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| import streamlit as st | |
| import os | |
| from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| from langchain.schema import StrOutputParser | |
| # from datetime import datetime | |
| from datetime import datetime, timezone, timedelta | |
| from custom_llm import CustomLLM, custom_chain_with_history | |
| from typing import Optional | |
| from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder | |
| from langchain_core.chat_history import BaseChatMessageHistory | |
| from langchain.memory import ConversationBufferMemory#, PostgresChatMessageHistory | |
| import urllib.parse as up | |
| # os.environ['LANGCHAIN_TRACING_V2'] = "true" | |
| API_TOKEN = os.getenv('HF_INFER_API') | |
| def get_llm_chain(): | |
| return custom_chain_with_history( | |
| # llm=CustomLLM(repo_id="AdaptLLM/medicine-chat", model_type='text-generation', api_token=API_TOKEN, temperature=0.001), | |
| llm=CustomLLM(repo_id="google/gemma-7b", model_type='text-generation', api_token=API_TOKEN, stop=["\n<|","<|"], temperature=0.001), | |
| # memory=st.session_state.memory.chat_memory, | |
| memory=st.session_state.memory | |
| ) | |
| if 'memory' not in st.session_state: | |
| st.session_state['memory'] = ConversationBufferMemory(return_messages=True) | |
| # st.session_state.memory = PostgresChatMessageHistory(connection_string=POSTGRE_URL, session_id=str(datetime.timestamp(datetime.now()))) | |
| # st.session_state.memory = get_memory() | |
| st.session_state.memory.chat_memory.add_ai_message("Hello, I'm AI medical consultant. How can I help you today?") | |
| # st.session_state.memory.add_ai_message("Hello, My name is Jonathan Jordan. You can call me Jojo. 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<|","<|"], temperature=0.001), | |
| # memory=st.session_state.memory.chat_memory, | |
| # # memory=st.session_state.memory | |
| # ) | |
| st.session_state['chain'] = get_llm_chain() | |
| st.title("AI Medical Consultation") | |
| st.subheader("") | |
| # Initialize chat history | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [{"role":"assistant", "content":"Hello, I'm AI medical consultant. 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}) | |
| response = st.session_state.chain.invoke({"question":prompt, "memory":st.session_state.memory}).split("\n<|")[0] | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| st.markdown(response) | |
| # st.session_state.memory.add_user_message(prompt) | |
| # st.session_state.memory.add_ai_message(response) | |
| st.session_state.memory.save_context({"question":prompt}, {"output":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": response}) |