import os from langchain_huggingface import HuggingFaceEndpoint import streamlit as st model_id="mistralai/Mistral-7B-Instruct-v0.3" def get_llm_hf_inference(model_id=model_id, max_new_tokens=4096, temperature=0.1): """ Returns a language model for HuggingFace inference. Parameters: - model_id (str): The ID of the HuggingFace model repository. - max_new_tokens (int): The maximum number of new tokens to generate. - temperature (float): The temperature for sampling from the model. Returns: - llm (HuggingFaceEndpoint): The language model for HuggingFace inference. """ llm = HuggingFaceEndpoint( repo_id=model_id, max_new_tokens=max_new_tokens, temperature=temperature, token = os.getenv("HF_TOKEN") ) return llm import streamlit as st # Configure the Streamlit app st.set_page_config(page_title="Tadeusz ChatBot For Fun", page_icon="🤖", layout="wide") st.title("HAL") st.markdown("*This is a simple and hopefully fun chatbot. It uses the meta-llama/Meta-Llama-3.1-8B-Instruct. It is based on https://medium.com/@james.irving.phd/creating-your-personal-chatbot-using-hugging-face-spaces-and-streamlit-596a54b9e3ed*") # Initialize session state for avatars if "avatars" not in st.session_state: st.session_state.avatars = {'user': None, 'assistant': None} # Initialize session state for user text input if 'user_text' not in st.session_state: st.session_state.user_text = None # Initialize session state for model parameters if "max_response_length" not in st.session_state: st.session_state.max_response_length = 4096 if "system_message" not in st.session_state: st.session_state.system_message = "rude AI conversing with a human user" if "starter_message" not in st.session_state: st.session_state.starter_message = "What do you want?" # Sidebar for settings with st.sidebar: st.header("System Settings") # # AI Settings # st.session_state.system_message = st.text_input( # "System Message", value="rude AI conversing with a human user" # ) # st.session_state.starter_message = st.text_area( # 'First AI Message', value="What" # ) # # Model Settings # st.session_state.max_response_length = st.number_input( # "Max Response Length", value=128 # ) # Avatar Selection st.markdown("*Select Avatars:*") col1, col2 = st.columns(2) with col1: st.session_state.avatars['assistant'] = st.selectbox( "AI Avatar", options=["👺", "🤗", "💬", "🤖"], index=0 ) with col2: st.session_state.avatars['user'] = st.selectbox( "User Avatar", options=["👤", "👱", "👨🏾", "👩", "👧🏾"], index=0 ) # Reset Chat History reset_history = st.button("Reset Chat History") # Initialize or reset chat history if "chat_history" not in st.session_state or reset_history: st.session_state.chat_history = [{"role": "assistant", "content": st.session_state.starter_message}] from langchain_core.prompts import PromptTemplate from langchain_core.output_parsers import StrOutputParser def get_response(system_message, chat_history, user_text, eos_token_id=['User'], max_new_tokens=256, get_llm_hf_kws={}): """ Generates a response from the chatbot model. Args: system_message (str): The system message for the conversation. chat_history (list): The list of previous chat messages. user_text (str): The user's input text. model_id (str, optional): The ID of the HuggingFace model to use. eos_token_id (list, optional): The list of end-of-sentence token IDs. max_new_tokens (int, optional): The maximum number of new tokens to generate. get_llm_hf_kws (dict, optional): Additional keyword arguments for the get_llm_hf function. Returns: tuple: A tuple containing the generated response and the updated chat history. """ # Set up the model hf = get_llm_hf_inference(max_new_tokens=max_new_tokens, temperature=0.1) # Create the prompt template prompt = PromptTemplate.from_template( ( "[INST] {system_message}" "\nCurrent Conversation:\n{chat_history}\n\n" "\nUser: Please be as rude as you can when you answer. {user_text}.\n [/INST]" "\nAI:" ) ) # Make the chain and bind the prompt chat = prompt | hf.bind(skip_prompt=True) | StrOutputParser(output_key='content') # Generate the response response = chat.invoke( input=dict(system_message=system_message, user_text=user_text, chat_history=chat_history) ) # Only keep the newly generated response response = response.split("AI:")[-1] # Update the chat history chat_history.append({'role': 'user', 'content': user_text}) chat_history.append({'role': 'assistant', 'content': response}) return response, chat_history # Container for chat messages chat_interface = st.container(border=True) with chat_interface: output_container = st.container() st.session_state.user_text = st.chat_input(placeholder="Enter your text here.") # Display chat messages with output_container: # For every message in the history for message in st.session_state.chat_history: # Skip the system message if message['role'] == 'system': continue # Display the chat message using the correct avatar with st.chat_message(message['role'], avatar=st.session_state['avatars'][message['role']]): st.markdown(message['content']) # When the user enter new text: if st.session_state.user_text: # Display the user's new message immediately with st.chat_message("user", avatar=st.session_state.avatars['user']): st.markdown(st.session_state.user_text) # Display a spinner status bar while waiting for the response with st.chat_message("assistant", avatar=st.session_state.avatars['assistant']): with st.spinner("Thinking..."): # Call the Inference API with the system_prompt, user text, and history response, st.session_state.chat_history = get_response( system_message=st.session_state.system_message, user_text=st.session_state.user_text, chat_history=st.session_state.chat_history, max_new_tokens=st.session_state.max_response_length, ) st.markdown(response) # public-chat-interface