def recommend():
resume = request.form.get('resume', None)
requirements = request.form.get('requirements', None)
# Initialize recommender
recommender = Recommender(ngram_range=(1, 1),
use_tagger=True,
use_stem=False)
recommender.fit(resume, requirements)
# Requirement pair: [0] original requirement: [1]Extracted requirement
missing_requirement_pairs = recommender.find_missing_skills()
missing_requirements = [item[1] for item in missing_requirement_pairs]
course_recommedations = recommender.recommend()
if len(missing_requirements) > 0:
return render_template('recommend.html',
data=zip(missing_requirements,
course_recommedations))
return render_template('matchall.html')
def get_recommendations(resume_file,
requirement_file,
ngram_range=(1, 1),
use_tagger=False):
with open(resume_file, 'r') as handle:
resume = handle.read()
with open(requirement_file, 'r') as handle:
requirements = handle.read()
recommender = Recommender(ngram_range=ngram_range, use_tagger=use_tagger)
recommender.initialize_attributes(resume, requirements)
recommender.vectorize_resume()
recommender.vectorize_requirements()
missing_requirements = recommender.find_missing_skills()
print "Requirements:"
print recommender.requirements
print "preprocessed_requirements:"
print recommender.preprocessed_requirements
print "recommender.missing_requirements"
print recommender.missing_requirements
course_recommedations = recommender.recommend()
return missing_requirements, course_recommedations
