def handle_resume_post(self):
# Get the name of the uploaded file
file = request.files['file']
if file and self.__allowed_file(file.filename):
# Save file to upload folder
file.save(os.path.join(self.__app.config['UPLOAD_FOLDER'], file.filename))
# use crf here
annotator = Annotator()
annotated_resume = annotator.annotate_using_trained_model(self.__app.config['UPLOAD_FOLDER'] + self.__seperator + file.filename)
tagged_resume = self.__crfsuite.tag_doc(annotated_resume)
template = render_template('%s.xml' % self.__path_output_xml, entities=tagged_resume)
response = make_response(template)
response.headers['Content-Type'] = 'application/xml'
return response
else:
return "Invalid file type, use PDF, DOC or DOCX", 406
def get_ies_scores(self):
extractor = Extractor()
ies_filenames = extractor.populate_file_names(self.__ies_accuracy_test)
ies_filenames = extractor.filter_by_valid_exts(ies_filenames)
filenames, resume_content = extractor.read_resume_content_tika_api(
ies_filenames, self.__ies_accuracy_test)
filenames, resume_content = extractor.remove_empty_resumes(
filenames, resume_content)
resume_labels = extractor.read_resume_labels(self.__ies_accuracy_test,
filenames)
true_edu_insts = [
extractor.get_edu_institutions(xml_tree)
for xml_tree in resume_labels
]
true_edu_majors = [
extractor.get_edu_majors(xml_tree) for xml_tree in resume_labels
]
true_emp_names = [
extractor.get_company_names(xml_tree) for xml_tree in resume_labels
]
true_emp_jtitles = [
extractor.get_job_titles(xml_tree) for xml_tree in resume_labels
]
cs = CrfSuite()
cs.load_tagger()
annotator = Annotator()
annotated_resumes = [
annotator.annotate_using_trained_model(self.__ies_accuracy_test +
self.__seperator +
filename[0] + filename[1])
for filename in filenames
]
predicted_entity_list = [
cs.tag_doc(resume) for resume in annotated_resumes
]
ies_edu_insts = [
extractor.get_edu_institutions_from_list(entity_list)
for entity_list in predicted_entity_list
]
ies_edu_majors = [
extractor.get_edu_major_from_list(entity_list)
for entity_list in predicted_entity_list
]
ies_emp_names = [
extractor.get_company_names_from_list(entity_list)
for entity_list in predicted_entity_list
]
ies_emp_jtitles = [
extractor.get_company_position_from_list(entity_list)
for entity_list in predicted_entity_list
]
tokeniser = Tokeniser()
true_edu_insts = tokeniser.docs_tolower(
tokeniser.tokenise_doclines_to_words(true_edu_insts))
true_edu_majors = tokeniser.docs_tolower(
tokeniser.tokenise_doclines_to_words(true_edu_majors))
true_emp_names = tokeniser.docs_tolower(
tokeniser.tokenise_doclines_to_words(true_emp_names))
true_emp_jtitles = tokeniser.docs_tolower(
tokeniser.tokenise_doclines_to_words(true_emp_jtitles))
ies_edu_insts = tokeniser.docs_tolower(
tokeniser.tokenise_doclines_to_words(ies_edu_insts))
ies_edu_majors = tokeniser.docs_tolower(
tokeniser.tokenise_doclines_to_words(ies_edu_majors))
ies_emp_names = tokeniser.docs_tolower(
tokeniser.tokenise_doclines_to_words(ies_emp_names))
ies_emp_jtitles = tokeniser.docs_tolower(
tokeniser.tokenise_doclines_to_words(ies_emp_jtitles))
edu_insts_match_score = self.score_matches(ies_edu_insts,
true_edu_insts)
edu_majors_match_score = self.score_matches(ies_edu_majors,
true_edu_majors)
emp_names_match_score = self.score_matches(ies_emp_names,
true_emp_names)
emp_jtitles_match_score = self.score_matches(ies_emp_jtitles,
true_emp_jtitles)
print(edu_insts_match_score)
print(edu_majors_match_score)
print(emp_names_match_score)
print(emp_jtitles_match_score)
