def main():
sys.path.append(os.path.join(os.path.dirname('__file__'), '..'))
from keras_en_parser_and_analyzer.library.rule_based_parser import ResumeParser
from keras_en_parser_and_analyzer.library.utility.io_utils import read_pdf_and_docx
current_dir = os.path.dirname('__file__')
current_dir = current_dir if current_dir is not '' else '.'
data_dir_path = current_dir + '/demo/data_' # directory to scan for any pdf and docx files
collected = read_pdf_and_docx(data_dir_path, command_logging=True)
for file_path, file_content in collected.items():
print('parsing file: ', file_path)
parser = ResumeParser()
parser.parse(file_content)
print(parser.raw) # print out the raw contents extracted from pdf or docx files
if parser.unknown is False:
print(parser.summary())
print('++++++++++++++++++++++++++++++++++++++++++')
print('count: ', len(collected))
def main():
current_dir = os.path.dirname(__file__)
current_dir = current_dir if current_dir is not '' else '.'
data_dir_path = current_dir + '/demo/data' # directory to scan for any pdf files
training_data_dir_path = current_dir + '/demo/data/training_data'
collected = read_pdf_and_docx(data_dir_path, command_logging=True, callback=lambda index, file_path, file_content: {
gui_annotate(training_data_dir_path, index, file_path, file_content)
})
print('count: ', len(collected))
def main():
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from keras_en_parser_and_analyzer.library.dl_based_parser import ResumeParser
from keras_en_parser_and_analyzer.library.utility.io_utils import read_pdf_and_docx
current_dir = os.path.dirname(__file__)
current_dir = current_dir if current_dir is not '' else '.'
data_dir_path = current_dir + '/data/resume_samples' # directory to scan for any pdf and docx files
def parse_resume(file_path, file_content):
print('parsing file: ', file_path)
parser = ResumeParser()
parser.load_model(current_dir + '/models')
parser.parse(file_content)
# print(parser.raw.encode("utf-8")) # print out the raw contents extracted from pdf or docx files
if parser.unknown is False:
summary, expertise, experience, knowledge, project = parser.summary()
# print(parser.summary().encode("utf-8"))
print(summary.encode('utf-8'))
vocabulary = []
for i in experience:
vocabulary.append(i.encode("utf-8"))
print(vocabulary)
# prefinal_vocabulary = []
# for i in vocabulary:
# try:
# prefinal_vocabulary.append(i.decode("utf-8"))
# except:
# prefinal_vocabulary.append(str(i,"utf-8"))
# print(prefinal_vocabulary[9])
final_vocabulary = []
# for i in prefinal_vocabulary:
# print(i)
# try:
# j = i.split(',')
# except:
# pass
# print(j)
print('++++++++++++++++++++++++++++++++++++++++++')
collected = read_pdf_and_docx(data_dir_path, command_logging=True, callback=lambda index, file_path, file_content: {
parse_resume(file_path, file_content)
})
print('count: ', len(collected))
def main():
data_dir_path = './data/resume_samples' # directory to scan for any pdf and docx files
collected = read_pdf_and_docx(data_dir_path)
for file_path, file_content in collected.items():
print('parsing file: ', file_path)
parser = ResumeParser()
parser.parse(file_content)
print(parser.raw) # print out the raw contents extracted from pdf or docx files
if parser.unknown is False:
print(parser.summary())
print('++++++++++++++++++++++++++++++++++++++++++')
print('count: ', len(collected))
