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chat_bot.py
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chat_bot.py
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import re
from revChatGPT.V3 import Chatbot
from transformers import pipeline
import time
from text2vec import SentenceModel
from googlesearch import search
from cxr.prompt import prob2text,prob2text_zh
from r2g.report_generate import reportGen
from cxr.diagnosis import getJFImg,JFinfer,JFinit
import json
# from query import query_msd, query_prompt
from engine_LLM.api import answer_quest, query_range
from modality_identify import ModalityClip
# from dental.diagnosis import PAN,Score2txt
class MyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, list) and all(isinstance(x, int) for x in obj):
return obj
return super().default(obj)
fivedisease_zh={
"心脏肥大":0,
"肺水肿":1,
"肺实变":2,
"肺不张":3,
"胸腔积液":4,
}
fivedisease={
"Cardiomegaly":0,
"Edema":1,
"Consolidation":2,
"Atelectasis":3,
"Pleural Effusion":4,
}
chest_base_dict={"肺不张":0,
# "心脏肥大":1, 仅是现象，有好几种可能疾病，需要医生才能判断
"胸腔积液":2,
# "肺浸润":3, 仅是现象，有好几种可能疾病，需要医生才能判断
"纵隔肿块":4,
# "肺结节":5, 默沙东上仅定义了《孤立性肺结节》
# "肺炎":6,
"吸入性肺炎":6.1,"社区获得性肺炎":6.2,"医院获得性肺炎":6.3,"呼吸机相关性肺炎":6.4,"免疫缺陷患者肺炎":6.5,
"气胸":7,
# "肺实变":8, 仅是现象，有好几种可能疾病，需要医生才能判断
"肺水肿":9,
# "肺气肿":10, # 是慢性阻塞性肺病的一种，默沙东上仅对慢性阻塞性肺病进行了定义
"慢性阻塞性肺病（COPD）":10,
"胸膜纤维化和钙化":11,
# "胸膜增厚":12,
"膈疝":13,
"牙周炎":14,
"牙龈炎":15,
"龋齿":16,
"牙髓炎":17
}
class base_bot:
def start(self):
"""为当前会话新建chatbot"""
pass
def reset(self):
"""删除当前会话的chatbot"""
pass
def chat(self,message: str):
pass
class gpt_bot(base_bot):
def __init__(self, engine: str,api_key: str,):
"""初始化模型"""
self.agent=None
self.engine=engine
self.api_key=api_key
img_model,imgcfg=JFinit('./cxr/config/JF.json','./weights/JFchexpert.pth')
self.imgcfg=imgcfg
self.img_model=img_model
self.reporter=reportGen()
self.modality=["chest x-ray", "panoramic dental x-ray", "knee mri","Mammography"]
self.translator = pipeline(model="zhaozh/radiology-report-en-zh-ft-base",device=0,max_length=500,truncation=True)
self.identifier=ModalityClip(self.modality)
# self.dental_net = PAN(dental_config)
self.sent_model = SentenceModel()
self.msd_dict=json.load(open('./engine_LLM/dataset/msd_dict.json','r',encoding='utf-8'))
def ret_local(self,query:str,mode=1):
topic_range=query_range(self.sent_model,query,k=1,bar=0.0)
# Chinese
if mode==0:
return "https://"+self.msd_dict[topic_range[0]]
# English
else:
return "https://"+self.msd_dict[topic_range[0]].replace('www.msdmanuals.cn','www.merckmanuals.com')
# translate chest X-ray reports generated by r2g into Chinese.
def radio_en_zh(self, content: str):
output=self.translator(content)
report_zh=output[0]['generated_text']
return report_zh
def chat_with_gpt(self, prompt):
iteration=0
# message=self.agent.ask(prompt)
# return "I'm coming!"
while True:
iteration+=1
print(f"talking {iteration}......")
try:
message=self.agent.ask(prompt)
except:
time.sleep(10)
continue
break
return message
def start(self):
"""为当前会话新建chatbot"""
if self.agent is not None:
self.agent.reset()
system_prompt="You are ChatCAD-plus, a universal and reliable CAD system from ShanghaiTech. Respond conversationally"
self.agent = Chatbot(engine=self.engine,api_key=self.api_key,system_prompt=system_prompt,proxy="127.0.0.1:7890")
# self.agent = Chatbot(engine=self.engine,api_key=self.api_key)
instruction="Act as a doctor named ChatCAD-plus. Unless specified, all your answers should be in Chinese."
res=self.chat_with_gpt(instruction)
print(res)
return
# pass
def reset(self):
if self.agent is not None:
return
else:
self.agent.reset()
return
def end(self):
"""删除当前会话的chatbot"""
if self.agent is not None:
self.agent=None
else:
print("No remaining agent to be ended!")
