app.py

import os
import gradio as gr
import clueai
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("huolongguo10/HR_Chat")
model = T5ForConditionalGeneration.from_pretrained("huolongguo10/HR_Chat")
# 使用
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

base_info = "用户：你是谁？\n小元：我是huolongguo10助手智能助手。\n"
def preprocess(text):
  text = f"{base_info}{text}"
  text = text.replace("\n", "\\n").replace("\t", "\\t")
  return text

def postprocess(text):
  return text.replace("\\n", "\n").replace("\\t", "\t").replace('%20','  ')#.replace(" ", " ")



generate_config = {'do_sample': True, 'top_p': 0.9, 'top_k': 50, 'temperature': 0.9, 
                   'num_beams': 1, 'max_length': 1024, 'min_length': 3, 'no_repeat_ngram_size': 5, 
                   'length_penalty': 0.6, 'return_dict_in_generate': True, 'output_scores': True}
def answer(text, sample=True, top_p=0.9, temperature=0.9):
  '''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=1024, return_tensors="pt").to(device) 
  if not sample:
      out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=1024, num_beams=1, length_penalty=0.6)
  else:
      out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=1024, do_sample=True, top_p=top_p, temperature=temperature, no_repeat_ngram_size=12)
  #out=model.generate(**encoding, **generate_config)
  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："
    input_text = input_text.strip()
    output_text = answer(input_text)
    print("open_model".center(20, "="))
    print(f"{input_text}\n{output_text}")
    #print("="*20)
    history.append((input, output_text))
    #print(history)
    return history, history
def chatyuan_bot_regenerate(input, history):
    
    history = history or []
    
    if history:
      input=history[-1][0]
      history=history[:-1]
      
    
    if len(history) > 1:
       history = history[-1:]

    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："
    input_text = input_text.strip()
    output_text = answer(input_text)
    print("open_model".center(20, "="))
    print(f"{input_text}\n{output_text}")
    history.append((input, output_text))
    #print(history)
    return history, history
  
block = gr.Blocks()

with block as demo:
    gr.Markdown("""<h1><center>huolongguo10助手</center></h1>

    """)
    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 History")
      clear = gr.Button('🧹 清除发送框 | Clear Input')
      send = gr.Button("🚀 发送 | Send")
      regenerate = gr.Button("🚀 重新生成本次结果 | regenerate")


    regenerate.click(chatyuan_bot_regenerate, inputs=[message, state], outputs=[chatbot, state])      
    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："
    input_text = input_text.strip()
    output_text = ChatYuan(api_key, input_text)
    print("api".center(20, "="))
    print(f"api_key:{api_key}\n{input_text}\n{output_text}")
    #print("="*20)
    history.append((input, output_text))
    #print(history)
    return history, history



block = gr.Blocks()

with block as demo_1:
    gr.Markdown("""
    """)
    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=[api_key,message, state], outputs=[chatbot, state])
    with gr.Row():
      clear_history = gr.Button("👋 清除历史对话 | Clear Context")
      clear = gr.Button('🧹 清除发送框 | Clear Input')
      send = gr.Button("🚀 发送 | Send")

    send.click(chatyuan_bot_api, inputs=[api_key,message, state], outputs=[chatbot, state],api_name='send')
    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("""啥也没有
    """)


gui = gr.TabbedInterface(interface_list=[demo], tab_names=["开源模型"])
gui.launch(quiet=True,show_api=True, share = False)