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import streamlit as st
from huggingface_hub import InferenceClient
from langchain import HuggingFaceHub
import requests
# Internal usage
import os
from dotenv import load_dotenv
from time import sleep
load_dotenv()
# Set HF API token
HUGGINGFACEHUB_API_TOKEN = os.getenv('HF_TOKEN')
#AVATARS
av_us = './man.png' #"🦖" #A single emoji, e.g. "🧑💻", "🤖", "🦖". Shortcodes are not supported.
av_ass = './robot.png'
# FUNCTION TO LOG ALL CHAT MESSAGES INTO chathistory.txt
def writehistory(text):
with open('chathistory.txt', 'a') as f:
f.write(text)
f.write('\n')
f.close()
repo="HuggingFaceH4/starchat-beta"
### START STREAMLIT UI
st.title("🤗 AI 聊天機器人 測試版")
st.subheader("支援中文對話")
# Set a default model
if "hf_model" not in st.session_state:
st.session_state["hf_model"] = "HuggingFaceH4/starchat-beta"
### INITIALIZING STARCHAT FUNCTION MODEL
def starchat(model,myprompt, your_template):
from langchain import PromptTemplate, LLMChain
os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN
llm = HuggingFaceHub(repo_id=model ,
model_kwargs={"min_length":30,
"max_new_tokens":1024, "do_sample":True,
"temperature":0.2, "top_k":50,
"top_p":0.95, "eos_token_id":49155})
template = your_template
prompt = PromptTemplate(template=template, input_variables=["myprompt"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
llm_reply = llm_chain.run(myprompt)
reply = llm_reply.partition('<|end|>')[0]
return reply
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
if message["role"] == "user":
with st.chat_message(message["role"],avatar=av_us):
st.markdown(message["content"])
else:
with st.chat_message(message["role"],avatar=av_ass):
st.markdown(message["content"])
# Accept user input
if myprompt := st.chat_input("請介紹台灣"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": myprompt})
# Display user message in chat message container
with st.chat_message("user", avatar=av_us):
st.markdown(myprompt)
usertext = f"user: {myprompt}"
writehistory(usertext)
# Display assistant response in chat message container
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
res = starchat(
st.session_state["hf_model"],
myprompt, "<|system|>\n<|end|>\n<|user|>\n{myprompt}<|end|>\n<|assistant|>")
response = res.split(" ")
for r in response:
full_response = full_response + r + " "
message_placeholder.markdown(full_response + "▌")
sleep(0.1)
message_placeholder.markdown(full_response)
asstext = f"assistant: {full_response}"
writehistory(asstext)
st.session_state.messages.append({"role": "assistant", "content": full_response}) |