from wxpy import *
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.utils import EndpointConfig
agent = Agent('w_domain.yml',
policies=[
MemoizationPolicy(),
KerasPolicy(max_history=3, epochs=200, batch_size=50)
])
data = agent.load_data('./data/stories.md')
agent.train(data)
interpreter = RasaNLUInterpreter('./models/nlu/default/trainedNlu')
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
agent = Agent.load('./models/dialogue',
interpreter=interpreter,
action_endpoint=action_endpoint)
bot = Bot(console_qr=False, cache_path=True)
@bot.register(bot.friends())
def reply_my_friend(msg):
ans = agent.handle_message(msg.text)
print(ans)
return ans[0]['text']
embed()
from rasa_core.channels import HttpInputChannel
from rasa_core.channels.facebook import FacebookInput
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
# load your trained agent
agent = Agent.load(
"models/dialogue",
interpreter=RasaNLUInterpreter(
model_directory=
"C:/Users/Administrator/Desktop/BotV3.0/models/nlu/current",
config_file="nlu_model_config.json"))
YOUR_FB_VERIFY = "rasa-bot"
YOUR_FB_SECRET = "a9f5370c907e14a983051bd4d266c47b"
YOUR_FB_PAGE_ID = "158943344706542"
YOUR_FB_PAGE_TOKEN = "EAACZAVkjEPR8BANiwfuKaSVz8yxtLsytuOPvaUzUTlCMAmvuX9TdqGR5P4F1EepBfZCQoKhSR49zM5C9pYX9hmmv3qqiUnRCMDE0eJ1lWRjeqNYTLLA5nbXelSMw0p7neZBSyyIcNHS3e1lbbf2raWPY8IUosJZBMlDLLA7ZBJgTxZAZCvhbO84"
input_channel = FacebookInput(
fb_verify=
YOUR_FB_VERIFY, # you need tell facebook this token, to confirm your URL
fb_secret=YOUR_FB_SECRET, # your app secret
fb_tokens={YOUR_FB_PAGE_ID:
YOUR_FB_PAGE_TOKEN}, # page ids + tokens you subscribed to
debug_mode=True # enable debug mode for underlying fb library
)
agent.handle_channel(HttpInputChannel(8080, "", input_channel))
def run_dialogue(serve_forever=True):
interpreter = RasaNLUInterpreter('./models/nlu/default/chat')
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
agent = Agent.load('./models/dialogue', interpreter=interpreter, action_endpoint=action_endpoint)
rasa_core.run.serve_application(agent, channel='cmdline')
return agent
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
logger = logging.getLogger(__name__)
def run_weather_online(input_channel, interpreter,
domain_file="domain.yml",
training_data_file='data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(), KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
max_history=2,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/doctornlu')
run_weather_online(ConsoleInputChannel(), nlu_interpreter)
from rasa_core.channels import HttpInputChannel
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_slack_connector import SlackInput
nlu_interpreter = RasaNLUInterpreter(
'./models/nlu/default/retailbot') ## model path
agent = Agent.load('./models/dialogue', interpreter=nlu_interpreter)
input_channel = SlackInput(
'xoxp-423894218069-423894219605-424790010167-542a4c4faab8353d53dfe2e1d3ae56af', #app verification token
'xoxb-423894218069-423901535829-mCl8rB6reWAonQpHCMXhsbB9', # bot verification token
'tmuWECAy59Qba6hbYzSJft11', # slack verification token
True)
agent.handle_channel(HttpInputChannel(5004, '/', input_channel))
import requests
import json
from flask import Flask
from flask import request
from flask import Response
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.utils import EndpointConfig
# load trained models
interpreter = RasaNLUInterpreter('./models/current/nlu')
agent = Agent.load('./models/current/dialogue', interpreter=interpreter,action_endpoint=EndpointConfig(url="0.0.0.0:5055/webhook"))
token = '739701752:AAEu1dcyxVeTNf8pgyN5FfMZx-2Te3PwprA'
# https://api.telegram.org/bot{token}/deleteWebhook
app = Flask(__name__)
# accept telegram messages
@app.route('/',methods=['POST','GET'])
def index():
if(request.method == 'POST'):
msg = request.get_json()
print(msg)
chat_id , message = parse_msg(msg) #message é a mensagem do usuario, chat_id do user
response_messages = applyAi(message)
