학습한 모델 웹사이트 챗봇으로 시각화 하기! - 모델 서빙하기 2

from fastapi import FastAPI
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM,AutoTokenizer

# 기본 모델 가져오기 
base_model = AutoModelForCausalLM.from_pretrained("SmolLM1.7B")

# 로컬 경로로 PeftModel 불러오기
model = PeftModel.from_pretrained(base_model,"saved_model")

# 로컬 경로로 tokenizer 불러오기
tokenizer = AutoTokenizer.from_pretrained("saved_model")


def get_answer(prompt): 
    #prompt = "what is a road runner?"
    inputs = tokenizer(prompt, return_tensors='pt')
    
    generate_ids = model.generate(inputs.input_ids, max_length = inputs.input_ids.shape[1]+15)# 입력 길이에 +n만큼 출력한다.
    output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    output = output[len(prompt):]
    return output
    
# 오브제긑 생성
app = FastAPI()

@app.get('/')
def root():
    return{'message':'Hello World'}


@app.get('/chat_test')
def test(user_message):
    return{'message': get_answer(user_message)}

@app.post('/chat')
def test(param : dict={}):
    user_message=param.get('user_message', ' ')
    return {'message': get_answer(user_message)}

uvicorn main:app --reload

 

import streamlit as st

st.write('hello!')

 

 streamlit run st.py

 

이메일 한번 작성해주면 이렇게 바로 로컬 주소로 열어주네요

hello도 떠있습니다.

 

한번 꾸며볼게요!

import streamlit as st

st.title("My Chatbot")
st.header("Custom Trained SmolLM Model")
st.subheader("pretrained - matworks data")
st.caption("created by YJH")
st.write('hello!')

h가 빠졌네요 ㅎ.ㅎ.....

일단 깔끔하게 꾸몄습니다.

이제 인풋을 만들어야죠

 

 

import streamlit as st

st.title("My Chatbot")
st.header("Custom Trained SmolLM Model")
st.subheader("pretrained - Mathworks data")
st.caption("created by YJH")
st.write('hello!')


st.text_input("type a message..", key = "user_message")

message = st.session_state.user_message
st.write(f"user_message: {message}")

잘 된것을 확인할 수 있습니다.

 

import streamlit as st

class llama_web():
    def __init__(self):
        pass
    
    def window(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('hello!')


        st.text_input("type a message..", key = "user_message")

        message = st.session_state.user_message
        st.write(f"user_message: {message}")
    
if __name__ == '__main__':
    web = llama_web()
    web.window()

코드 살짝 정리했습니다.

 

import streamlit as st
import requests 
import json
class llama_web():
    def __init__(self):
        self.URL = "http://127.0.0.1:800/chat"
        
        
    def get_answer(self,message):
        param={'user_message': message}
        resp = requests.post(self.URL,json=param)
        output = json.loads(resp.content)
        
        return output    
    
    def window(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('hello!')


        st.text_input("type a message..", key = "user_message")

        #message = st.session_state.user_message
        #st.write(f"user_message: {message}")
        
        if user_message := st.session_state['user_message']:
            output = self.get_answer(user_message)
            st.write(f"user_message: {user_message}")
            st.write(f"bot_message: {output}")
    
if __name__ == '__main__':
    web = llama_web()
    web.window()

또잉...?

아 오타였네요 ㅎㅎ...

주소가 800이 아니라 8000 입니다.

오 뭔가 뱉고 있습니다

 

좀 더 깔끔하게 다듬어 봤습니다.

import streamlit as st
import requests 
import json
class llama_web():
    def __init__(self):
        self.URL = "http://127.0.0.1:8000/chat"
        
        
    def get_answer(self,message):
        param={'user_message': message}
        resp = requests.post(self.URL,json=param)
        output = json.loads(resp.content)['message']
        
        return output    
    
    def window(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('hello!')


        st.text_input("type a message..", key = "user_message")

        #message = st.session_state.user_message
        #st.write(f"user_message: {message}")
        
        if user_message := st.session_state['user_message']:
            output = self.get_answer(user_message)
            #st.write(f"user_message: {user_message}")
            #st.write(f"bot_message: {output}")
            st.success(f"bot_message: {output}")# 초록 박스 안에 
    
if __name__ == '__main__':
    web = llama_web()
    web.window()

 

이제 진짜 챗봇처럼 기록도 남아있어야겠죠?

