app.py

""" HAH 2024 v0.1 Chatbot
@author: Dr M As'ad
@email: [email protected]
"""

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')
) 

#Create supported models
model_links ={
    "HAH-2024-v0.1":"drmasad/HAH-2024-v0.11",
    "Mistral":"mistralai/Mistral-7B-Instruct-v0.2",
}

model_info ={
    "HAH-2024-v0.1":
        {'description':"""The HAH-2024-v0.1 model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nIt was created by fine tuning Mistra 7b instruct usin 3000 review articles on diabetes** \n""",
        'logo':'https://www.hmgaihub.com/untitled.png'},
    "Mistral":
    {'description':
        """The Mistral model is a **Large Language Model (LLM)** that's able to have question and answer interactions...""",
    'logo':
        'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'},
}

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)

#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) #Reset button


# 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.write(f"Changed to {selected_model}")
    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}')
# st.title(f'ChatBot Using {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)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})


    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        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,#0.5,
            stream=True,
            max_tokens=3000,
        )

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