class WhatsappBotServer:
"""
A sample server class for Whatsapp integration with any MindMeld application
"""
def __init__(self, name, app_path, nlp=None):
"""
Args:
name (str): The name of the server.
app_path (str): The path of the MindMeld application.
nlp (NaturalLanguageProcessor): MindMeld NLP component, will try to load from app path
if None.
"""
self.app = Flask(name)
if not nlp:
self.nlp = NaturalLanguageProcessor(app_path)
self.nlp.load()
else:
self.nlp = nlp
self.conv = Conversation(nlp=self.nlp, app_path=app_path)
self.logger = logging.getLogger(__name__)
@self.app.route("/", methods=["POST"])
def handle_message(): # pylint: disable=unused-variable
incoming_msg = request.values.get('Body', '').lower()
resp = MessagingResponse()
msg = resp.message()
response_text = self.conv.say(incoming_msg)[0]
msg.body(response_text)
return str(resp)
def run(self, host="localhost", port=7150):
self.app.run(host=host, port=port)
def test_model_accuracies_are_similar_before_and_after_caching(home_assistant_app_path):
# clear model cache
model_cache_path = MODEL_CACHE_PATH.format(app_path=home_assistant_app_path)
try:
shutil.rmtree(MODEL_CACHE_PATH.format(app_path=home_assistant_app_path))
except FileNotFoundError:
pass
# Make sure no cache exists
assert os.path.exists(model_cache_path) is False
nlp = NaturalLanguageProcessor(home_assistant_app_path)
nlp.build(incremental=True)
nlp.dump()
entity_eval = (
nlp.domains["times_and_dates"]
.intents["change_alarm"]
.entity_recognizer.evaluate()
)
role_eval = (
nlp.domains["times_and_dates"]
.intents["change_alarm"]
.entities["sys_time"]
.role_classifier.evaluate()
)
entity_accuracy_no_cache = entity_eval.get_accuracy()
role_accuracy_no_cache = role_eval.get_accuracy()
example_cache = os.listdir(
MODEL_CACHE_PATH.format(app_path=home_assistant_app_path)
)[0]
nlp = NaturalLanguageProcessor(home_assistant_app_path)
nlp.load(example_cache)
# make sure cache exists
assert os.path.exists(model_cache_path) is True
entity_eval = (
nlp.domains["times_and_dates"]
.intents["change_alarm"]
.entity_recognizer.evaluate()
)
role_eval = (
nlp.domains["times_and_dates"]
.intents["change_alarm"]
.entities["sys_time"]
.role_classifier.evaluate()
)
entity_accuracy_cached = entity_eval.get_accuracy()
role_accuracy_cached = role_eval.get_accuracy()
assert role_accuracy_no_cache == role_accuracy_cached
assert entity_accuracy_no_cache == entity_accuracy_cached
def test_model_cache_files_present_after_second_incremental_build(
kwik_e_mart_app_path):
# clear model cache
model_cache_path = MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path)
try:
shutil.rmtree(MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path))
except FileNotFoundError:
pass
# Make sure no cache exists
assert os.path.exists(model_cache_path) is False
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.build(incremental=True)
nlp.dump()
initial_timestamp = nlp.incremental_timestamp
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.build(incremental=True)
nlp.dump()
new_timestamp = nlp.incremental_timestamp
nlp.load(initial_timestamp)
nlp.load(new_timestamp)
def test_model_accuracies_are_similar_before_and_after_caching(
kwik_e_mart_app_path):
# clear model cache
model_cache_path = MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path)
try:
shutil.rmtree(MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path))
except FileNotFoundError:
pass
# Make sure no cache exists
assert os.path.exists(model_cache_path) is False
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.build(incremental=True)
nlp.dump()
intent_eval = nlp.domains["store_info"].intent_classifier.evaluate()
entity_eval = (nlp.domains["store_info"].intents["get_store_hours"].
entity_recognizer.evaluate())
intent_accuracy_no_cache = intent_eval.get_accuracy()
entity_accuracy_no_cache = entity_eval.get_accuracy()
example_cache = os.listdir(
MODEL_CACHE_PATH.format(app_path=kwik_e_mart_app_path))[0]
nlp = NaturalLanguageProcessor(kwik_e_mart_app_path)
nlp.load(example_cache)
# make sure cache exists
assert os.path.exists(model_cache_path) is True
intent_eval = nlp.domains["store_info"].intent_classifier.evaluate()
entity_eval = (nlp.domains["store_info"].intents["get_store_hours"].
