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| 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 |