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Upload chatbot_streamlit.py
Browse files- chatbot_streamlit.py +60 -0
chatbot_streamlit.py
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import streamlit as st
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import pandas as pd
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import numpy as np
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from io import StringIO
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import asyncio
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from langchain.agents import initialize_agent, AgentType
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from langchain_community.callbacks import StreamlitCallbackHandler
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from langchain_community.tools import DuckDuckGoSearchRun
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from langchain_openai import ChatOpenAI
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api_key = st.secrets["OPENAI_API_KEY"]
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with st.sidebar:
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"[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
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"[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/2_Chat_with_search.py)"
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"[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
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st.title("π LangChain - Chat with search")
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"""
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In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
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Try more LangChain π€ Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
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"""
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if "messages" not in st.session_state:
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st.session_state["messages"] = [
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{"role": "assistant", "content": "Hi, I'm a chatbot who is trying to answer your questions"}
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]
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for msg in st.session_state.messages:
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st.chat_message(msg["role"]).write(msg["content"])
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if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"):
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st.session_state.messages.append({"role": "user", "content": prompt})
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st.chat_message("user").write(prompt)
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if not api_key:
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st.info("Please add your OpenAI API key to continue.")
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st.stop()
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llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=api_key, streaming=True)
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Search = DuckDuckGoSearchRun(name="Search")
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# Create a new event loop
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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search_agent = initialize_agent([Search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True)
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with st.chat_message("assistant"):
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st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
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response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
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st.session_state.messages.append({"role": "assistant", "content": response})
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st.write(response)
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