def submit():
global university
global tree_json
if "major" in request.form:
major = str(request.form["major"]).strip('"')
if "university" in request.form:
university_ip = str(request.form["university"]).strip('"')
# create_data_for_graph(university_ip, major, skills_employer, univ_major_number, major_code_lookup)
tree_json = create_data_for_tree(
university_ip,
major,
skills_employer_tree,
univ_major_number,
major_code_lookup,
employer_second_degree_tree
)
else:
# create_data_for_graph(university, major, skills_employer, univ_major_number, major_code_lookup)
tree_json = create_data_for_tree(
university,
major,
skills_employer_tree,
univ_major_number,
major_code_lookup,
employer_second_degree_tree
)
return json.dumps(tree_json)
def submit():
global university
global tree_json
if "major" in request.form:
major = str(request.form["major"]).strip('"')
if "university" in request.form:
university_ip = str(request.form["university"]).strip('"')
# create_data_for_graph(university_ip, major, skills_employer, univ_major_number, major_code_lookup)
tree_json = create_data_for_tree(university_ip, major,
skills_employer_tree,
univ_major_number,
major_code_lookup,
employer_second_degree_tree)
else:
# create_data_for_graph(university, major, skills_employer, univ_major_number, major_code_lookup)
tree_json = create_data_for_tree(university, major,
skills_employer_tree,
univ_major_number,
major_code_lookup,
employer_second_degree_tree)
return json.dumps(tree_json)
def analyze():
# global results_json
global university
global tree_json
if request.method:
# Get and save file from browser upload
files = request.files['file']
if files:
filename = str(files.filename)
extension = filename.rsplit('.', 1)[1]
filename_without_extension = filename.rsplit('.', 1)[0]
files.save(os.path.join(iHire.config['UPLOAD_FOLDER'], filename))
if extension == 'pdf':
text_from_pdf = extract_text_from_pdf(filename)
text_from_pdf = text_from_pdf.replace('\xc2\xa0', ' ')
with open(filename_without_extension + '.txt', 'wb') as write_file:
write_file.write(text_from_pdf)
textfile_name = filename_without_extension + '.txt'
else:
textfile_name = filename
university = extract_univ(open(textfile_name).read(), univ_dict, univ_normalize)
print filename
# create_data_for_graph(university, "", skills_employer, univ_major_number, major_code_lookup)
tree_json = create_data_for_tree(
university,
"",
skills_employer_tree,
univ_major_number,
major_code_lookup,
employer_second_degree_tree
)
resume_text = [open(textfile_name).read()]
predicted_decision = model.decision_function(resume_text)
top_predictions, normalized_prediction_score = get_top_predictions(predicted_decision)
out = dict()
skills_map_with_percent_list = []
titles = sorted(skills_map_with_percent.keys())
for title in titles:
temp_skill_map = dict()
temp_skill_map[title] = skills_map_with_percent[title]
skills_map_with_percent_list.append(temp_skill_map)
out["university"] = university
out["skills_map"] = skills_map_with_percent_list
out["titles"] = titles
out["candidate_skills"] = dict()
out["title_data"] = dict()
try:
tokens = nltk.word_tokenize(resume_text[0].lower())
except UnicodeDecodeError:
tokens = nltk.word_tokenize(resume_text[0].decode('utf-8').lower())
skill_score = []
for pred in top_predictions:
try:
top15 = skills_map_with_percent[title_title_map[pred]]["skills"][:15]
except KeyError:
top15 = []
temp_skill_list = [t for t in top15 if len(t) > 1 and t.lower() in tokens]
out["candidate_skills"][title_title_map[pred]] = temp_skill_list
out["title_data"][title_title_map[pred]] = titles_data[title_title_map[pred]]
skill_score.append(int(len(temp_skill_list) / 15.0 * 100.0))
