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.environ.get('HUGGINGFACEHUB_API_TOKEN')  # Replace with your token
)

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

def reset_conversation():
    '''
    Resets Conversation
    '''
    st.session_state.messages = []
    return None

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

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

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

st.title("Mistral-7B Chatbot")

# Function to get streamed response
def get_streamed_response(message, history):
    all_message = [{
        "role": "system",
        "content": "From now on, you are an AI assistant knowledgeable in general topics. You can respond with relevant information and provide concise, friendly replies. Always maintain a helpful and neutral tone. Ensure to be concise to keep the conversation flowing smoothly."
    }]
    
    for human, assistant in history:
        all_message.append({"role": "user", "content": human})
        all_message.append({"role": "assistant", "content": assistant})

    return all_message

# 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})

    # Prepare all messages for the conversation context
    history = [(msg["content"], next((m["content"] for m in st.session_state.messages if m["role"] == "assistant"), "")) for msg in st.session_state.messages]

    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        try:
            response = client.chat.completions.create(
                model=model_link,
                messages=get_streamed_response(prompt, history),
                temperature=temperature,
                max_tokens=150  # Adjust the token limit as needed
            )

            st.markdown(response['choices'][0]['message']['content'])

        except Exception as e:
            st.markdown("An error occurred. Please try again later.")
            st.markdown("Error details:")
            st.markdown(str(e))

    # Add assistant's response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response['choices'][0]['message']['content']})