def run_bot(self, sentence):
"""function to run the bot"""
intent = action_predict(str(sentence), self.botname)
reply = self.dsl(intent, sentence)
if reply == "none":
reply = random.choice([
"there must be an error", "ask that gopi to fix me :(",
"sorry this is a prototype"
])
return reply
def action_train_protocol(sentence, Train=True):
"""function to train the action prediction model"""
if Train:
training_data = []
with open('datasets/action_dataset.json') as data_file:
data = json.load(data_file)
for line in data:
#fetching training data
training_data.append(line)
action_train(training_data, 20000, 'bulb')
print(action_predict(sentence, 'bulb'))
def send_recieve():
control = {'status': None, 'text': None, 'blue_state': 0, 'red_state': 0}
if request.method == 'GET':
try:
data = json.loads(request.data.decode('utf-8'))
log(data)
except (ValueError, TypeError, KeyError):
print("Error caught")
control['status'] = 0
return json.dumps(control)
control['status'] = 1
data = request.args.get('text')
intent = action_predict(str(data), 'bulb')
ent = ner_predict(str(data), 'bulb')
entities = []
for i in ent:
entities.append(i[1])
log(entities)
if intent == 'ON':
control['text'] = "intent recognised ON \n"
if 'blue' in entities:
control['text'] = control[
'text'] + 'entity recognised Blue light \n'
control['blue_state'] = 1
elif 'red' in entities:
control['text'] = control[
'text'] + 'entity recognised Red light \n'
control['red_state'] = 1
elif 'lights' in entities:
control['text'] = control[
'text'] + 'entity recognised Blue light and Red light\n'
control['red_state'] = 1
control['blue_state'] = 1
elif intent == 'OFF':
control['text'] = "intent recognised OFF \n"
if 'blue' in entities:
control['text'] = control[
'text'] + 'entity recognised Blue light \n'
control['blue_state'] = 2
elif 'red' in entities:
control['text'] = control[
'text'] + 'entity recognised Red light \n'
control['red_state'] = 2
elif 'lights' in entities:
control['text'] = control[
'text'] + 'entity recognised Blue light and Blue light\n'
control['red_state'] = 2
control['blue_state'] = 2
control = json.dumps(control)
return control
def action_train_protocol(self, sentence, Train=True):
"""function to train the action prediction model"""
if Train:
training_data = []
with open(self.botname +
'/datasets/action_dataset.json') as data_file:
data = json.load(data_file)
for line in data:
#fetching training data
training_data.append(line)
action_train(training_data, 20000,
self.botname) #training the model
print("intent:" + action_predict(sentence, self.botname))
def send_recieve():
control = {'status': None, 'text': None, 'blue_state': 0, 'red_state': 0}
if request.method == 'GET':
control['status'] = 1
data = request.args.get('text')
intent = action_predict(str(data).lower(), 'bulb')
ent = ner_predict(str(data).lower(), 'bulb')
entities = []
for i in ent:
entities.append(i[1])
log(entities)
arr = []
if intent == 'ON':
arr.append("Intent recognised : ON")
if 'blue' in entities:
arr.append("Entity recognised : Blue light")
control['blue_state'] = 1
if 'red' in entities:
arr.append("Entity recognised : Red light")
control['red_state'] = 1
elif 'lights' in entities:
arr.append("Entity recognised : Blue light and Red light")
control['red_state'] = 1
control['blue_state'] = 1
elif intent == 'OFF':
arr.append("Intent recognised : OFF")
if 'blue' in entities:
arr.append("Entity recognised : Blue light")
control['blue_state'] = 2
elif 'red' in entities:
arr.append("Entity recognised : Red light")
control['red_state'] = 2
elif 'lights' in entities:
arr.append("Entity recognised : Lights")
control['red_state'] = 2
control['blue_state'] = 2
control["text"] = arr
control = json.dumps(control)
return control
from ghee.ner import ner_predict
from ghee.action import action_predict
print(ner_predict('turn on all lights', 'bulb'))
print(action_predict('turn off lights', 'bulb'))