app.py

import streamlit as st
from openai import OpenAI
from typing import Iterator
import os
from phoenix.otel import register
from datetime import datetime

tracer_provider = register(
  project_name=st.secrets['PHOENIX_PROJECT_NAME'],
  endpoint="https://app.phoenix.arize.com/v1/traces"
)

from openinference.instrumentation.openai import OpenAIInstrumentor
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

st.set_page_config(
    page_title="Taiwan Smol Chat",
    page_icon="🦉",
    layout="centered"
)

st.title("🦉Taiwan Smol Chat")
st.info('Model based on [lianghsun/Llama-3.2-Taiwan-3B-Instruct](https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B-Instruct)', icon="🧠")
st.warning('Playgroud 有可能因為 GPU 被挪用至生成資料使用導致暫時性無法對話。', icon="⚠️")

client = OpenAI(
    api_key=st.secrets['API_KEY'],
    base_url=st.secrets['API_BASE_URL'],
)

if "openai_model" not in st.session_state:
    st.session_state["openai_model"] = st.secrets['MODEL']

if "messages" not in st.session_state:
    current_date = datetime.now().strftime("%Y-%m-%d")
    st.session_state.messages = [{"role": "system", "content": f"現在的日期: {current_date}"}]

for message in st.session_state.messages:
    if message['role'] == "system": continue
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

if prompt := st.chat_input("來聊點什麼吧"):
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    with st.chat_message("assistant"):
        stream = client.chat.completions.create(
            model=st.session_state["openai_model"],
            messages=[
                {"role": m["role"], "content": m["content"]}
                for m in st.session_state.messages
            ],
            stream=True,
            temperature=.2
        )
        response = st.write_stream(stream)
    st.session_state.messages.append(
        {"role": "assistant", "content": response})

# with st.container():
#     st.caption('Please be aware that current Large Language Models (LLMs) can exhibit “hallucinations,” producing plausible-sounding but inaccurate or fabricated information. It is crucial to carefully review and verify any content generated by LLMs to avoid misunderstandings or misinformation. Always cross-check facts and consult reliable sources before making important decisions based on LLM outputs.')