# Import necessary libraries import streamlit as st import os from openai import OpenAI import json working_dir = os.path.dirname(os.path.abspath(__file__)) endpoint_data = json.load(open(f"{working_dir}/model_info.json")) def clear_chat(): st.session_state.messages = [] st.title("Intel® AI for Enterprise Inference") st.header("LLM chatbot") # Extract the keys (model names) from the JSON data # model_names = list(endpoint_data.keys()) with st.sidebar: #Enter openai_api key under "Secrets " in HF settings #Enter base_url under "Variables" in HF settings api_key = st.session_state.api_key = st.secrets["openai_apikey"] base_url = st.session_state.base_url = os.environ.get("base_url") client = OpenAI(api_key=api_key, base_url=base_url) model_names = client.models.list() modelname = st.selectbox("Select LLM model (Running on Intel® Gaudi®) ", model_names) st.write(f"You selected: {modelname}") st.button("Start New Chat", on_click=clear_chat) try: if "messages" not in st.session_state: st.session_state.messages = [] for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) if prompt := st.chat_input("What is up?"): st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) with st.chat_message("assistant"): try: stream = client.chat.completions.create( model=modelname, messages=[ {"role": m["role"], "content": m["content"]} for m in st.session_state.messages ], max_tokens=4096, stream=True, ) response = st.write_stream(stream) except Exception as e: st.error(f"An error occurred while generating the response: {e}") response = "An error occurred while generating the response." st.session_state.messages.append({"role": "assistant", "content": response}) except KeyError as e: st.error(f"Key error: {e}") except Exception as e: st.error(f"An unexpected error occurred: {e}")