def profile_score():
# Call appropriate scripts here
# Get user skill list
user_skill_list = []
location = application.get_profile(selectors=['location'])
#print (location['location']['name']) + ", " + str(location['location']['country']['code'])
for item in application.get_profile(selectors=['skills'])['skills']['values']:
user_skill_list.append(str(item['skill']['name']))
company = request.form['company_list']
title = request.form['profile_list']
profile_score, top_skill_vector = skill_score.score_evaluation(user_skill_list, company, title, location)
if math.isnan(profile_score):
profile_score = 0
profile_score = math.ceil(profile_score*100)
employee_scores = skill_score.evaluate_employee_scores(company, title, location)
employee_scores = [math.ceil(x*100) for x in employee_scores]
employee_scores = [value for value in employee_scores if not math.isnan(value)]
employee_scores.append(profile_score)
sorted_scores = sorted(employee_scores)
user_score_index = sorted_scores.index(profile_score) + 1
ax = plt.subplot('111', axisbg='#EBEBEB')
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(False)
plt.scatter(range(1,len(employee_scores)+1), sorted_scores, linestyle='--', marker='o', color='b')
plt.xlabel('Employee Number')
plt.ylabel('Profile Score')
plt.annotate('You', xy=(user_score_index, profile_score), xytext=(user_score_index - 0.3, profile_score + 5), textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5), arrowprops=dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
file_name = './app/static/img/graph_'+str(company)+'.png'
plt.savefig(file_name, bbox_inches='tight', transparent = True)
plt.clf()
return render_template("profile_score.html", company = company, score = profile_score, profile = title,
top_skills = top_skill_vector, user_skills = user_skill_list, curr_time = datetime.datetime.now().time())
def index():
global authentication
global application
global company_recommendations_based_on_score
global linkedin_user_name
global user_cluster
global recommended_companies
global recommended_profile
#if(os.path.isfile('./app/static/img/graph*.png')):
# os.remove('./app/static/img/graph.png')
my_dir = './app/static/img/'
for fname in os.listdir(my_dir):
if fname.startswith("graph"):
os.remove(os.path.join(my_dir, fname))
user = g.user
if authentication is None:
return redirect(url_for('authenticate_user'))
if application is None:
authentication.authorization_code = request.args['code']
authentication.get_access_token()
application = linkedin.LinkedInApplication(authentication)
# Get user skill list
user_skill_list = []
location = application.get_profile(selectors=['location'])
linkedin_user_name = application.get_profile(selectors=['first-name'])['firstName']
user_experience = ''
for item in application.get_profile(selectors=['positions'])['positions']['values']:
if 'summary' in item:
user_experience += item['summary']
#print get_keywords.get_keywords(user_experience)
user_cluster = cluster_recommender.cluster_score(user_experience, user_skill_list)
recommended_companies = [x.encode('ascii', 'ignore') for x in user_cluster[0]]
recommended_profile = user_cluster[1]
for item in application.get_profile(selectors=['skills'])['skills']['values']:
user_skill_list.append(str(item['skill']['name']))
for company in app.config['COMPANY_LIST']:
for title in app.config['PROFILE_LIST']:
profile_score, top_skill_vector = skill_score.score_evaluation(user_skill_list, company, title, None)
if (company, title) not in company_recommendations_based_on_score:
if math.isnan(profile_score):
profile_score = 0
company_recommendations_based_on_score[(company, title)] = math.ceil(profile_score*100)
#print application.search_job(selectors=[{'jobs': ['id', 'customer-job-code', 'posting-date', 'position', 'location']}], params={'company-name' : 'Google', 'job-title' : 'Software Engineer', 'count': 10})
form = CompanySelectForm()
if form.validate_on_submit():
return redirect(url_for('profile_score'))
return render_template("index.html",
title = 'Home',
user = linkedin_user_name,
form = form)
def index():
