app.py

from openai import OpenAI  # Assuming Nvidia client is available in the same library, adjust if necessary
import streamlit as st

# Initialize Nvidia client
client = OpenAI(
    base_url="https://integrate.api.nvidia.com/v1",  # Nvidia API endpoint
    api_key=st.secrets["NVIDIA_API_KEY"]  # Nvidia API Key from Streamlit secrets
)

st.title("ChatGPT-like clone with Nvidia Model")

# Initialize session state variables if not already present
if "openai_model" not in st.session_state:
    st.session_state["openai_model"] = "nvidia/llama-3.1-nemotron-70b-instruct"

if "messages" not in st.session_state:
    # Adding the initial system message
    st.session_state.messages = [{"role": "system", "content": "You are a helpful assistant."}]

# Render the chat history
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Get new user input
if prompt := st.chat_input("What is up?"):
    # Add user message to the session state
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    # Display assistant's message while waiting for the response
    with st.chat_message("assistant"):
        # Create Nvidia completion request with full conversation history
        stream = client.chat.completions.create(
            model=st.session_state["openai_model"],
            messages=st.session_state.messages,  # Include all previous messages in the API call
            temperature=0.5,
            top_p=0.7,
            max_tokens=1024,
            stream=True,
        )
        response = ""
        for chunk in stream:
            if chunk.choices[0].delta.content is not None:
                chunk_text = chunk.choices[0].delta.content
                response += chunk_text
                st.write(chunk_text, end="")

    # Store the assistant response in the session state
    st.session_state.messages.append({"role": "assistant", "content": response})