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from openai import OpenAI
import streamlit as st
import os
import sys
from dotenv import load_dotenv, dotenv_values
load_dotenv()
# initialize the client
client = OpenAI(
base_url="https://api-inference.huggingface.co/v1",
api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN') #"hf_xxx" # Replace with your token
)
st.title("💬 Ask")
st.caption("🚀 A streamlit chatbot powered by Nedum")
# Initialize chat history
if 'messages' not in st.session_state:
st.session_state['messages'] = [] #[{"role": "assistant", "content": "How can I help you?"}]
# Display chat messages from history on app rerun
for messasge in st.session_state.messages:
st.chat_message(messasge["role"]).write(messasge["content"])
# React to user input
if prompt := st.chat_input():
# Display user message in chat message container
st.chat_message("user").write(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
##Get response to the message using client
response = client.chat.completions.create(model="google/gemma-2b-it", messages=st.session_state.messages)
msg = response.choices[0].message.content
# Display assistant response in chat message container
st.chat_message("assistant").write(msg)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": msg}) |