send_message(chat_id,response_messages)
return Response('ok',status=200)
else:
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from ga_connector import GoogleConnector
from rasa_core.utils import EndpointConfig
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/chatter')
#generator = EndpointConfig(url="http://localhost:5055/nlg")
#agent = Agent.load('./models/dialogue', interpreter = nlu_interpreter, action_endpoint=action_endpoint , generator=generator)
agent = Agent.load('./models/dialogue', interpreter=nlu_interpreter, action_endpoint = action_endpoint)
input_channel = GoogleConnector()
agent.handle_channels([input_channel], 5004, serve_forever=True)
from rasa_core.channels.facebook import FacebookInput
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.utils import EndpointConfig
input_channel = FacebookInput(
fb_verify="ResBot",
# you need tell facebook this token, to confirm your URL
fb_secret="f12e71394b4b95e1ae348a8023b72855", # your app secret
fb_access_token=
"EAAGH6Mgz3VoBAPX7ZCaOvbkNi6Y2qhKJ5ZBycstxdpYnEMr3LfOiqBksrrVNZAZA0FbxwilAyEhJa34EgczzHk9ZCPgC0KjAxroGuxd43xQjtaqv7v7kon1BH3L1S1pgV5G0zBjZCc5F75AS9aLXHpi7vEMUTQnaoHZBZAMe0JyM3eKKsykWfxhL"
# token for the page you subscribed to
)
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/ResBot')
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
agent = Agent.load('./models/dialogue',
interpreter=nlu_interpreter,
action_endpoint=action_endpoint)
# set serve_forever=True if you want to keep the server running
s = agent.handle_channels([input_channel], 5004, serve_forever=True)
def __init__(self):
interpreter = RasaNLUInterpreter(
'./models/nlu/default/helloweather_model')
self.run_online_trainer(interpreter)
# Messenger Platform
from controllers.messenger_send_api import verify_webhook, respond
# Image
from controllers.image import predict_herb_image
# Utilities
import random
import os
# Initialize the Flask application
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = os.path.join(os.getcwd(), "api_images")
# NLU
rasa_nlu_interpreter_path = os.path.join(os.getcwd(), "models", "nlu",
"default", "current")
interpreter = RasaNLUInterpreter(rasa_nlu_interpreter_path)
rasa_nlu_dialogue_path = os.path.join(os.getcwd(), "models", "dialogue")
agent = Agent.load(rasa_nlu_dialogue_path, interpreter=interpreter)
@app.route('/')
def index():
return '<h1>Hello World<h1/>'
def get_message():
sample_responses = [
"อู้หยังนิ", "บ่ฮู้เรื่อง", "อู้ไปเรื่อย", "สวัสดีเจ้า", "ต่อนยอน"
]
# return selected item to the user
return random.choice(sample_responses)
def load_interpreter():
print("loading in interpreter")
interpreter = RasaNLUInterpreter(INTEPRETER_PATH)
return interpreter
#!/usr/bin/env python
#SENIVERSE_KEY=hjne3bys5ilki7yw python ./webchat.py
import os
import pypher
import json
import warnings
import ruamel.yaml as yaml
from pypher.builder import Param, Pypher
from neo4j.v1 import GraphDatabase
from rasa_core.interpreter import RasaNLUInterpreter
import sys
import argparse
interpret = RasaNLUInterpreter('./models/nlu/longbi')
extracted_intents = None
extracted_values = None # entities values
prediction = None
a_list=['过度依赖父母怎么办','总咬指甲怎么办','爱说谎怎么办','过度胆小怎么办','害怕分离怎么办','孩子经常夹腿怎么办','5岁了还尿床怎么办','多动不专心怎么办','不自主的挤眉弄眼怎么办','走路说话晚怎么办','不看人不理人怎么办','孩子易冲动、爱攻击别人怎么办','家长如何帮助孩子对应压力','家长如何帮助孩子理解和表达自己的感受']
for a in a_list:
extracted_entities = None
extracted_intents = None
prediction = interpret.parse(a)
print(a,':',prediction['intent'])
if len(prediction.get("entities")) > 0:
# extracted_entities = None
except ImportError:
import md5
md5_constructor = md5.new
md5_hmac = md5
import sha
sha_constructor = sha.new
sha_hmac = sha
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
sys.path.append('../MITIE/mitielib')
# from momo.helper import get_momo_answer # 导入获取机器人回答获取函数
nlu_model_path = '../models/default/model_20171117-181635'
agent = Agent.load("../models/policy/mom", interpreter=RasaNLUInterpreter(nlu_model_path))
