app.py

# %%
import os, json, itertools, bisect, gc
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
import transformers
import torch
from accelerate import Accelerator
import accelerate
import time
import os
import gradio as gr
import requests
import random
import googletrans
translator = googletrans.Translator()

model = None
tokenizer = None
generator = None

os.environ["CUDA_VISIBLE_DEVICES"]=""

def load_model(model_name, eight_bit=0, device_map="auto"):
 global model, tokenizer, generator
 print("Loading "+model_name+"...")

 if device_map == "zero":
 device_map = "balanced_low_0"

 # config
 gpu_count = torch.cuda.device_count()
 print('gpu_count', gpu_count)

 if torch.cuda.is_available():
 torch_dtype = torch.float16
 else:
 torch_dtype = torch.float32
 
 print(model_name)
 tokenizer = transformers.LLaMATokenizer.from_pretrained(model_name)
 model = transformers.LLaMAForCausalLM.from_pretrained(
 model_name,
 #device_map=device_map,
 #device_map="auto",
 torch_dtype=torch_dtype,
 #max_memory = {0: "14GB", 1: "14GB", 2: "14GB", 3: "14GB",4: "14GB",5: "14GB",6: "14GB",7: "14GB"},
 #load_in_8bit=eight_bit,
 #from_tf=True,
 low_cpu_mem_usage=True,
 load_in_8bit=False,
 cache_dir="cache"
 )
 if torch.cuda.is_available():
 model = model.cuda()
 else:
 model = model.cpu()
 generator = model.generate

# chat doctor
def chatdoctor(input, state): 
 # print('input',input)
 # history = history or []
 print('state',state)
 
 invitation = "ChatDoctor: "
 human_invitation = "Patient: "
 fulltext = "If you are a doctor, please answer the medical questions based on the patient's description. \n\n"
 
 for i in range(len(state)):
 if i % 2:
 fulltext += human_invitation + state[i] + "\n\n"
 else:
 fulltext += invitation + state[i] + "\n\n"
 fulltext += human_invitation + input + "\n\n"
 fulltext += invitation
 print('fulltext: ',fulltext)

 generated_text = ""
 gen_in = tokenizer(fulltext, return_tensors="pt").input_ids
 if torch.cuda.is_available():
 gen_in = gen_in.cuda()
 else:
 gen_in = gen_in.cpu()
 in_tokens = len(gen_in)
 print('len token',in_tokens)
 with torch.no_grad():
 generated_ids = generator(
 gen_in,
 max_new_tokens=200,
 use_cache=True,
 pad_token_id=tokenizer.eos_token_id,
 num_return_sequences=1,
 do_sample=True,
 repetition_penalty=1.1, # 1.0 means 'off'. unfortunately if we penalize it it will not output Sphynx:
 temperature=0.5, # default: 1.0
 top_k = 50, # default: 50
 top_p = 1.0, # default: 1.0
 early_stopping=True,
 )
 generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] # for some reason, batch_decode returns an array of one element?
 text_without_prompt = generated_text[len(fulltext):]
 response = text_without_prompt
 response = response.split(human_invitation)[0]
 response.strip()
 print(invitation + response)
 print("")
 return response


def predict(input, chatbot, state):
 print('predict state: ', state)
 
 # input에 한국어가 detect 되면 영어로 변경, 아니면 그대로
 is_kor = True
 if googletrans.Translator().detect(input).lang == 'ko':
 en_input = translator.translate(input, src='ko', dest='en').text
 else:
 en_input = input
 is_kor = False
 
 response = chatdoctor(en_input, state)

 if is_kor:
 ko_response = translator.translate(response, src='en', dest='ko').text
 else:
 ko_response = response
 
 state.append(response)
 chatbot.append((input, ko_response))
 return chatbot, state

load_model("mnc-ai/chatdoctor")

with gr.Blocks() as demo:
 gr.Markdown("""<h1><center>챗 닥터입니다. 어디가 불편하신가요?</center></h1>
 """)
 chatbot = gr.Chatbot()
 state = gr.State([])
 with gr.Row():
 txt = gr.Textbox(show_label=False, placeholder="여기에 질문을 쓰고 엔터").style(container=False)
 clear = gr.Button("상담 새로 시작")
 txt.submit(predict, inputs=[txt, chatbot, state], outputs=[chatbot, state], queue=False )
 txt.submit(lambda x: "", txt, txt)
 clear.click(lambda: None, None, chatbot, queue=False)
 clear.click(lambda x: "", txt, txt)
 # clear 클릭 시 state 초기화
 clear.click(lambda x: [], state, state)
 
demo.launch()