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# ©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("Intel® AI for Enterprise Inference - Chatbot")
# 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"]
api_key = None
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})
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