Create app.py

app.py

@@ -0,0 +1,60 @@
+from langchain.agents import ConversationalChatAgent, AgentExecutor
+from langchain.memory import ConversationBufferMemory
+from langchain_community.callbacks import StreamlitCallbackHandler
+from langchain_community.chat_message_histories import StreamlitChatMessageHistory
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_core.runnables import RunnableConfig
+from langchain_openai import ChatOpenAI
+
+import streamlit as st
+
+st.set_page_config(page_title="Ask Your TA", page_icon="🧑‍🔬")
+st.title("‍🔬 Ask Your TA")
+
+openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
+
+msgs = StreamlitChatMessageHistory()
+memory = ConversationBufferMemory(
+    chat_memory=msgs, return_messages=True, memory_key="chat_history", output_key="output"
+)
+if len(msgs.messages) == 0 or st.sidebar.button("Reset chat history"):
+    msgs.clear()
+    msgs.add_ai_message("How can I help you?")
+    st.session_state.steps = {}
+
+avatars = {"human": "user", "ai": "assistant"}
+for idx, msg in enumerate(msgs.messages):
+    with st.chat_message(avatars[msg.type]):
+        # Render intermediate steps if any were saved
+        for step in st.session_state.steps.get(str(idx), []):
+            if step[0].tool == "_Exception":
+                continue
+            with st.status(f"**{step[0].tool}**: {step[0].tool_input}", state="complete"):
+                st.write(step[0].log)
+                st.write(step[1])
+        st.write(msg.content)
+
+if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"):
+    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)
+    tools = [DuckDuckGoSearchRun(name="Search")]
+    chat_agent = ConversationalChatAgent.from_llm_and_tools(llm=llm, tools=tools)
+    executor = AgentExecutor.from_agent_and_tools(
+        agent=chat_agent,
+        tools=tools,
+        memory=memory,
+        return_intermediate_steps=True,
+        handle_parsing_errors=True,
+    )
+    with st.chat_message("assistant"):
+        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+        cfg = RunnableConfig()
+        cfg["callbacks"] = [st_cb]
+        response = executor.invoke(prompt, cfg)
+        st.write(response["output"])
+        st.session_state.steps[str(len(msgs.messages) - 1)] = response["intermediate_steps"]