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()

# Comment_test_11_09_2024

# 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 models
model_links = {
    "Meta-Llama-3-8B": "meta-llama/Meta-Llama-3-8B-Instruct",
    "Mistral-7B": "mistralai/Mistral-7B-Instruct-v0.2",
    "Gemma-7B": "google/gemma-1.1-7b-it",
    "Gemma-2B": "google/gemma-1.1-2b-it",
    "Zephyr-7B-β": "HuggingFaceH4/zephyr-7b-beta",
    # "Meta-Llama-3.1-8B": "meta-llama/Meta-Llama-3.1-8B-Instruct",  # TODO: Update when/if Serverless Inference available
}

# Pull info about the model to display
model_info = {
    "Mistral-7B": {
        'description': """The Mistral model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nIt was created by the Mistral AI team and has over **7 billion parameters.** \n"""
    },
    "Gemma-7B": {
        'description': """The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nIt was created by Google's AI Team and has over **7 billion parameters.** \n"""
    },
    "Gemma-2B": {
        'description': """The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nIt was created by Google's AI Team and has over **2 billion parameters.** \n"""
    },
    "Zephyr-7B": {
        'description': """The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nFrom Huggingface: \n\
            Zephyr is a series of language models that are trained to act as helpful assistants. \
            Zephyr 7B is the third model in the series, and is a fine-tuned version of google/gemma-7b that was trained on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n"""
    },
    "Zephyr-7B-β": {
        'description': """The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nFrom Huggingface: \n\
            Zephyr is a series of language models that are trained to act as helpful assistants. \
            Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n"""
    },
    "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 Meta's AI team and has over **8 billion parameters.** \n"""
    },
    "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.\n \
            \nIt was created by Meta's AI team and has over **8 billion parameters.** \n"""
    },
}

# Random dog images for error message
random_dog = [
    "0f476473-2d8b-415e-b944-483768418a95.jpg",
    "1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg",
    "526590d2-8817-4ff0-8c62-fdcba5306d02.jpg",
    "1326984c-39b0-492c-a773-f120d747a7e2.jpg",
    "42a98d03-5ed7-4b3b-af89-7c4876cb14c3.jpg",
    "8b3317ed-2083-42ac-a575-7ae45f9fdc0d.jpg",
    "ee17f54a-83ac-44a3-8a35-e89ff7153fb4.jpg",
    "027eef85-ccc1-4a66-8967-5d74f34c8bb4.jpg",
    "08f5398d-7f89-47da-a5cd-1ed74967dc1f.jpg",
    "0fd781ff-ec46-4bdc-a4e8-24f18bf07def.jpg",
    "0fb4aeee-f949-4c7b-a6d8-05bf0736bdd1.jpg",
    "6edac66e-c0de-4e69-a9d6-b2e6f6f9001b.jpg",
    "bfb9e165-c643-4993-9b3a-7e73571672a6.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)

# 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'])

# Only display the logo if it exists
if 'logo' in model_info[selected_model]:
    st.sidebar.image(model_info[selected_model]['logo'])

st.sidebar.markdown("*Generated content may be inaccurate or false.*")
st.sidebar.markdown("\nFor More Visit **Womener AI**")
st.sidebar.markdown("\nRun into issues? \nTry coming back in a bit, GPU access might be limited or something is down.")

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)
    # 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"):

        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,  # 0.5,
                stream=True,
                max_tokens=3000,
            )

            response = st.write_stream(stream)

        except Exception as e:
            response = "😵‍💫 Looks like someone unplugged something! \
                        \n Either the model space is being updated or something is down. \
                        \n Try again later. \
                        \n 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})