app.py

import streamlit as st
from openai import OpenAI
import os
from dotenv import load_dotenv

load_dotenv()

# Initialize the client
client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1",
    api_key=os.getenv('HUGGINGFACEHUB_API_TOKEN')
)

# Model link for Mistral
model_link = "mistralai/Mistral-7B-Instruct-v0.2"

def reset_conversation():
    '''
    Resets Conversation
    '''
    st.session_state.messages = []

# Set the temperature value directly in the code
temperature = 0.5

# Add reset button to clear conversation
st.button('Reset Chat', on_click=reset_conversation)

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Accept user input
if prompt := st.chat_input("Type your message here..."):

    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)

    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})

    # Interact with the model
    try:
        # Send the user and system messages to the API
        messages_for_api = [{"role": "system", "content": "You are a helpful assistant."}] + st.session_state.messages

        response = client.chat.completions.create(
            model=model_link,
            messages=messages_for_api,
            temperature=temperature,
            max_tokens=150  # Adjust the max tokens according to your needs
        )

        assistant_response = response["choices"][0]["message"]["content"]

        # Display assistant response in chat message container
        with st.chat_message("assistant"):
            st.markdown(assistant_response)

        # Append the assistant's response to the chat history
        st.session_state.messages.append({"role": "assistant", "content": assistant_response})

    except Exception as e:
        # Display error message to user
        with st.chat_message("assistant"):
            st.markdown("Sorry, I couldn't process your request. Please try again later.")
        st.write(e)