Datasets:
jblitzar
/
github-python

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return self.__isint(v) or v == '\n' or '-->' in v
def __process_sentences(self, v) -> List[str]:
sentence = tokenize.sent_tokenize(v)
return sentence
def save_data(self, save_path, sentences) -> None:
with open(save_path, 'w') as f:
for sentence in sentences:
f.write("%s\n" % sentence)
def run(self) -> List[str]:
total: str = ''
for data in self.all_data:
if not self.__should_skip(data):
cleaned = data.replace('>', '').replace('\n', '').strip()
if cleaned:
total += ' ' + cleaned
sentences = self.__process_sentences(total)
return sentences
def convert_to_paragraphs(self) -> str:
sentences: List[str] = self.run()
return ' '.join([sentence.strip() for sentence in sentences]).strip()
@app.route('/summarize', methods=['POST'])
def convert_raw_text():
ratio = float(request.args.get('ratio', 0.2))
min_length = int(request.args.get('min_length', 25))
max_length = int(request.args.get('max_length', 500))
data = request.data
if not data:
abort(make_response(jsonify(message="Request must have raw text"), 400))
parsed = Parser(data).convert_to_paragraphs()
summary = summarizer(parsed, ratio=ratio, min_length=min_length, max_length=max_length)
return jsonify({
'summary': summary
})
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='')
parser.add_argument('-model', dest='model', default='bert-base-multilingual-uncased', help='The model to use')
parser.add_argument('-greediness', dest='greediness', help='', default=0.45)
parser.add_argument('-reduce', dest='reduce', help='', default='mean')
parser.add_argument('-hidden', dest='hidden', help='', default=-2)
parser.add_argument('-port', dest='port', help='', default=5000)
parser.add_argument('-host', dest='host', help='', default='0.0.0.0')
args = parser.parse_args()
print(f"Using Model: {args.model}")
summarizer = Summarizer(
model=args.model,
hidden=int(args.hidden),
reduce_option=args.reduce,
greedyness=float(args.greediness)
)
app.run(host=args.host, port=int(args.port) ,debug=True)
# <FILESEP>
# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
class Data_set(object):
def __init__(self, config, shuffle, name):
self.tfrecord_file = config.tfdata_path
self.batch_size = config.batch_size
self.min_after_dequeue = config.min_after_dequeue
self.capacity = config.capacity
self.actual_image_size = config.train_image_size
self.shuffle = shuffle
self.name = name
def read_processing_generate_image_label_batch(self):
if self.name.find('train') != -1:
# get filename list
tfrecord_filename = tf.gfile.Glob(self.tfrecord_file + '*%s*' % self.name)
print('tfrecord train filename', tfrecord_filename)
filename_queue = tf.train.string_input_producer(tfrecord_filename, num_epochs=None, shuffle=True)
# get tensor of image/label
image, label = read_tfrecord_and_decode_into_image_label_pair_tensors(filename_queue,
self.actual_image_size)
#image = channels_image_standardization(image)
image = image_standardization(image)
image = dataaugmentation(image)
image_batch, label_batch = tf.train.shuffle_batch([image, label],
batch_size=self.batch_size,
capacity=self.capacity,
num_threads=2,
min_after_dequeue=self.min_after_dequeue)
image_batch = tf.contrib.image.rotate(image_batch, tf.random_uniform(shape = (tf.shape(image_batch)[0], ), minval=-0.5, maxval=0.5, seed=37), interpolation='BILINEAR')
else: