app.py

import numpy as np
import streamlit as st
from openai import OpenAI
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')  # Replace with your token
)

# Create supported model
model_links = {
    "Meta-Llama-3-8B": "meta-llama/Meta-Llama-3-8B-Instruct"
}

# Pull info about the model to display
model_info = {
    "Meta-Llama-3-8B": {
        'description': """The Llama (3) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n
        \nIt was created by the [**Meta's AI**](https://llama.meta.com/) team and has over **8 billion parameters.** \n""",
        'logo': 'Llama_logo.png'
    }
}

# Random dog images for error message
random_dog = ["0f476473-2d8b-415e-b944-483768418a95.jpg", "1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg"]

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

# Define the available models
models = [key for key in model_links.keys()]

# Create the sidebar with the dropdown for model selection
selected_model = st.sidebar.selectbox("Select Model", models)

# Custom description for SciMom
st.sidebar.write("Built for my mom, with love. This model is pretrained with textbooks of Science NCERT.")
st.sidebar.write("Model used: Meta Llama, trained using: Docker AutoTrain.")

# Create a temperature slider
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))

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

# Create model description
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'])
st.sidebar.markdown("*Generated content may be inaccurate or false.*")

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.session_state.prev_option = selected_model
    reset_conversation()

# Pull in the model we want to use
repo_id = model_links[selected_model]

st.subheader(f'AI - {selected_model}')

# Set a default model
if selected_model not in st.session_state:
    st.session_state[selected_model] = model_links[selected_model]

# 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(f"Hi I'm {selected_model}, ask me a question"):
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)
    st.session_state.messages.append({"role": "user", "content": prompt})

    # Display assistant response in chat message container
    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=temp_values,
                stream=True,
                max_tokens=3000,
            )
            response = st.write_stream(stream)

        except Exception as e:
            response = "😵‍💫 Looks like something went wrong! Here's a random pic of a 🐶:"
            st.write(response)
            random_dog_pick = 'https://random.dog/' + random_dog[np.random.randint(len(random_dog))]
            st.image(random_dog_pick)
            st.write("This was the error message:")
            st.write(e)

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