final_score = [sum(x)/2 for x in zip(normalized_prediction_score, skill_score)]
final_titles_list = []
sorted_score_indexes = [i[0] for i in sorted(enumerate(final_score), key=lambda x:x[1], reverse=True)]
for s in sorted_score_indexes:
final_titles_list.append(title_title_map[top_predictions[s]])
final_score_sorted = sorted(final_score, reverse=True)
out["final_prediction_list"] = final_titles_list
out["final_score_sorted"] = final_score_sorted
out["tree_json"] = json.dumps(tree_json)
print final_titles_list[:5]
print final_score_sorted[:5]
if os.path.isfile(textfile_name):
os.remove(textfile_name)
if os.path.isfile(filename):
os.remove(filename)
# results_json = OrderedDict(out)
return json.dumps(OrderedDict(out))
def analyze():
# global results_json
global university
global tree_json
if request.method:
# Get and save file from browser upload
files = request.files['file']
if files:
filename = str(files.filename)
extension = filename.rsplit('.', 1)[1]
filename_without_extension = filename.rsplit('.', 1)[0]
files.save(os.path.join(iHire.config['UPLOAD_FOLDER'], filename))
if extension == 'pdf':
text_from_pdf = extract_text_from_pdf(filename)
text_from_pdf = text_from_pdf.replace('\xc2\xa0', ' ')
with open(filename_without_extension + '.txt',
'wb') as write_file:
write_file.write(text_from_pdf)
textfile_name = filename_without_extension + '.txt'
else:
textfile_name = filename
university = extract_univ(
open(textfile_name).read(), univ_dict, univ_normalize)
print filename
# create_data_for_graph(university, "", skills_employer, univ_major_number, major_code_lookup)
tree_json = create_data_for_tree(university, "",
skills_employer_tree,
univ_major_number,
major_code_lookup,
employer_second_degree_tree)
resume_text = [open(textfile_name).read()]
predicted_decision = model.decision_function(resume_text)
top_predictions, normalized_prediction_score = get_top_predictions(
predicted_decision)
out = dict()
skills_map_with_percent_list = []
titles = sorted(skills_map_with_percent.keys())
for title in titles:
temp_skill_map = dict()
temp_skill_map[title] = skills_map_with_percent[title]
skills_map_with_percent_list.append(temp_skill_map)
out["university"] = university
out["skills_map"] = skills_map_with_percent_list
out["titles"] = titles
out["candidate_skills"] = dict()
out["title_data"] = dict()
try:
tokens = nltk.word_tokenize(resume_text[0].lower())
except UnicodeDecodeError:
tokens = nltk.word_tokenize(
resume_text[0].decode('utf-8').lower())
skill_score = []
for pred in top_predictions:
try:
top15 = skills_map_with_percent[
title_title_map[pred]]["skills"][:15]
except KeyError:
top15 = []
temp_skill_list = [
t for t in top15 if len(t) > 1 and t.lower() in tokens
]
out["candidate_skills"][
title_title_map[pred]] = temp_skill_list
out["title_data"][title_title_map[pred]] = titles_data[
title_title_map[pred]]
skill_score.append(int(len(temp_skill_list) / 15.0 * 100.0))
final_score = [
sum(x) / 2
for x in zip(normalized_prediction_score, skill_score)
]
final_titles_list = []
sorted_score_indexes = [
i[0] for i in sorted(
enumerate(final_score), key=lambda x: x[1], reverse=True)
]
for s in sorted_score_indexes:
final_titles_list.append(title_title_map[top_predictions[s]])
final_score_sorted = sorted(final_score, reverse=True)
out["final_prediction_list"] = final_titles_list
out["final_score_sorted"] = final_score_sorted
out["tree_json"] = json.dumps(tree_json)
print final_titles_list[:5]
print final_score_sorted[:5]
if os.path.isfile(textfile_name):
os.remove(textfile_name)
if os.path.isfile(filename):
os.remove(filename)
# results_json = OrderedDict(out)
return json.dumps(OrderedDict(out))
