Upload chatbot_streamlit.py

chatbot_streamlit.py

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+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_community.callbacks import StreamlitCallbackHandler
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_openai import ChatOpenAI
+
+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 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=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)