 

import streamlit as st
import requests 
import json
import pandas as pd 

class llama_web():
    def __init__(self):
        self.URL = "http://127.0.0.1:8000/chat"
        
        
    def get_answer(self,message):
        param={'user_message': message}
        resp = requests.post(self.URL,json=param)
        output = json.loads(resp.content)['message']
        
        return output  
    
    def update_log(self, user_message, bot_message):
        if 'chat_log' not in st.session_state:
            st.session_state.chat_log = {'user_message': [], 'bot_message':[]} #새로 만들어 준 것이다.
        st.session_state.chat_log['user_message'] .append(user_message) 
        st.session_state.chat_log['bot_message'] .append(bot_message)
        
        return st.session_state.chat_log
    
    def window(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('hello!')


        st.text_input("type a message..", key = "user_message")

        #message = st.session_state.user_message
        #st.write(f"user_message: {message}")
        
        if user_message := st.session_state['user_message']:
            output = self.get_answer(user_message)
            #st.write(f"user_message: {user_message}")
            #st.write(f"bot_message: {output}")
            st.success(f"bot_message: {output}")# 초록 박스 안에 
            
            chat_log = self.update_log(user_message, output)
            st.write(pd.DataFrame(chat_log))
    
if __name__ == '__main__':
    web = llama_web()
    web.window()

그런데 이렇게 되면 뭔가 챗봇 느낌은 아니다!

 

챗봇스러움을 늘려봅시다.

import streamlit as st
from streamlit_chat import message
import requests 
import json
import pandas as pd 

class llama_web():
    def __init__(self):
        self.URL = "http://127.0.0.1:8000/chat"
        
        
    def get_answer(self,message):
        param={'user_message': message}
        resp = requests.post(self.URL,json=param)
        output = json.loads(resp.content)['message']
        
        return output  
    
    def update_log(self, user_message, bot_message):
        if 'chat_log' not in st.session_state:
            st.session_state.chat_log = {'user_message': [], 'bot_message':[]} #새로 만들어 준 것이다.
        st.session_state.chat_log['user_message'] .append(user_message) 
        st.session_state.chat_log['bot_message'] .append(bot_message)
        
        return st.session_state.chat_log
    
    def window(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('hello!')


        st.text_input("type a message..", key = "user_message")

        #message = st.session_state.user_message
        #st.write(f"user_message: {message}")
        
        if user_message := st.session_state['user_message']:
            output = self.get_answer(user_message)
            #st.write(f"user_message: {user_message}")
            #st.write(f"bot_message: {output}")
            st.success(f"bot_message: {output}")# 초록 박스 안에 
            
            chat_log = self.update_log(user_message, output)
            st.write(pd.DataFrame(chat_log))
            
            
    def chat(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('한번 써 보세요!')
        
        message("Hello. I'm matworks user",is_user=True)
        message("Hello. I'm matworks chatbot",is_user=False)

        
        
    
if __name__ == '__main__':
    web = llama_web()
    web.chat()

귀엽다!

