import os from langchain_huggingface import HuggingFaceEndpoint import streamlit as st from langchain_core.prompts import PromptTemplate from langchain_core.output_parsers import StrOutputParser model_id="mistralai/Mistral-7B-Instruct-v0.3" def get_llm_hf_inference(model_id=model_id, max_new_tokens=128, 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 # Configure the Streamlit app st.set_page_config(page_title="HuggingFace ChatBot", page_icon="🤗") st.title("Personal HuggingFace ChatBot") st.markdown(f"*This is a simple chatbot that uses the HuggingFace transformers library to generate responses to your text input. It uses the {model_id}.*") # 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 = 256 if "system_message" not in st.session_state: st.session_state.system_message = "friendly AI conversing with a human user" if "starter_message" not in st.session_state: st.session_state.starter_message = "Hello, there! How can I help you today?" # Sidebar for settings with st.sidebar: st.header("System Settings") # AI Settings st.session_state.system_message = st.text_area( "System Message", value="You are a friendly AI conversing with a human user." ) st.session_state.starter_message = st.text_area( 'First AI Message', value="Hello, there! How can I help you today?" ) # 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}] 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: {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)) 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 # Chat interface 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)