import os from typing import Optional import requests import uvicorn from llm.basemodel import EHRModel from llm.llm import VirtualNurseLLM from fastapi import FastAPI, HTTPException from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import FileResponse, HTMLResponse from pythainlp.tokenize import sent_tokenize from pydantic import BaseModel from llm.models import model_list, get_model import time initial_model = "typhoon-v1.5x-70b-instruct" nurse_llm = VirtualNurseLLM( # base_url=model_list[initial_model]["base_url"], model_name=model_list[initial_model]["model_name"], # api_key=model_list[initial_model]["api_key"] ) # model: OpenThaiGPT # nurse_llm = VirtualNurseLLM( # base_url="https://api.aieat.or.th/v1", # model=".", # api_key="dummy" # ) app = FastAPI() app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) class UserInput(BaseModel): user_input: str model_name: str = "typhoon-v1.5x-70b-instruct" class NurseResponse(BaseModel): nurse_response: str class EHRData(BaseModel): ehr_data: Optional[EHRModel] current_context: Optional[str] current_prompt: Optional[str] current_prompt_ehr: Optional[str] current_patient_response: Optional[str] current_question: Optional[str] class ChatHistory(BaseModel): chat_history: list # @app.get("/", response_class=HTMLResponse) # def read_index(): # return """ # # # # MALI_NURSE API # # #

Welcome to MALI_NURSE API

#

This is the index page. Use the link below to access the API docs:

# Go to Swagger Docs UI # # # """ @app.get("/history") def get_chat_history(): return ChatHistory(chat_history = nurse_llm.chat_history) @app.get("/details") def get_ehr_data(): return EHRData( ehr_data=nurse_llm.ehr_data, current_context=nurse_llm.current_context, current_prompt=nurse_llm.current_prompt, current_prompt_ehr=nurse_llm.current_prompt_ehr, current_patient_response=nurse_llm.current_patient_response, current_question=nurse_llm.current_question ) def toggle_debug(): nurse_llm.debug = not nurse_llm.debug return {"debug_mode": "on" if nurse_llm.debug else "off"} @app.post("/reset") def data_reset(): nurse_llm.reset() print("Chat history and EHR data have been reset.") model_cache = {} def get_model_cached(model_name): if model_name not in model_cache: model_cache[model_name] = get_model(model_name=model_name) return model_cache[model_name] @app.post("/nurse_response") def nurse_response(user_input: UserInput): """ Models: "typhoon-v1.5x-70b-instruct (default)", "openthaigpt", "llama-3.3-70b-versatile" """ start_time = time.time() if user_input.model_name != nurse_llm.model_name: print(f"Changing model to {user_input.model_name}") try: nurse_llm.client = get_model_cached(model_name=user_input.model_name) except ValueError: return {"error": "Invalid model name"} print(nurse_llm.client) # response = nurse_llm.slim_invoke(user_input.user_input) response = nurse_llm.invoke(user_input.user_input) end_time = time.time() duration = end_time - start_time print(f"Function running time: {duration} seconds") # Log the model name, user input, response, and execution time in CSV format with open("runtime_log.csv", "a") as log_file: log_file.write(f"{user_input.model_name},{user_input.user_input},{response},{duration}\n") return NurseResponse(nurse_response=response) # TTS from tts.tts import app as tts_app app.mount("/tts", tts_app) if __name__ == "__main__": uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)