# momo_chat = ChatBot(
# 'Momo',
# storage_adapter='chatterbot.storage.MongoDatabaseAdapter',
# logic_adapters=[
# "chatterbot.logic.BestMatch",
# "chatterbot.logic.MathematicalEvaluation",
# "chatterbot.logic.TimeLogicAdapter",
# ],
# input_adapter='chatterbot.input.VariableInputTypeAdapter',
# output_adapter='chatterbot.output.OutputAdapter',
# database='chatterbot',
# read_only=True
# )
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
logger = logging.getLogger(__name__)
def run_weather_online(input_channel,
interpreter,
domain_file="domain.yml",
training_data_file='data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=2),
KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/supportnlu')
run_weather_online(ConsoleInputChannel(), nlu_interpreter)
def collect_story_predictions(resource_name, policy_model_path, nlu_model_path,
max_stories):
"""Test the stories from a file, running them through the stored model."""
if nlu_model_path is not None:
interpreter = RasaNLUInterpreter(model_directory=nlu_model_path)
else:
interpreter = RegexInterpreter()
agent = Agent.load(policy_model_path, interpreter=interpreter)
story_graph = training.extract_story_graph(resource_name, agent.domain,
interpreter)
preds = []
actual = []
g = TrainingDataGenerator(story_graph,
agent.domain,
use_story_concatenation=False,
tracker_limit=max_stories)
completed_trackers = g.generate()
failed_stories = []
logger.info("Evaluating {} stories\nProgress:"
"".format(len(completed_trackers)))
for tracker in tqdm(completed_trackers):
sender_id = "default-" + uuid.uuid4().hex
story = {"predicted": [], "actual": []}
events = list(tracker.events)
actions_between_utterances = []
last_prediction = []
for i, event in enumerate(events[1:]):
if isinstance(event, UserUttered):
p, a = align_lists(last_prediction, actions_between_utterances)
story["predicted"].extend(p)
story["actual"].extend(a)
actions_between_utterances = []
agent.handle_message(event.text, sender_id=sender_id)
tracker = agent.tracker_store.retrieve(sender_id)
last_prediction = actions_since_last_utterance(tracker)
elif isinstance(event, ActionExecuted):
actions_between_utterances.append(event.action_name)
if last_prediction:
preds.extend(last_prediction)
preds_padding = (len(actions_between_utterances) -
len(last_prediction))
story["predicted"].extend(["None"] * preds_padding)
preds.extend(story["predicted"])
actual.extend(actions_between_utterances)
actual_padding = (len(last_prediction) -
len(actions_between_utterances))
story["actual"].extend(["None"] * actual_padding)
actual.extend(story["actual"])
if story["predicted"] != story["actual"]:
failed_stories.append(story)
return actual, preds, failed_stories
from rasa_core.train import online
from rasa_core.utils import EndpointConfig
logger = logging.getLogger(__name__)
def run_weather_online(interpreter,
domain_file="malu_domain.yml",
training_data_file='data/stories.md'):
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
core_threshold=0.01,
nlu_threshold=0.01)
agent = Agent(
domain_file,
policies=[MemoizationPolicy(max_history=2),
KerasPolicy(), fallback],
interpreter=interpreter,
action_endpoint=action_endpoint)
data = agent.load_data(training_data_file)
agent.train(data, batch_size=50, epochs=200, max_training_samples=300)
online.run_online_learning(agent)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/malu')
run_weather_online(nlu_interpreter)
from rasa_core.channels import HttpInputChannel
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_slack_connector import SlackInput
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/AIchatbotnlu')
agent = Agent.load('./models/dialogue', interpreter=nlu_interpreter)
input_channel = SlackInput(
'xoxp-xxxx', #app verification token
'xoxb-yyyy', # bot verification token
'zzzz', # slack verification token
True)
agent.handle_channel(HttpInputChannel(5004, '/', input_channel))
from __future__ import unicode_literals
import argparse
import logging
import warnings
