def add_to_train():
global count, cached_training_set, classified, sender, subject, email_text
values = request.values
if classified is None:
return redirect('/')
category = int(values['category'])
inv_categories = {v: k for k, v in categories.items()}
category = inv_categories[classified] if category == 0 else category
temporary = build_training_set_from_text(text=email_text, category=category, sender=sender, subject=subject)
cached_training_set += temporary
count += 1
if count >= 2:
training_set = load_training_set()
training_set += cached_training_set
save_training_set(training_set)
print 'Training set updated and persisted with new items'
count, cached_training_set, classified = 0, [], None
return redirect('/')
from ml.training_set_util import build_training_set, save_training_set save_training_set(build_training_set(path='email_dataset'))
