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| 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 """ | |
| # <!DOCTYPE html> | |
| # <html> | |
| # <head> | |
| # <title>MALI_NURSE API</title> | |
| # </head> | |
| # <body> | |
| # <h1>Welcome to MALI_NURSE API</h1> | |
| # <p>This is the index page. Use the link below to access the API docs:</p> | |
| # <a href="/docs">Go to Swagger Docs UI</a> | |
| # </body> | |
| # </html> | |
| # """ | |
| def get_chat_history(): | |
| return ChatHistory(chat_history = nurse_llm.chat_history) | |
| 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"} | |
| 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] | |
| 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) |