Update app.py

app.py

@@ -1,54 +1,53 @@
-import streamlit as st
-from langchain_groq import ChatGroq
-from langchain_community.utilities import ArxivAPIWrapper,WikipediaAPIWrapper
-from langchain_community.tools import ArxivQueryRun,WikipediaQueryRun,DuckDuckGoSearchRun
-from langchain.agents import initialize_agent,AgentType
-from langchain.callbacks import StreamlitCallbackHandler
-import os
-from dotenv import load_dotenv
-## Code
-####
-
-## Arxiv and wikipedia Tools
-arxiv_wrapper=ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
-arxiv=ArxivQueryRun(api_wrapper=arxiv_wrapper)
-
-api_wrapper=WikipediaAPIWrapper(top_k_results=1,doc_content_chars_max=200)
-wiki=WikipediaQueryRun(api_wrapper=api_wrapper)
-
-search=DuckDuckGoSearchRun(name="Search")
-
-
-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).
-"""
-
-## Sidebar for settings
-st.sidebar.title("Settings")
-api_key=st.sidebar.text_input("Enter your Groq API Key:",type="password")
-
-if "messages" not in st.session_state:
-    st.session_state["messages"]=[
-        {"role":"assisstant","content":"Hi,I'm a chatbot who can search the web. How can I help you?"}
-    ]
-
-for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg['content'])
-
-if prompt:=st.chat_input(placeholder="What is machine learning?"):
-    st.session_state.messages.append({"role":"user","content":prompt})
-    st.chat_message("user").write(prompt)
-
-    llm=ChatGroq(groq_api_key=api_key,model_name="Llama3-8b-8192",streaming=True)
-    tools=[search,arxiv,wiki]
-
-    search_agent=initialize_agent(tools,llm,agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,handling_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)
-
+import streamlit as st
+from langchain_groq import ChatGroq
+from langchain_community.utilities import ArxivAPIWrapper,WikipediaAPIWrapper
+from langchain_community.tools import ArxivQueryRun,WikipediaQueryRun,DuckDuckGoSearchRun
+from langchain.agents import initialize_agent,AgentType
+from langchain.callbacks import StreamlitCallbackHandler
+import os
+from dotenv import load_dotenv
+## Code
+####
+
+## Arxiv and wikipedia Tools
+arxiv_wrapper=ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
+arxiv=ArxivQueryRun(api_wrapper=arxiv_wrapper)
+
+api_wrapper=WikipediaAPIWrapper(top_k_results=1,doc_content_chars_max=200)
+wiki=WikipediaQueryRun(api_wrapper=api_wrapper)
+
+search=DuckDuckGoSearchRun(name="Search")
+
+
+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).
+"""
+
+## Sidebar for settings
+st.sidebar.title("Settings")
+api_key=st.sidebar.text_input("Enter your Groq API Key:",type="password")
+
+if "messages" not in st.session_state:
+    st.session_state["messages"]=[
+        {"role":"assisstant","content":"Hi,I'm a chatbot who can search the web. How can I help you?"}
+    ]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg['content'])
+
+if prompt:=st.chat_input(placeholder="What is machine learning?"):
+    st.session_state.messages.append({"role":"user","content":prompt})
+    st.chat_message("user").write(prompt)
+
+    llm=ChatGroq(groq_api_key=api_key,model_name="Llama3-8b-8192",streaming=True)
+    tools=[search,arxiv,wiki]
+
+    search_agent=initialize_agent(tools,llm,agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,handling_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)