from rasa_core.actions import Action
from rasa_core.agent import Agent
from rasa_core.channels.console import ConsoleInputChannel
from rasa_core.events import SlotSet
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.events import AllSlotsReset, Restarted
interpret1 = RasaNLUInterpreter('../fine_grained/models/nlu/longbi')
import search
import warnings
warnings.filterwarnings("ignore")
logger = logging.getLogger(__name__)
#from cocoNLP.extractor import extractor
class Performance(Action):
def name(self):
return 'action_query_Performance'
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
logger = logging.getLogger(__name__)
def run_faq_online(input_channel,
interpreter,
domain_file="faq_domain.yml",
training_data_file='data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
max_history=2,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/faq_bot')
run_faq_online(ConsoleInputChannel(), nlu_interpreter)
from rasa_core.channels.console import ConsoleInputChannel
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
logger = logging.getLogger(__name__)
def run_foodie_online(input_channel, interpreter,
domain_file="foodie_domain.yml",
training_data_file='data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(), KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
max_history=2,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/foodienlu')
run_foodie_online(ConsoleInputChannel(), nlu_interpreter)
from rasa_core.agent import Agent
from rasa_core.channels.console import ConsoleInputChannel
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
logger = logging.getLogger(__name__)
def run_restaurant_online(input_channel, interpreter,
domain_file="restaurant_domain.yml",
training_data_file='data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(), KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
max_history=2,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/restaurantnlu')
run_restaurant_online(ConsoleInputChannel(), nlu_interpreter)
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from ga_connector import GoogleConnector
from rasa_core.utils import EndpointConfig
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
nlu_interpreter = RasaNLUInterpreter(
'C:\\Users\\HP\\Desktop\\starter-pack-rasa-stack-master\\models\\current\\nlu_model'
)
agent = Agent.load('./models/current/dialogue',
interpreter=nlu_interpreter,
action_endpoint=action_endpoint)
input_channel = GoogleConnector()
agent.handle_channels([input_channel], 5004, serve_forever=True)
from rasa_core.channels import HttpInputChannel
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_slack_connector import SlackInput
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/Homer_nlu')
agent = Agent.load('./models/dialogue', interpreter=nlu_interpreter)
input_channel = SlackInput()
agent.handle_channel(HttpInputChannel(5005, '/', input_channel))
from rasa_core.channels.console import ConsoleInputChannel
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
logger = logging.getLogger(__name__)
def run_online(input_channel, interpreter,
domain_file="domain.yml",
training_data_file='data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=5), KerasPolicy()],
interpreter=interpreter)
training_data = agent.load_data(training_data_file)
agent.train_online(training_data,
input_channel=input_channel,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/current')
run_online(ConsoleInputChannel(), nlu_interpreter)
from rasa_nlu.model import Metadata, Interpreter
from rasa_nlu import config
from rasa_nlu.model import Trainer
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
##run durectly
'''
python -m rasa_core.run -d nlu_dialogue/models/restaurant -u nlu_model/restaurant/default/model_20180620-224333
'''
model_directory = 'nlu_model/jarvis_nlu/default/current'
# interpreter = Interpreter.load(model_directory)
agent = Agent.load('nlu_dialogue/models/jarvis_nlu',
interpreter=RasaNLUInterpreter(model_directory))
