def post(self):
ifile = self.request.get("summary")
check = self.request.get("isteam")
if not ifile:
logging.error("no file selected")
self.redirect( "/addsummary" )
elif check == "team":
mytuple = self.team_process( ifile )
bests = mytuple[0]
contestdate = mytuple[1]
s = Summary(
summary = ifile,
bests = bests,
name = contestdate
)
s.put()
self.redirect( "/archive" )
else:
mytuple = self.process( ifile )
bests = mytuple[0]
contestdate = mytuple[1]
s = Summary(
summary = ifile,
bests = bests,
name = contestdate
)
s.put()
self.redirect( "/archive" )
def run_decode():
logging.debug('decode your input by our pretrained model')
try:
source = request.get_json()['source'] # GET request with String from frontend directly
logging.debug('input: {}'.format(source)) # GET String-type context from the backend
try:
logging.debug('using the pretrained model.')
sentNums, summary = summarizationModel.decode.run_(source)
except Exception as e:
logging.error(e)
else:
logging.debug('The number of sentences is {}'.format(sentNums))
logging.debug('The abstract is that {}'.format(summary))
results = {'sent_no': sentNums, 'final': summary}
try:
article = Content(text=source)
abstract = Summary(text=summary)
pair = Article(article=article.id, abstract=abstract.id)
article.save()
abstract.save()
pair.save()
except Exception as e:
logging.error(e)
return json.dumps(results)
except:
message = {'message' : 'Fail to catch the data from client.'}
return json.dumps(message)
def get_summary(self, tr_list):
map = self.get_map_from_tr_list(tr_list)
return Summary(map['Previous Close'], map['Open'], map['Bid'],
map['Ask'], map["Day's Range"], map['52 Week Range'],
map['Volume'], map['Avg. Volume'],
get_number_from_currency(map.get('Market Cap', None)),
map.get('Beta',
None), map['PE Ratio (TTM)'], map['EPS (TTM)'],
map['Earnings Date'], map['Forward Dividend & Yield'],
map['Ex-Dividend Date'], map['1y Target Est'])
def train():
"""
A simple Neural Network
:return:
"""
logging.info("Start Training!")
corpus_type = 'train'
summary = Summary(args.batch_size, args.max_len)
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(summary.parameters(), lr=args.lr)
summary.train()
start_epoch = 0
if args.model_name:
checkpoint = torch.load(args.load_model)
summary = checkpoint['model']
start_epoch = checkpoint['epochs']
start_epoch += 1 if start_epoch != 0 else start_epoch
for epoch in range(start_epoch, args.epoch):
epoch_loss = 0
batch_num = 0
for i, batch in enumerate(bachify_data(corpus_type)):
batch_df, batch_label, _, _ = batch
batch_df = torch.tensor(batch_df)
batch_label = torch.tensor(batch_label)
binary_output = summary(batch_df)
# calculate loss
loss = criterion(binary_output, batch_label)
loss.backward()
optimizer.step()
epoch_loss += loss
batch_num += 1
logging.info("Epoch {}: Total loss is {}, Avg loss is {}".format(epoch, epoch_loss, epoch_loss/batch_num))
# store model
model_name = "{}_epoch_model.tar".format(epoch)
directory = os.path.join(args.save_path, model_name)
if not os.path.exists(directory):
os.makedirs(directory)
torch.save({
'model': summary.state_dict(),
'loss': epoch_loss / batch_num,
"epochs": epoch
}, directory)
logging.info("Finish Training!")
