def chat_fun_english(question, model, chat_settings, chatlog_filepath):
#Get the input and check if it is a question or a command, and execute if it is a command
#question = input("You: ")
is_command, terminate_chat, reload_model = chat_command_handler.handle_command(
question, model, chat_settings)
if is_command:
pass
else:
#If it is not a command (it is a question), pass it on to the chatbot model to get the answer
question_with_history, answer = model.chat(question, chat_settings)
#Print the answer or answer beams and log to chat log
if chat_settings.show_question_context:
print("Question with history (context): {0}".format(
question_with_history))
print("\n1st if")
if chat_settings.show_all_beams:
for i in range(len(answer)):
print("ChatBot (Beam {0}): {1}".format(i, answer[i]))
print("\n2nd if")
else:
print("ChatBot: {0}".format(answer))
#print("\n else")
print()
if chat_settings.inference_hparams.log_chat:
chat_command_handler.append_to_chatlog(chatlog_filepath, question,
answer)
return answer
def chat_fun_urdu(n_query, model, chat_settings, chatlog_filepath):
chk, response = wt.query_check(n_query)
terminate_chat = False
#Get the input and check if it is a question or a command, and execute if it is a command
#question = input("You: ")
question = n_query
is_command, terminate_chat, reload_model = chat_command_handler.handle_command(
question, model, chat_settings)
if is_command:
pass
elif chk:
return response
else:
question = ChatSettings.To_query(n_query)
#If it is not a command (it is a question), pass it on to the chatbot model to get the answer
question_with_history, answer = model.chat(question, chat_settings)
#Print the answer or answer beams and log to chat log
if chat_settings.show_question_context:
print("Question with history (context): {0}".format(
question_with_history))
if chat_settings.show_all_beams:
for i in range(len(answer)):
print("ChatBot (Beam {0}): {1}".format(i, answer[i]))
else:
n_answer = ChatSettings.To_answer(answer)
print("ChatBot: {0}".format(n_answer))
print()
return n_answer
if chat_settings.inference_hparams.log_chat:
chat_command_handler.append_to_chatlog(chatlog_filepath, question,
answer)
def chat_fun():
terminate_chat = False
reload_model = False
while not terminate_chat:
#Create the model
print()
print("Initializing model..."
if not reload_model else "Re-initializing model...")
print()
with ChatbotModel(mode="infer",
model_hparams=chat_settings.model_hparams,
input_vocabulary=input_vocabulary,
output_vocabulary=output_vocabulary,
model_dir=model_dir) as model:
#Load the weights
print()
print("Loading model weights...")
print()
model.load(checkpoint)
#Show the commands
if not reload_model:
chat_command_handler.print_commands()
while True:
#Get the input and check if it is a question or a command, and execute if it is a command
question = input("You: ")
is_command, terminate_chat, reload_model = chat_command_handler.handle_command(
question, model, chat_settings)
if terminate_chat or reload_model:
break
elif is_command:
continue
else:
#If it is not a command (it is a question), pass it on to the chatbot model to get the answer
question_with_history, answer = model.chat(
question, chat_settings)
#Print the answer or answer beams and log to chat log
if chat_settings.show_question_context:
print("Question with history (context): {0}".format(
question_with_history))
print("\n1st if")
if chat_settings.show_all_beams:
for i in range(len(answer)):
print("ChatBot (Beam {0}): {1}".format(
i, answer[i]))
print("\n2nd if")
else:
print("ChatBot: {0}".format(answer))
print("\n else")
print()
if chat_settings.inference_hparams.log_chat:
chat_command_handler.append_to_chatlog(
chatlog_filepath, question, answer)
def get(self, question):
is_command, terminate_chat, _ = chat_command_handler.handle_command(question, model, chat_settings)
if terminate_chat:
answer = "[Can't terminate from http request]"
elif is_command:
answer = "[Command processed]"
else:
#If it is not a command (it is a question), pass it on to the chatbot model to get the answer
_, answer = model.chat(question, chat_settings)
if chat_settings.inference_hparams.log_chat:
chat_command_handler.append_to_chatlog(chatlog_filepath, question, answer)
return answer
def chat(self, question):
is_command, terminate_chat = chat_command_handler.handle_command(
question, self.model, self.chat_settings)
if terminate_chat:
return "Terminate is not supported in wechat model."
elif not is_command:
# If it is not a command (it is a question), pass it on to the chatbot model to get the answer
question_with_history, answer = self.model.chat(
question, self.chat_settings)
# Print the answer or answer beams and log to chat log
if self.chat_settings.show_question_context:
return "Question with history (context): {0}".format(
question_with_history)
if self.chat_settings.show_all_beams:
for i in range(len(answer)):
return "ChatBot (Beam {0}): {1}".format(i, answer[i])
else:
return format(answer)
if self.chat_settings.inference_hparams.log_chat:
chat_command_handler.append_to_chatlog(self.chatlog_filepath,
question, answer)
#Load the weights
print()
print("Loading model weights...")
model.load(checkpoint)
# Setting up the chat
chatlog_filepath = path.join(
model_dir, "chat_logs", "chatlog_{0}.txt".format(
datetime.datetime.now().strftime("%Y%m%d_%H%M%S")))
chat_settings = ChatSettings(hparams.inference_hparams)
chat_command_handler.print_commands()
while (True):
#Get the input and check if it is a question or a command, and execute if it is a command
question = input("You: ")
is_command, terminate_chat = chat_command_handler.handle_command(
question, model, chat_settings)
if terminate_chat:
break
elif is_command:
continue
else:
#If it is not a command (it is a question), pass it on to the chatbot model to get the answer
question_with_history, answer = model.chat(question, chat_settings)
#Print the answer or answer beams and log to chat log
if chat_settings.show_question_context:
print("Question with history (context): {0}".format(
question_with_history))
if chat_settings.show_all_beams:
for i in range(len(answer)):
def chat_fun(n_query):
terminate_chat = False
reload_model = False
chk, response = wt.query_check(n_query)
while not terminate_chat:
#Create the model
print()
print("Initializing model..."
if not reload_model else "Re-initializing model...")
print()
with ChatbotModel(mode="infer",
model_hparams=chat_settings.model_hparams,
input_vocabulary=input_vocabulary,
output_vocabulary=output_vocabulary,
model_dir=model_dir) as model:
#Load the weights
print()
print("Loading model weights...")
print()
model.load(checkpoint)
#Show the commands
if not reload_model:
#Uncomment the following line if you want to print commands.
#chat_command_handler.print_commands()
print('Model Reload!')
while True:
#Get the input and check if it is a question or a command, and execute if it is a command
#question = input("You: ")
question = n_query
is_command, terminate_chat, reload_model = chat_command_handler.handle_command(
question, model, chat_settings)
if terminate_chat or reload_model:
break
elif is_command:
continue
elif chk:
return response
else:
question = ChatSettings.To_query(n_query)
#If it is not a command (it is a question), pass it on to the chatbot model to get the answer
question_with_history, answer = model.chat(
question, chat_settings)
#Print the answer or answer beams and log to chat log
if chat_settings.show_question_context:
print("Question with history (context): {0}".format(
question_with_history))
if chat_settings.show_all_beams:
for i in range(len(answer)):
print("ChatBot (Beam {0}): {1}".format(
i, answer[i]))
else:
n_answer = ChatSettings.To_answer(answer)
print("ChatBot: {0}".format(n_answer))
print()
return n_answer
if chat_settings.inference_hparams.log_chat:
chat_command_handler.append_to_chatlog(
chatlog_filepath, question, answer)