global authentication
global application
global company_recommendations_based_on_score
global linkedin_user_name
global user_cluster
global recommended_companies
global recommended_profile
#if(os.path.isfile('./app/static/img/graph*.png')):
# os.remove('./app/static/img/graph.png')
my_dir = './app/static/img/'
for fname in os.listdir(my_dir):
if fname.startswith("graph"):
os.remove(os.path.join(my_dir, fname))
user = g.user
if authentication is None:
return redirect(url_for('authenticate_user'))
if application is None:
authentication.authorization_code = request.args['code']
authentication.get_access_token()
application = linkedin.LinkedInApplication(authentication)
# Get user skill list
user_skill_list = []
location = application.get_profile(selectors=['location'])
linkedin_user_name = application.get_profile(
selectors=['first-name'])['firstName']
user_experience = ''
for item in application.get_profile(
selectors=['positions'])['positions']['values']:
if 'summary' in item:
user_experience += item['summary']
#print get_keywords.get_keywords(user_experience)
user_cluster = cluster_recommender.cluster_score(
user_experience, user_skill_list)
recommended_companies = [
x.encode('ascii', 'ignore') for x in user_cluster[0]
]
recommended_profile = user_cluster[1]
for item in application.get_profile(
selectors=['skills'])['skills']['values']:
user_skill_list.append(str(item['skill']['name']))
for company in app.config['COMPANY_LIST']:
for title in app.config['PROFILE_LIST']:
profile_score, top_skill_vector = skill_score.score_evaluation(
user_skill_list, company, title, None)
if (company,
title) not in company_recommendations_based_on_score:
if math.isnan(profile_score):
profile_score = 0
company_recommendations_based_on_score[(
company, title)] = math.ceil(profile_score * 100)
#print application.search_job(selectors=[{'jobs': ['id', 'customer-job-code', 'posting-date', 'position', 'location']}], params={'company-name' : 'Google', 'job-title' : 'Software Engineer', 'count': 10})
form = CompanySelectForm()
if form.validate_on_submit():
return redirect(url_for('profile_score'))
return render_template("index.html",
title='Home',
user=linkedin_user_name,
form=form)
def profile_score():
# Call appropriate scripts here
# Get user skill list
user_skill_list = []
location = application.get_profile(selectors=['location'])
#print (location['location']['name']) + ", " + str(location['location']['country']['code'])
for item in application.get_profile(
selectors=['skills'])['skills']['values']:
user_skill_list.append(str(item['skill']['name']))
company = request.form['company_list']
title = request.form['profile_list']
profile_score, top_skill_vector = skill_score.score_evaluation(
user_skill_list, company, title, location)
if math.isnan(profile_score):
profile_score = 0
profile_score = math.ceil(profile_score * 100)
employee_scores = skill_score.evaluate_employee_scores(
company, title, location)
employee_scores = [math.ceil(x * 100) for x in employee_scores]
employee_scores = [
value for value in employee_scores if not math.isnan(value)
]
employee_scores.append(profile_score)
sorted_scores = sorted(employee_scores)
user_score_index = sorted_scores.index(profile_score) + 1
ax = plt.subplot('111', axisbg='#EBEBEB')
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
ax.spines[spine].set_visible(False)
plt.scatter(range(1,
len(employee_scores) + 1),
sorted_scores,
linestyle='--',
marker='o',
color='b')
plt.xlabel('Employee Number')
plt.ylabel('Profile Score')
plt.annotate('You',
xy=(user_score_index, profile_score),
xytext=(user_score_index - 0.3, profile_score + 5),
textcoords='offset points',
ha='right',
va='bottom',
bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3,rad=0'))
file_name = './app/static/img/graph_' + str(company) + '.png'
plt.savefig(file_name, bbox_inches='tight', transparent=True)
plt.clf()
return render_template("profile_score.html",
company=company,
score=profile_score,
profile=title,
top_skills=top_skill_vector,
user_skills=user_skill_list,
curr_time=datetime.datetime.now().time())
