app.py

import streamlit as st
from openai import OpenAI
import os
import numpy as np
from dotenv import load_dotenv
import random

# Load environment variables
load_dotenv()

# Constants
MAX_TOKENS = 4000
DEFAULT_TEMPERATURE = 0.5

# Initialize the client
def initialize_client():
    api_key = os.environ.get('HUGGINGFACEHUB_API_TOKEN')
    if not api_key:
        st.error("HUGGINGFACEHUB_API_TOKEN not found in environment variables.")
        st.stop()
    return OpenAI(
        base_url="https://api-inference.huggingface.co/v1",
        api_key=api_key
    )

# Create supported models
model_links = {
    "Meta-Llama-3.1-8B": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "Mistral-7B-Instruct-v0.3": "mistralai/Mistral-7B-Instruct-v0.3",
    "Gemma-7b-it": "google/gemma-7b-it",
}

# Model information including logos
model_info = {
    "Meta-Llama-3.1-8B": {
        'description': """The Llama (3.1) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.
        \nIt was created by the [**Meta's AI**](https://llama.meta.com/) team and has over **8 billion parameters.**\n""",
        "logo": "llama_logo.gif",
    },
    "Mistral-7B-Instruct-v0.3": {
        'description': """The Mistral-7B-Instruct-v0.3 is an instruct-tuned version of Mistral-7B.
        \nIt was created by [**Mistral AI**](https://mistral.ai/) and has **7 billion parameters.**\n""",
        "logo": "mistrail.jpeg",
    },
    "Gemma-7b-it": {
        'description': """Gemma is a family of lightweight, state-of-the-art open models from Google.
        \nThe 7B-it variant is instruction-tuned and has **7 billion parameters.**\n""",
        "logo": "gemma.jpeg",
    }
}

# Random dog images for error message
random_dog_images = ["broken_llama3.jpeg"]

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

st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button
    
def main():
    st.header('Multi-Models')

    # Initialize client
    client = initialize_client()

    # Sidebar for model selection and temperature
    selected_model = st.sidebar.selectbox("Select Model", list(model_links.keys()))
    temperature = st.sidebar.slider('Select a temperature value', 0.0, 1.0, DEFAULT_TEMPERATURE)

    if "prev_option" not in st.session_state:
        st.session_state.prev_option = selected_model

    if st.session_state.prev_option != selected_model:
        st.session_state.messages = []
        # st.write(f"Changed to {selected_model}")
        st.session_state.prev_option = selected_model
        reset_conversation()

    st.markdown(f'_powered_ by ***:violet[{selected_model}]***')

    # Display model info and logo
    st.sidebar.write(f"You're now chatting with **{selected_model}**")
    st.sidebar.markdown(model_info[selected_model]['description'])
    st.sidebar.image(model_info[selected_model]['logo'], use_column_width=True)
    st.sidebar.markdown("*Generated content may be inaccurate or false.*")

    # 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"])

    # Chat input and response
    if prompt := st.chat_input("Type message here..."):
        process_user_input(client, prompt, selected_model, temperature)

def process_user_input(client, prompt, selected_model, temperature):
    # Display user message
    with st.chat_message("user"):
        st.markdown(prompt)
    st.session_state.messages.append({"role": "user", "content": prompt})

    # Generate and display assistant response
    with st.chat_message("assistant"):
        try:
            stream = client.chat.completions.create(
                model=model_links[selected_model],
                messages=[
                    {"role": m["role"], "content": m["content"]}
                    for m in st.session_state.messages
                ],
                temperature=temperature,
                stream=True,
                max_tokens=MAX_TOKENS,
            )
            response = st.write_stream(stream)
        except Exception as e:
            handle_error(e)
            return

    st.session_state.messages.append({"role": "assistant", "content": response})

def handle_error(error):
    response = """😵‍💫 Looks like someone unplugged something!
    \n Either the model space is being updated or something is down."""
    st.write(response)
    random_dog_pick = random.choice(random_dog_images)
    st.image(random_dog_pick)
    st.write("This was the error message:")
    st.write(str(error))

if __name__ == "__main__":
    main()