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'))