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)
    
demo.launch()