entity_recognizer.evaluate())
intent_accuracy_cached = intent_eval.get_accuracy()
entity_accuracy_cached = entity_eval.get_accuracy()
assert intent_accuracy_no_cache == intent_accuracy_cached
assert entity_accuracy_no_cache == entity_accuracy_cached
class WhatsappBotServer:
def __init__(self, name, app_path, nlp=None):
"""
Args:
name (str): The name of the server.
app_path (str): The path of the MindMeld application.
nlp (NaturalLanguageProcessor): MindMeld NLP component, will try to load from app path
if None.
"""
self.firebase = firebaseHelper()
self.app = Flask(name)
if not nlp:
self.nlp = NaturalLanguageProcessor(app_path)
self.nlp.load()
else:
self.nlp = nlp
self.conv = Conversation(nlp=self.nlp, app_path=app_path)
self.logger = logging.getLogger(__name__)
self.url = None
@self.app.route("/", methods=["POST"])
def handle_message(): # pylint: disable=unused-variable
# print(request.values)
# Getting number from which message came
id = request.values.get('From', '')
id = id.split('+')[1]
# print(request.values) #uncomment this to dif deeper
exist = self.firebase.existID(id)
if not exist:
result = self.firebase.createID(id)
incoming_msg = request.values.get('Body', '').lower()
location = {
'Latitude': request.values.get('Latitude', ''),
'Longitude': request.values.get('Longitude', '')
}
if request.values.get('Latitude', '') and request.values.get('Longitude', ''):
intent = l_t.getIntent()
print(intent)
result = self.firebase.setCurrLocation(location, id)
resp = MessagingResponse()
msg = resp.message()
params = dict(dynamic_resource=dict(id=id))
if intent == 'loc_for_source':
incoming_msg = "source for location"
elif intent == 'loc_for_hotel':
incoming_msg = "location for hotel"
elif intent == 'loc_for_food':
incoming_msg = "location for food"
else:
incoming_msg = "general location"
try:
response_text = self.conv.say(incoming_msg, params=params)[0]
messages = response_text.split("~")
for msg in messages:
if msg:
sendMessage(msg, id)
except IndexError:
msg.body("Didn't understand. sorry")
else:
resp = MessagingResponse()
msg = resp.message()
# Used to send dynamic id of the user making query
params = None
if nth.getTarget() == None :
params = dict(dynamic_resource =dict(id=id)) #Used to send dynamic id of the user making query
else:
params = dict(dynamic_resource =dict(id=id),target_dialogue_state=nth.getTarget())
try:
response_text = self.conv.say(incoming_msg, params=params)[0]
messages = response_text.split("~")
for msg in messages:
if msg:
sendMessage(msg, id)
except IndexError:
msg.body("Didn't understand. sorry")
return str(resp)
def sendMessage(msg, number):
# Change the from whatsapp number with your twilio account number
valid=validators.url(msg)
if valid :
self.url = msg
else:
if self.url:
client.messages.create(body=msg, from_="whatsapp:+14155238886", to="whatsapp:+"+str(number), media_url=[self.url])
self.url = None
else:
client.messages.create(body=msg, from_="whatsapp:+14155238886", to="whatsapp:+"+str(number))
def run(self, host="localhost", port=7150):
self.app.run(host=host, port=port)
def start_remainder(self):
remainder_service = remainderHelper(self.firebase)
remainder_service.start(self.firebase.getReminders())
*Assumes models have already been built (loads stored model)
"""
import argparse
from mindmeld.components import NaturalLanguageProcessor
parser = argparse.ArgumentParser()
parser.add_argument(dest="input_file",
type=str,
help="Please provide input file path")
args = parser.parse_args()
input_file = args.input_file
nlp = NaturalLanguageProcessor(
'../mindmeld-blueprints/blueprints/video_discovery')
nlp.load()
with open(input_file, 'r') as queries:
with open('pre_annotate.txt', 'w+') as predictions:
for query in queries:
query = query.rstrip()
prediction = nlp.domains["video_content"]._children["browse"]\
.entity_recognizer.predict(query)
new_string = ''
counter = 0
for entity in prediction:
entity_text = entity.text
entity_start = entity.span.start
entity_end = entity.span.end
entity_type = entity.entity.type
new_string += query[counter:entity_start]