def load_chatbot(self, user_id):
self.chatbot_instance_path = chatbot_instance_dir_path + '/' + str(
user_id)
self.chatbot_instance_path += '.cbinstance'
self.chatbot = Chatbot()
self.chatbot.load(self.chatbot_instance_path)
def __init__(self):
# 提前加载各种模型
self.classify = Classify()
self.recall = Recall()
self.sort = DnnSort()
self.chatbot = Chatbot()
self.message_manager = MessageManager()
def start_chatbot():
print("Starting the chatbot")
chatbot_resource_filename = "wechat_chatbot_resource.json"
local_chatbot = Chatbot()
local_chatbot.start_bot(
chatbot_resource_filename,
backend_read=False) # Turn off backend read cuz no SQL to read
return local_chatbot
class Chatbot_rest(Resource):
def __init__(self):
self.chatbot_instance_path = ''
pass
def __del__(self):
pass
def get(self, user_id):
user_id, query = user_id.split('_')
print(query)
self.load_chatbot(user_id)
chatbot = self.chatbot
print("receive : " + str(query))
result = chatbot.talk(query)
print("response : " + str(result))
self.save_chatbot()
return result
def post(self, user_id):
self.load_chatbot(user_id)
msg_from_user = dict(request.get_json(force=True))
print("receive : " + str(msg_from_user))
msg = msg_from_user['message']['text']
msg_to_user = self.chatbot.talk(msg)
msg_to_user['code'] = 200
msg_to_user['parameter'] = {}
print("response : " + str(msg_to_user))
self.save_chatbot()
return msg_to_user
def load_chatbot(self, user_id):
self.chatbot_instance_path = chatbot_instance_dir_path + '/' + str(
user_id)
self.chatbot_instance_path += '.cbinstance'
self.chatbot = Chatbot()
self.chatbot.load(self.chatbot_instance_path)
def save_chatbot(self):
self.chatbot.save(self.chatbot_instance_path)
def _load_model(self):
model_path = os.path.join(DEEPQA_PATH, 'save')
saved_models = os.listdir(model_path)
saved_models = [
saved_model.replace("model-", "") for saved_model in saved_models
if not saved_model.startswith(".")
and os.path.isdir(os.path.join(model_path, saved_model))
]
self._logger.info('TensorFlow detected: v{}'.format(tf.__version__))
for saved_model in saved_models:
self._logger.info("loading model {}".format(saved_model))
model = Chatbot()
try:
self._init_deep_qa_bot(model,
args=[
"--modelTag", saved_model,
"--keepAll", "--test", "daemon",
"--rootDir", DEEPQA_PATH
])
self._deepqa_models.append(model)
if self._max_length > model.args.maxLength:
self._max_length = model.args.maxLength
except Exception as e:
self._logger.warning(
"failed to load model {}".format(saved_model))
self._logger.error(e)
self._logger.info("loaded model {}".format(
[model.args.modelTag for model in self._deepqa_models]))
def run(self):
for server_name in config.sections():
# Changing the log level to the level specified in the config file
logger.setLevel(
logging.getLevelName(config[server_name]["log_level"]))
address = config[server_name]["address"]
user = config[server_name]["username"]
password = config[server_name]["password"]
game_password = config[server_name]["game_password"]
motd_scoreboard = str_to_bool(
config[server_name]["motd_scoreboard"])
scoreboard_type = config[server_name]["scoreboard_type"]
level_threshhold = config[server_name]["level_threshhold"]
enable_greeter = str_to_bool(config[server_name]["enable_greeter"])
max_players = config[server_name]["max_players"]
server = Server(server_name, address, user, password,
game_password, max_players, level_threshhold)
self.servers.append(server)
if motd_scoreboard:
motd_updater = MotdUpdater(server, scoreboard_type)
motd_updater.start()
self.motd_updaters.append(motd_updater)
cb = Chatbot(server, greeter_enabled=enable_greeter)
server.chat.add_listener(cb)
self.chatbots.append(cb)
print("Initialisation complete")
def get_chatbot():
lemmatizer = Lemmatizer()
text_processor = TextProcessor(
tokenizer,
stemmer,
joblib.load(config['vectorizer']),
lemmatizer,
Parser.get_instance(),
tagged_words_corpus)
phrase_extractor = PhraseExtractor(text_processor)
svo_extractor = SvoExtractor(text_processor, phrase_extractor)
sentence_scorer = SentenceScorer(text_processor, phrase_extractor)
entity_extractor = EntityExtractor(text_processor)
topic_classifier = TopicClassifierLocal(text_processor)
'''
summarizer = Summarizer(
text_processor,
sentence_scorer
)
'''
'''
summarizer = HttpSummarizer(
text_processor,
sentence_scorer,
summarization_service)
'''
summarizer = MultiProcessSummarizer(
text_processor,
sentence_scorer,
SummarizationTask,
Consumer)
data_manager = DataManager(
text_processor,
neo4j_service,
wikipedia_service,
Parser.get_instance(),
svo_extractor,
summarizer)
chatbot = Chatbot(
text_processor,
topic_classifier,
data_manager,
entity_extractor)
return chatbot
def initBot():
""" Instantiate the chatbot for later use
Should be called only once
"""
if not ChatbotManager.bot:
logger.info('Initializing bot...')
