import streamlit as st # from langchain_community.llms import HuggingFaceTextGenInference import os from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.schema import StrOutputParser from custom_llm import CustomLLM, custom_chain_with_history API_TOKEN = os.getenv('HF_INFER_API') from typing import Optional from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_community.chat_models import ChatAnthropic from langchain_core.chat_history import BaseChatMessageHistory from langchain.memory import ConversationBufferMemory from langchain_core.runnables.history import RunnableWithMessageHistory @st.cache_resource def get_llm_chain(): return custom_chain_with_history(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), memory=st.session_state.memory) if 'memory' not in st.session_state: st.session_state['memory'] = ConversationBufferMemory(return_messages=True) st.session_state.memory.chat_memory.add_ai_message("Hello there! I'm AI assistant of Lintas Media Danawa. How can I help you today?") if 'chain' not in st.session_state: # st.session_state['chain'] = custom_chain_with_history(llm=CustomLLM(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"]), memory=st.session_state.memory) st.session_state['chain'] = get_llm_chain() # st.session_state['chain'] = custom_chain_with_history(llm=InferenceClient("https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1", headers = {"Authorization": f"Bearer {API_TOKEN}"}, stream=True, max_new_tokens=512, temperature=0.01), memory=st.session_state.memory) st.title("LMD Chatbot V2 Sample") st.subheader("Knowledge-base from web scrapping and FAQ") # Initialize chat history if "messages" not in st.session_state: st.session_state.messages = [{"role":"assistant", "content":"Hello there! I'm AI assistant of Lintas Media Danawa. How can I help you today?"}] # 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"]) # React to user input if prompt := st.chat_input("Ask me anything.."): # Display user message in chat message container st.chat_message("User").markdown(prompt) # Add user message to chat history st.session_state.messages.append({"role": "User", "content": prompt}) # full_response = st.session_state.chain.invoke(prompt).split("\n<|")[0] full_response = st.session_state.chain.invoke({"question":prompt, "memory":st.session_state.memory}).split("\n<|")[0] with st.chat_message("assistant"): st.markdown(full_response) # Display assistant response in chat message container # with st.chat_message("assistant"): # message_placeholder = st.empty() # full_response = "" # for chunk in st.session_state.chain.stream(prompt): # full_response += chunk + " " # message_placeholder.markdown(full_response + " ") # if full_response[-4:] == "\n<|": # break # st.markdown(full_response) st.session_state.memory.save_context({"question":prompt}, {"output":full_response}) st.session_state.memory.chat_memory.messages = st.session_state.memory.chat_memory.messages[-15:] # Add assistant response to chat history st.session_state.messages.append({"role": "assistant", "content": full_response})