app.py

import streamlit as st
import pandas as pd
import numpy as np
from io import StringIO
import asyncio


from langchain.agents import initialize_agent, AgentType
from langchain.callbacks import StreamlitCallbackHandler
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_openai import ChatOpenAI
from langchain_community.callbacks import StreamlitCallbackHandler


openai_api_key = st.secrets["OPENAI_API_KEY"]


with st.sidebar:
    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/2_Chat_with_search.py)"
    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"

st.title("🔎 LangChain - Chat with search")

"""
In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
"""

if "messages" not in st.session_state:
    st.session_state["messages"] = [
        {"role": "assistant", "content": "Hi, I'm a chatbot who is trying to answer your questions"}
    ]

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"):
    st.session_state.messages.append({"role": "user", "content": prompt})
    st.chat_message("user").write(prompt)

    if not openai_api_key:
        st.info("Please add your OpenAI API key to continue.")
        st.stop()

    llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=openai_api_key, streaming=True)
    Search = DuckDuckGoSearchRun(name="Search")
    
    # Create a new event loop
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    
    search_agent = initialize_agent([Search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True)
 
  
 
 
    with st.chat_message("assistant"):
        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
        response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
        st.session_state.messages.append({"role": "assistant", "content": response})
        st.write(response)