# ©2024 Intel Corporation # Permission is granted for recipient to internally use and modify this software for purposes of benchmarking and testing on Intel architectures. # This software is provided "AS IS" possibly with faults, bugs or errors; it is not intended for production use, and recipient uses this design at their own risk with no liability to Intel. # Intel disclaims all warranties, express or implied, including warranties of merchantability, fitness for a particular purpose, and non-infringement. # Recipient agrees that any feedback it provides to Intel about this software is licensed to Intel for any purpose worldwide. No permission is granted to use Intel’s trademarks. # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the code. # 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("Chat Bot") # Extract the keys (model names) from the JSON data model_names = list(endpoint_data.keys()) with st.sidebar: modelname = st.selectbox("Select a LLM model (Hosted by DENVR DATAWORKS) ", model_names) st.write(f"You selected: {modelname}") st.button("Start New Chat", on_click=clear_chat) endpoint = endpoint_data[modelname] # api_key=os.environ.get('API_KEY') api_key = st.secrets["openai_apikey"] if not api_key: st.info("Please add your OpenAI API key to continue.") st.stop() base_url = endpoint client = OpenAI(api_key=api_key, base_url=base_url) # Extract the model name models = client.models.list() modelname = models.data[0].id 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"): stream = client.chat.completions.create( model=modelname, messages=[ {"role": m["role"], "content": m["content"]} for m in st.session_state.messages ], max_tokens=5000, stream=True, ) response = st.write_stream(stream) st.session_state.messages.append({"role": "assistant", "content": response})