def report_cxr_zh(self,img_path, mode:str='run'):
img1,img2=getJFImg(img_path,self.imgcfg)
text_report=self.reporter.report(img1)[0]
text_report=self.radio_en_zh(text_report)
prob=JFinfer(self.img_model,img2,self.imgcfg)
converter=prob2text_zh(prob,fivedisease_zh)
# default setting: promptB
res=converter.promptB()
prompt_report_zh=res+" 网络B生成了诊断报告："+text_report
awesomePrompt_zh="\nRefine the report of Network B based on results from Network A using Chinese.Please do not mention Network A and \
Network B. Suppose you are a doctor writing findings for a chest x-ray report."
prompt_report_zh=prompt_report_zh+awesomePrompt_zh
print("Refine CXR reports...")
refined_report = self.chat_with_gpt(prompt_report_zh)
if mode=='debug':
return text_report,refined_report,prob.detach().cpu().numpy().tolist()
else:
return refined_report
# def report_dental_zh(self,img_path):
# txt_prompt = Score2txt(self.dental_net.excution(img_path)).promptGeneration()
# return txt_prompt
def report_zh(self,img_path, mode:str='run'):
# identify modality
index=self.identifier.identify(img_path)
# call ModalitySpecificModel
if index==0:
return self.report_cxr_zh(img_path,mode),self.modality[index]
elif index==1:
# return self.report_dental_zh(img_path)
# The source code of the CAD network for dental x-rays is currently not planned to be open-sourced
# You can try it in the future online version of ChatCAD+.
return "Tooth CAD not publicly available!", self.modality[index]
elif index==2:
return "Knee CAD not publicly available!", self.modality[index]
else:
print("error!")
return
def chat(self,message: str, ref_record: str):
# check if it is a clinical-related input.
check_prompt="用户的以下输入是在针对医学问题进行提问吗？如果是，请回复1，如果不是或者不确定，请回复0，回答的长度为1，请不要有多余的回复。"
check_message=self.chat_with_gpt(f'{ref_record}{check_prompt}\n{message}')
# check_message=self.chat_with_gpt(check_prompt+'\n'+message)
print(f"check message: {check_message}")
numbers = re.findall(r'\d+', check_message)
assert len(numbers)==1
check=eval(numbers[0])
if check==0:
ans=self.chat_with_gpt(f"{ref_record}\nuser:**以中文回答**\n"+message)
return ans
refine_prompt="请根据以下内容概括患者的提问并对所涉及的疾病指出其全称：\n"
refined_message=self.chat_with_gpt(ref_record+'\n'+refine_prompt+message)
# refined_message=self.chat_with_gpt(refine_prompt+message)
topic_range=query_range(self.sent_model,refined_message,k=5)
if len(topic_range)==0:
response = self.chat_with_gpt(f"{ref_record}\nuser:**以中文回答**\n"+message)
response +="<br>注：未在默沙东数据库中得到明确依据，请谨慎采纳"
return response
refine_prompt="请根据以下内容概括患者的提问：\n"
refined_message=self.chat_with_gpt(ref_record+'\n'+refine_prompt+message)
# refined_message=self.chat_with_gpt(refine_prompt+message)
ret=answer_quest(refined_message,api_key=self.api_key,topic_base_dict=topic_range)
if ret==None:
# response = self.chat_with_gpt(refined_message)
# response = self.chat_with_gpt(message)
response = self.chat_with_gpt(f"{ref_record}\nuser:**以中文回答**\n"+message)
response +="<br>注：未在默沙东数据库中得到明确依据，请谨慎采纳"
message=response
else:
query,knowledge=ret
knowledge=knowledge.replace("\n\n","\n")
# needed_site=ret_website(query)
needed_site=self.ret_local(query,0)
try:
index = knowledge.index("：")
except ValueError:
index = -1
knowledge = knowledge[index+1:]
# chat_message=f"\n请参考以下知识来解答病人的问题“{message}”并给出分析，切记不要机械地重复以下知识：\n"+knowledge
chat_message=f"{ref_record}\nuser:**以中文回答**\n请参考以下知识来解答病人的问题“{message}”并给出分析，请注意保持语句通顺\n[{knowledge}]"
response = self.chat_with_gpt(chat_message)
print("ChatCAD+'s answer:\n")
print(response)
# message = response+f"<br><br>注：相关资料来自默沙东医疗手册专业版 <a href={needed_site}, class='underline',style='#c82423' >{query}</a>"
message= response+f"<br><br>注：相关资料来自默沙东医疗手册专业版 [{query}]({needed_site})"+""
return message
# TODO: Integrate with DoctorGLM https://github.com/xionghonglin/DoctorGLM
class glm_bot(base_bot):
def __init__(self, model_path):
"""初始化 GLM 模型"""
self.model_path = model_path
def start(self):
"""为当前会话新建chatbot"""
pass
def end(self):
"""删除当前会话的chatbot"""
pass
def chat(self,message: str):
pass