# Create your views here.
def home(requests):
return render(requests, 'jarvis/home.html', {})
def task(requests):
req = requests.GET['query']
context = {}
### using NLU intent prediction
# intent = interpreter.parse(req)
# print(intent)
# context['message'] = intent
from rasa_core.agent import Agent
from rasa_core.utils import EndpointConfig
from rasa_core.interpreter import RasaNLUInterpreter
agent = Agent.load(
'models/dialogue',
interpreter=RasaNLUInterpreter('./models/nlu/default/restaurantbot/'),
action_endpoint=EndpointConfig(url="http://127.0.0.1:5055/webhook"))
print("Bot is ready to talk! Start conversation with 'hi'")
while True:
st = input()
if st == 'stop':
break
responses = agent.handle_text(st)
for response in responses:
print(response["text"])
from rasa_core.channels.console import ConsoleInputChannel
from rasa_core.interpreter import RegexInterpreter
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.policies.memoization import MemoizationPolicy
from rasa_core.interpreter import RasaNLUInterpreter
logger = logging.getLogger(__name__)
def run_weather_online(input_channel,
interpreter,
domain_file="weather_domain.yml",
training_data_file='data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=2),
KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
if __name__ == '__main__':
logging.basicConfig(level="INFO")
nlu_interpreter = RasaNLUInterpreter('./models/nlu/default/weathernlu')
run_weather_online(ConsoleInputChannel(), nlu_interpreter)
import os
from rasa_core.channels.rasa_chat import RasaChatInput
from rasa_core.channels.channel import CollectingOutputChannel, UserMessage
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
from rasa_core.utils import EndpointConfig
from rasa_core import utils
from flask import render_template, Blueprint, jsonify, request
# load your trained agent
interpreter = RasaNLUInterpreter("models/nlu/default/horoscopebot/")
MODEL_PATH = "models/dialogue"
action_endpoint = EndpointConfig(
url="https://horoscopebot1212-actions.herokuapp.com/webhook")
agent = Agent.load(MODEL_PATH,
interpreter=interpreter,
action_endpoint=action_endpoint)
class MyNewInput(RasaChatInput):
@classmethod
def name(cls):
return "rasa"
def _check_token(self, token):
if token == 'secret':
return {'username': 1234}
else:
def run_online_dialogue(serve_forever=True):
interpreter = RasaNLUInterpreter('./models/nlu/default/chat')
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
agent = Agent.load('./models/dialogue', interpreter=interpreter, action_endpoint=action_endpoint)
interactive.run_interactive_learning(agent)#, channel='cmdline')
return agent
from rasa_core.agent import Agent
from rasa_core.interpreter import RasaNLUInterpreter
import time
interpreter = RasaNLUInterpreter(
'/app/project/model/default/model_20190204-222950')
messages = [
"Hi! you can chat in this window. Type 'stop' to end the conversation."
]
agent = Agent.load('/app/project/model/dialogue', interpreter=interpreter)
def chatlogs_html(messages):
messages_html = "".join(["<p>{}</p>".format(m) for m in messages])
chatbot_html = """<div class="chat-window" {}</div>""".format(
messages_html)
return chatbot_html
while True:
print(chatlogs_html(messages))
time.sleep(0.3)
a = input()
messages.append(a)
if a == 'stop':
break
responses = agent.handle_message(a)
for r in responses:
messages.append(r.get("text"))
