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import streamlit as st
import autogen
from autogen import OpenAIWrapper, AssistantAgent, UserProxyAgent
from typing import List
def setting_config(model_name: str) -> tuple[List[dict], dict]:
if model_name.startswith("gpt"):
config_list = [
{
"model": model_name,
"base_url": "https://api.openai.com/v1",
"api_key": st.secrets.openai,
}
]
else:
config_list = [
{
"model": model_name, # "google/gemma-2-9b-it",
"base_url": "https://integrate.api.nvidia.com/v1",
"api_key": st.secrets.nvidia,
"max_tokens": 1000,
}
]
llm_config={
"timeout": 500,
"seed": 42,
"config_list": config_list,
"temperature": 0.5,
}
return config_list, llm_config
class TrackableUserProxyAgent(UserProxyAgent):
t = 0
def _process_received_message(self, message, sender, silent):
global t
with st.chat_message(sender.name, avatar="streamlit_images/{}.png".format(self.t)):
st.write(f"**{message['name'].replace('_',' ')}**: {message['content']}")
self.t += 1
if self.t == 4:
self.t = 0
st.divider()
return super()._process_received_message(message, sender, silent)
def doctor_recruiter(prompt_recruiter_doctors: str, model_name: str) -> str:
config_list, llm_config = setting_config(model_name)
client = OpenAIWrapper(api_key=config_list[0]['api_key'], config_list=config_list)
response = client.create(messages=[{"role": "user", "content": prompt_recruiter_doctors + "\nReturn ONLY JSON file (don't use Markdown tags like: ```json)."}],
temperature=0.3,
seed=42,
model=config_list[0]['model'])
text = client.extract_text_or_completion_object(response)
return text
def doctor_discussion(doctor_description: str, prompt_internist_doctor: str, model_name: str) -> str:
config_list, llm_config = setting_config(model_name)
doctor = OpenAIWrapper(api_key=config_list[0]['api_key'], config_list=config_list)
response = doctor.create(messages=[
{"role": "assistant",
"content": doctor_description},
{"role": "user",
"content": prompt_internist_doctor + "\nDon't use Markdown tags."}
],
temperature=0.3,
model=config_list[0]['model'])
text = doctor.extract_text_or_completion_object(response)
return text
def multiagent_doctors(json_data: dict, model_name: str) -> List[AssistantAgent]:
config_list, llm_config = setting_config(model_name)
doc = []
for i in range(len(json_data['doctors'])):
doc.append(AssistantAgent(
name=json_data['doctors'][i]['role'].replace(" ", "_"),
llm_config=llm_config,
system_message="As a " + json_data['doctors'][i]['role'].replace(" ", "_") + ". Discuss with other medical experts in the team to help the INTERNIST DOCTOR make a final decision. Avoid postponing further examinations and repeating opinions given in the analysis, but explain in a logical and concise manner why you are making this final decision."))
return doc
def care_discussion_start(doc: List[AssistantAgent], prompt_reunion: str, internist_sys_message: str, model_name: str) -> autogen.GroupChatManager:
config_list, llm_config = setting_config(model_name)
doc.append(TrackableUserProxyAgent(
name="internist_doctor",
human_input_mode="NEVER",
max_consecutive_auto_reply=1,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith(("JUSTIFIABLE", "UNJUSTIFIABLE")),
code_execution_config=False,
llm_config=llm_config,
system_message=internist_sys_message
))
groupchat = autogen.GroupChat(agents=doc,
messages=[],
max_round=(len(doc)+1),
speaker_selection_method="round_robin",
role_for_select_speaker_messages='user',
)
manager = autogen.GroupChatManager(groupchat=groupchat,
llm_config=llm_config,
max_consecutive_auto_reply=1
)
doc[-1].initiate_chat(
manager,
message=prompt_reunion,
)
return manager |