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kplug / demo_chatbot_jddc.py

xusong28

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d0547d2 8 months ago

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	# coding=utf-8
	# author: xusong <xusong28@jd.com>
	# time: 2022/9/05 14:12

	"""
	TODO: 还要能判断是否需要回复。
	"""

	import torch
	import gradio as gr
	from info import article
	from kplug import modeling_kplug_s2s_patch
	from transformers import BertTokenizer, BartForConditionalGeneration

	model = BartForConditionalGeneration.from_pretrained("eson/kplug-base-jddc")
	tokenizer = BertTokenizer.from_pretrained("eson/kplug-base-jddc")


	def predict(input, history=[]):
	"""
	拼接方案：直接拼接history作为输入，不区分角色。虽然简单粗糙，但是encoder-decoder架构不会混淆输入和输出（如果是gpt架构就需要区分角色了）。
	"""
	# append the new user input tokens to the chat history
	history = history + [input] # history如果包含错误的response，可能会造成误差传递

	# tokenize the new input sentence
	bot_input_ids = tokenizer.encode("".join(history)[-500:], return_tensors='pt')

	# bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)

	# generate a response
	response = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()

	# convert the tokens to text, and then split the responses into lines
	response = "".join(tokenizer.decode(response[0], skip_special_tokens=True).split())
	history = history + [response]
	response = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)] # convert to tuples of list
	return response, history


	jddc_examples = [
	# 价保
	"昨天刚买的怎么就降了几十块，应该补给我差价吧",

	"请问这个猕猴桃是有货的吗?",
	# 到货时间
	"我下的这个单怎么还没到",
	# 快递
	"发什么快递",
	"能发邮政吗",
	]

	jddc_iface = gr.Interface(
	fn=predict,
	# inputs=["text", "state"],
	inputs=[
	gr.Textbox(
	label="输入文本",
	value="发什么快递"), # gr.State() 报错
	"state"
	],
	outputs=["chatbot", "state"],
	examples=jddc_examples,
	title="电商客服-生成式对话（Response Generation）",
	article=article,
	)

	if __name__ == "__main__":
	jddc_iface.launch()