def deleteStory(storyId):
Story.objects.get(id=ObjectId(storyId)).delete()
try:
intentClassifier = IntentClassifier()
intentClassifier.train()
except:
pass
try:
os.remove("{}/{}.model".format(app.config["MODELS_DIR"],storyId))
except OSError:
pass
return buildResponse.sentOk()
def deleteStory(storyId):
if (session.__getattribute__('loginstat') == 'login'):
Story.objects.get(id=ObjectId(storyId)).delete()
try:
intentClassifier = IntentClassifier()
intentClassifier.train()
except BaseException:
pass
try:
os.remove("{}/{}.model".format(app.config["MODELS_DIR"], storyId))
except OSError:
pass
return buildResponse.sentOk()
else:
return render_template('index.html')
def deleteStory(storyId):
story = Story.objects.filter(id=ObjectId(storyId))
if g.botId:
story=story.filter(bot=g.botId)
botId=g.botId
else:
botId='default'
story.get().delete()
try:
intentClassifier = IntentClassifier()
intentClassifier.setBotId(g.botId)
intentClassifier.train()
except BaseException:
pass
try:
os.remove("{}/{},{}.model".format(app.config["MODELS_DIR"],botId, storyId))
except OSError:
pass
return buildResponse.sentOk()
def buildModel(storyId):
sequenceLabeler.train(storyId)
intentClassifier = IntentClassifier()
intentClassifier.setBotId(g.botId)
intentClassifier.train()
return buildResponse.sentOk()
import nltk
import os
# Downloading necessary NLTK datasets
nltk.download("stopwords")
nltk.download("wordnet")
nltk.download('averaged_perceptron_tagger')
nltk.download('punkt')
# creating directory for storing chat logs
if not os.path.exists("logs"):
os.makedirs("logs")
try:
print("Training models..")
from app.core.intentClassifier import IntentClassifier
intentClassifier = IntentClassifier()
intentClassifier.setBotId('default')
intentClassifier.train()
print("Training models finished..")
except Exception as e:
e = str(e)
if e == "NO_DATA":
e = "load Data first into mongodb. Reffer Readme."
print("Could not train models..skipping.. (reason: {})".format(e))