 

이제 입력을 만들어 줍니다.

import streamlit as st
from streamlit_chat import message
import requests 
import json
import pandas as pd 

class llama_web():
    def __init__(self):
        self.URL = "http://127.0.0.1:8000/chat"
        
        
    def get_answer(self,message):
        param={'user_message': message}
        resp = requests.post(self.URL,json=param)
        output = json.loads(resp.content)['message']
        
        return output  
    
    def update_log(self, user_message, bot_message):
        if 'chat_log' not in st.session_state:
            st.session_state.chat_log = {'user_message': [], 'bot_message':[]} #새로 만들어 준 것이다.
        st.session_state.chat_log['user_message'] .append(user_message) 
        st.session_state.chat_log['bot_message'] .append(bot_message)
        
        return st.session_state.chat_log
    
    def window(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('hello!')


        st.text_input("type a message..", key = "user_message")

        #message = st.session_state.user_message
        #st.write(f"user_message: {message}")
        
        if user_message := st.session_state['user_message']:
            output = self.get_answer(user_message)
            #st.write(f"user_message: {user_message}")
            #st.write(f"bot_message: {output}")
            st.success(f"bot_message: {output}")# 초록 박스 안에 
            
            chat_log = self.update_log(user_message, output)
            st.write(pd.DataFrame(chat_log))
            
            
    def chat(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('한번 써 보세요!')
        
        #message("Hello. I'm matworks user",is_user=True)
        #message("Hello. I'm matworks chatbot",is_user=False)
        st.text_input("type a message..", key = "user_message")

        if user_message := st.session_state['user_message']:
            output = self.get_answer(user_message)
            
            message(user_message,is_user= True)
            message(output)
            

        
        
    
if __name__ == '__main__':
    web = llama_web()
    web.chat()

이제 완전히 대화하는 것 처럼 나오는데 저장이 안되네요
아까 만든 저장하는 코드를 재활용 합시다

 

import streamlit as st
from streamlit_chat import message
import requests 
import json
import pandas as pd 

class llama_web():
    def __init__(self):
        self.URL = "http://127.0.0.1:8000/chat"
        
        
    def get_answer(self,message):
        param={'user_message': message}
        resp = requests.post(self.URL,json=param)
        output = json.loads(resp.content)['message']
        
        return output  
    
    def update_log(self, user_message, bot_message):
        if 'chat_log' not in st.session_state:
            st.session_state.chat_log = {'user_message': [], 'bot_message':[]} #새로 만들어 준 것이다.
        st.session_state.chat_log['user_message'] .append(user_message) 
        st.session_state.chat_log['bot_message'] .append(bot_message)
        
        return st.session_state.chat_log
    
    def window(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('hello!')


        st.text_input("type a message..", key = "user_message")

        #message = st.session_state.user_message
        #st.write(f"user_message: {message}")
        
        if user_message := st.session_state['user_message']:
            output = self.get_answer(user_message)
            #st.write(f"user_message: {user_message}")
            #st.write(f"bot_message: {output}")
            st.success(f"bot_message: {output}")# 초록 박스 안에 
            
            chat_log = self.update_log(user_message, output)
            st.write(pd.DataFrame(chat_log))
            
            
    def chat(self):
        st.title("My Chatbot")
        st.header("Custom Trained SmolLM Model")
        st.subheader("pretrained - Mathworks data")
        st.caption("created by YJH")
        st.write('한번 써 보세요!')
        
        #message("Hello. I'm matworks user",is_user=True)
        #message("Hello. I'm matworks chatbot",is_user=False)
        st.text_input("type a message..", key = "user_message")

        if user_message := st.session_state['user_message']:
            output = self.get_answer(user_message)
            
            #message(user_message,is_user= True)
            #message(output)
            chat_log = self.update_log(user_message,output)
            #오래된 것은 아래에, 최신은 위로 가야 한다.
            bot_message = chat_log["bot_message"][::-1]# 순서 뒤집기 
            user_message = chat_log["user_message"][::-1]# 순서 뒤집기 
            for idx,(bot,user) in enumerate(zip(bot_message,user_message)):
                message(bot,key=f"{idx}_bot")
                message(user,key=f"{idx}_user",is_user = True)

        
        
    
if __name__ == '__main__':
    web = llama_web()
    web.chat()

잘 작동하네요 ㅎㅎ...

이제 모델을 좀 더 잘 만들어야 겠네요 ㅎㅎㅎㅎㅎ........