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')#"hf_xxx" # Replace with your token ) #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", } #Pull info about the model to display 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.\n \ \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 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.\n \ \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-7b/) team as has over **7 billion parameters.** \n""", 'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'}, } #Random dog images for error message random_dog = ["BlueLogoBox.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']) 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'Demo Chatbot') # 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"Liahona.AI powered by {selected_model}."): # 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: # st.empty() response = "😵‍💫 Looks like someone unplugged something!\ \n Either the model space is being updated or something is down.\ \n\ \n Try again later. \ \n\ \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})