import os
import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ClueAI/ChatYuan-large-v2")
model = T5ForConditionalGeneration.from_pretrained("ClueAI/ChatYuan-large-v2")
# 使用
device='cpu'
def preprocess(text):
text = text.replace("\n", "\\n").replace("\t", "\\t")
return text
def postprocess(text):
return text.replace("\\n", "\n").replace("\\t", "\t").replace('%20',' ')
def answer(text, sample=True, top_p=1, temperature=0.7):
'''sample：是否抽样。生成任务，可以设置为True;
top_p：0-1之间，生成的内容越多样'''
text = preprocess(text)
encoding = tokenizer(text=[text], truncation=True, padding=True, max_length=768, return_tensors="pt").to(device)
if not sample:
out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=512, num_beams=1, length_penalty=0.6)
else:
out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=512, do_sample=True, top_p=top_p, temperature=temperature, no_repeat_ngram_size=3)
out_text = tokenizer.batch_decode(out["sequences"], skip_special_tokens=True)
return postprocess(out_text[0])
def clear_session():
return '', None
def chatyuan_bot(input, history):
history = history or []
if len(history) > 5:
history = history[-5:]
context = "\n".join([f"用户：{input_text}\n小元：{answer_text}" for input_text, answer_text in history])
print(context)
input_text = context + "\n用户：" + input + "\n小元："
output_text = answer(input_text)
history.append((input, output_text))
print(history)
return history, history
block = gr.Blocks()
with block as demo:
gr.Markdown("""元语智能——ChatYuan
""")
chatbot = gr.Chatbot(label='ChatYuan')
message = gr.Textbox()
state = gr.State()
message.submit(chatyuan_bot, inputs=[message, state], outputs=[chatbot, state])
with gr.Row():
clear_history = gr.Button("👋 清除历史对话")
clear = gr.Button('🧹 清除发送框')
send = gr.Button("🚀 发送")
send.click(chatyuan_bot, inputs=[message, state], outputs=[chatbot, state])
clear.click(lambda: None, None, message, queue=False)
clear_history.click(fn=clear_session , inputs=[], outputs=[chatbot, state], queue=False)
def ChatYuan(api_key, text_prompt):
cl = clueai.Client(api_key,
check_api_key=True)
# generate a prediction for a prompt
# 需要返回得分的话，指定return_likelihoods="GENERATION"
prediction = cl.generate(model_name='ChatYuan-large', prompt=text_prompt)
# print the predicted text
print('prediction: {}'.format(prediction.generations[0].text))
response = prediction.generations[0].text
if response == '':
response = "很抱歉，我无法回答这个问题"
return response
def chatyuan_bot_api(api_key, input, history):
history = history or []
if len(history) > 5:
history = history[-5:]
context = "\n".join([f"用户：{input_text}\n小元：{answer_text}" for input_text, answer_text in history])
print(context)
input_text = context + "\n用户：" + input + "\n小元："
output_text = ChatYuan(api_key, input_text)
history.append((input, output_text))
print(history)
return history, history
block = gr.Blocks()
with block as demo_1:
gr.Markdown("""元语智能——ChatYuan
在使用此功能前，你需要有个API key. API key 可以通过这个平台获取
""")
api_key = gr.inputs.Textbox(label="请输入你的api-key(必填)", default="", type='password')
chatbot = gr.Chatbot(label='ChatYuan')
message = gr.Textbox()
state = gr.State()
message.submit(chatyuan_bot_api, inputs=[message, state], outputs=[chatbot, state])
with gr.Row():
clear_history = gr.Button("👋 清除历史对话")
clear = gr.Button('🧹 清除发送框')
send = gr.Button("🚀 发送")
send.click(chatyuan_bot_api, inputs=[message, state], outputs=[chatbot, state])
clear.click(lambda: None, None, message, queue=False)
clear_history.click(fn=clear_session , inputs=[], outputs=[chatbot, state], queue=False)
block = gr.Blocks()
with block as introduction:
gr.Markdown("""元语智能——ChatYuan
😉ChatYuan: 元语功能型对话大模型
👏这个模型可以用于问答、结合上下文做对话、做各种生成任务, 包括创意性写作, 也能回答一些像法律、新冠等领域问题. 它基于PromptCLUE-large结合数亿条功能对话多轮对话数据进一步训练得到.
👀PromptCLUE-large在1000亿token中文语料上预训练, 累计学习1.5万亿中文token, 并且在数百种任务上进行Prompt任务式训练. 针对理解类任务, 如分类、情感分析、抽取等, 可以自定义标签体系; 针对多种生成任务, 可以进行采样自由生成.
🚀在线Demo | ModelScope | Huggingface | 官网体验场 | ChatYuan-API | Github项目地址 | OpenI免费试用
""")
gui = gr.TabbedInterface(interface_list=[introduction,demo, demo_1], tab_names=["相关介绍","开源模型", "API调用"])
gui.launch(quiet=True,show_api=False, share = False)