ChatbotManager.bot = Chatbot() #chatbot.Chatbot()
# need to find the chatbotPath for model and data
ChatbotManager.bot.main(
['--modelTag', 'server', '--test', 'daemon', '--rootDir', ''])
else:
logger.info('Bot already initialized.')
class ChatServicer(chatbot_pb2_grpc.ChatBotServiceServicer):
def __init__(self):
# 提前加载各种模型
self.classify = Classify()
self.recall = Recall()
self.sort = DnnSort()
self.chatbot = Chatbot()
self.message_manager = MessageManager()
def Chatbot(self, request, context):
user_id = request.user_id
message = request.user_message
create_time = request.create_time
attention, prob = self.classify.predict(message)
if attention == "QA":
# 实现对对话数据的保存
self.message_manager.user_message_pipeline(
user_id,
message,
create_time,
attention,
entity=message["entity"])
recall_list, entity = self.recall.predict(message)
user_response = self.sort.predict(message, recall_list)
else:
# 实现对对话数据的保存
self.message_manager.user_message_pipeline(
user_id,
message,
create_time,
attention,
entity=message["entity"])
user_response = self.chatbot.predict(message)
self.message_manager.bot_message_pipeline(user_id, user_response)
create_time = int(time.time())
return chatbot_pb2.ResponsedMessage(user_response=user_response,
create_time=create_time)
def run(self):
for server_name in settings.sections():
server = Server(server_name,
settings.setting(server_name, "address"),
settings.setting(server_name, "username"),
settings.setting(server_name, "password"))
server.game_password = \
settings.setting(server_name, "game_password")
server.url_extras = \
settings.setting(server_name, "url_extras")
dosh_threshold = int(
settings.setting(server_name, "dosh_threshold"))
if dosh_threshold > 0:
server.dosh_threshold = dosh_threshold
has_motd_scoreboard = str_to_bool(
settings.setting(server_name, "motd_scoreboard"))
if has_motd_scoreboard:
scoreboard_type = settings.setting(server_name,
"scoreboard_type")
MotdUpdater(server, scoreboard_type).start()
self.servers.append(server)
Chatbot(
server,
str_to_bool(settings.setting(server_name, "enable_greeter")),
settings.setting(server_name, "username"))
info("Initialisation complete!\n")
while True:
command = input()
for server in self.servers:
server.web_admin.chat.submit_message(command)
def make_chatbot(self, name, server):
chatbot = Chatbot(
server.web_admin.chat,
server_name=server.name, name=name
)
scheduler = CommandScheduler(server, chatbot)
commands = CommandMap().get_commands(
server, chatbot, scheduler, MotdUpdater(server)
)
for name, command in commands.items():
chatbot.add_command(name, command)
server.web_admin.chat.add_listener(chatbot)
server.web_admin.chat.add_listener(scheduler)
self.stop_list.append(scheduler)
chatbot.run_init()
return chatbot
def init_chatbot():
bot = Chatbot()
bot.start()
return bot
#!/usr/bin/env python3
import os
import time
import pyperclip
import urllib.request
print("Iniciando chatbot..")
from chatbot.chatbot import Chatbot
chatbot = Chatbot()
chatbot.main([
"--rootDir", ".", "--test", "daemon", "--modelTag", "spanish", "--keepAll"
])
from vision import look
from ui import say
recent_value = ""
while True:
tmp_value = pyperclip.paste()
if tmp_value != recent_value:
recent_value = tmp_value
if "http" in recent_value:
if "png" in recent_value or \
"jpg" in recent_value or \
"jpeg" in recent_value:
print("Link: %s" % str(recent_value))
filename = None
from flask import Flask, render_template
from flask import request
from chatbot.chatbot import Chatbot
bot = Chatbot()
app = Flask(__name__)
@app.route("/")
def home():
return render_template("index.html")
@app.route('/ask', methods=['GET', 'POST'])
def ask():
question = request.form["question"]
answer = bot.answer(question)
return question + "</>" + answer
if __name__ == "__main__":
app.run()
from chatbot.chatbot import Chatbot
bot = Chatbot()
while True:
print("Response: ", bot.answer(input()))
ws.fit(line.strip().split())
ws.build_vocab()
print(len(ws))
pickle.dump(ws, open(config.chatbot_ws_by_word_input_path, "wb"))
ws = Word_sequence()
for line in open(config.chatbot_target_path, "r",
encoding="utf-8").readlines():
ws.fit(line.strip().split())
ws.build_vocab()
print(len(ws))
pickle.dump(ws, open(config.chatbot_ws_by_word_target_path, "wb"))
def train_seq2seq():
for i in range(10):
train(i)
if __name__ == '__main__':
# save_ws()
# for idx,(input,target,input_length,target_length) in enumerate(dataloader):
# print(input)
# print(target)
# print(input_length)
# print(target_length)
# break
# train_seq2seq()
chatbot = Chatbot()
while True:
print(chatbot.predict(input("请输入：")))
from chatbot.chatbot import Chatbot
bot = Chatbot()
bot.train("./conversation_data/questions.txt")
bot.answer("Cache")
