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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 datetime import datetime
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
API_TOKEN = os.getenv('HF_INFER_API')
POSTGRE_URL = os.environ['POSTGRE_URL']
@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<|","<|"], temperature=0.001),
# memory=st.session_state.memory.chat_memory,
memory=st.session_state.memory
)
@st.cache_resource
def get_memory():
return PostgresChatMessageHistory(connection_string=POSTGRE_URL, session_id=str(datetime.timestamp(datetime.now())))
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.sessio_state.memory = get_memory()
st.session_state.memory.chat_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("Chat With Me")
st.subheader("by Jonathan Jordan")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = [{"role":"assistant", "content":"Hello, My name is Jonathan Jordan. You can call me Jojo. 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(prompt).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})
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