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import pandas as pd
from os import environ
import datetime
import streamlit as st
from langchain.schema import HumanMessage, FunctionMessage
from helper import build_agents
from login import back_to_main
environ['OPENAI_API_BASE'] = st.secrets['OPENAI_API_BASE']
def on_chat_submit():
ret = st.session_state.agents[st.session_state.sel][st.session_state.ret_type]({"input": st.session_state.chat_input})
print(ret)
def clear_history():
st.session_state.agents[st.session_state.sel][st.session_state.ret_type].memory.clear()
def back_to_main():
if "user_info" in st.session_state:
del st.session_state.user_info
if "user_name" in st.session_state:
del st.session_state.user_name
if "jump_query_ask" in st.session_state:
del st.session_state.jump_query_ask
def chat_page():
st.session_state["agents"] = build_agents(f"{st.session_state.user_name}?default")
with st.sidebar:
st.radio("Retriever Type", ["Self-querying retriever", "Vector SQL"], key="ret_type")
st.selectbox("Knowledge Base", ["ArXiv Papers", "Wikipedia", "ArXiv + Wikipedia"], key="sel")
st.button("Clear Chat History", on_click=clear_history)
st.button("Logout", on_click=back_to_main)
for msg in st.session_state.agents[st.session_state.sel][st.session_state.ret_type].memory.chat_memory.messages:
speaker = "user" if isinstance(msg, HumanMessage) else "assistant"
if isinstance(msg, FunctionMessage):
with st.chat_message("Knowledge Base", avatar="📖"):
print(type(msg.content))
st.write(f"*{datetime.datetime.fromtimestamp(msg.additional_kwargs['timestamp']).isoformat()}*")
st.write("Retrieved from knowledge base:")
try:
st.dataframe(pd.DataFrame.from_records(map(dict, eval(msg.content))))
except:
st.write(msg.content)
else:
if len(msg.content) > 0:
with st.chat_message(speaker):
print(type(msg), msg.dict())
st.write(f"*{datetime.datetime.fromtimestamp(msg.additional_kwargs['timestamp']).isoformat()}*")
st.write(f"{msg.content}")
st.chat_input("Input Message", on_submit=on_chat_submit, key="chat_input")
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