = v[5]
current_role = v[6]
current_company = v[7]
f.write('<tr>\n')
f.write(html.table_picture(profile_url, picture))
f.write(html.table_entry(fullname))
f.write(html.table_entry(firstname))
f.write(html.table_entry(middlename))
f.write(html.table_entry(surname))
f.write(html.table_entry(email))
f.write(html.table_entry(current_role))
f.write(html.table_entry(current_company))
f.write('</tr>\n')
f.write('</tbody>\n')
f.write('</table>\n')
f.write(html.h3('Top roles'))
f.write(
html.
p('The following table shows the most common roles within the designated organisation.\nRunning this tool again with these top 3 results as keywords will result in more specific data as the api data extraction only pulls 1000 results.'
))
f.write(html.table_head(['Role', 'Count']))
for role, count in word_occurrence.items():
f.write('<tr>\n')
f.write(html.table_entry(role))
f.write(html.table_entry(count))
f.write('</tr>\n')
f.write('</tbody>\n')
f.write('</table>\n')
f.write(html.footer())
def write_html(users, data, job_role_count, filename):
domain = data.domain
validation = data.validation
user_counter = 0
for user in users:
user_counter += 1
filename = filename + '.html'
with open(filename, 'w') as f:
title = 'Linky: % s' % domain
f.write(html.header(title))
if validation != None:
headers = [
'picture', 'fullname', 'firstname', 'middlename', 'surname',
'email', 'email validation', 'current role', 'current company'
]
else:
headers = [
'picture', 'fullname', 'firstname', 'middlename', 'surname',
'email', 'current role', 'current company'
]
f.write(html.h3_span(['User Count', user_counter]))
f.write(html.p('Click the users image to view their LinkedIn!'))
f.write(html.input_box())
f.write(html.table_head(headers))
for user in users:
fullname = user.fullname
profile_url = user.profile_url
picture = user.picture
if picture == None:
picture = False
firstname = user.firstname
middlename = user.middlename
surname = user.surname
email = user.email
current_role = user.current_role
current_company = user.current_company
f.write('<tr>\n')
f.write(html.table_picture(profile_url, picture))
f.write(html.table_entry(fullname))
f.write(html.table_entry(firstname))
f.write(html.table_entry(middlename))
f.write(html.table_entry(surname))
f.write(html.table_entry(email))
if validation != None:
validated = user.validated
if validated == None:
f.write(html.table_entry('Unable to validate'))
else:
if validated == True:
try:
f.write(
html.table_entry('Got creds: %s') %
validated[1])
except:
f.write(html.table_entry(str(validated)))
else:
f.write(html.table_entry(str(validated)))
f.write(html.table_entry(current_role))
f.write(html.table_entry(current_company))
f.write('</tr>\n')
f.write('</tbody>\n')
f.write('</table>\n')
f.write(html.h3('Top roles'))
f.write(
html.
p('The following table shows the most common roles within the designated organisation.\nRunning this tool again with these top 3 results as keywords will result in more specific data as the api data extraction only pulls 1000 results.'
))
f.write(html.table_head(['Role', 'Count']))
for role, count in job_role_count.items():
f.write('<tr>\n')
f.write(html.table_entry(role))
f.write(html.table_entry(count))
f.write('</tr>\n')
f.write('</tbody>\n')
f.write('</table>\n')
f.write(html.footer())