Update app.py

app.py

@@ -1,54 +1,76 @@
 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_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
-## COMMIT
 
-## Arxiv and wikipedia Tools
-arxiv_wrapper=ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
-arxiv=ArxivQueryRun(api_wrapper=arxiv_wrapper)
+# Load environment variables
+load_dotenv()
 
-api_wrapper=WikipediaAPIWrapper(top_k_results=1,doc_content_chars_max=200)
-wiki=WikipediaQueryRun(api_wrapper=api_wrapper)
+## Arxiv and Wikipedia Tools
+arxiv_wrapper = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
+arxiv = ArxivQueryRun(api_wrapper=arxiv_wrapper)
 
-search=DuckDuckGoSearchRun(name="Search")
+wiki_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200)
+wiki = WikipediaQueryRun(api_wrapper=wiki_wrapper)
 
+search = DuckDuckGoSearchRun(name="Search")
 
+# Streamlit UI
 st.title("🔎 LangChain - Chat with search")
-"""
+st.markdown("""
 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
+# Sidebar for settings
 st.sidebar.title("Settings")
-api_key=st.sidebar.text_input("Enter your Groq API Key:",type="password")
+api_key = st.sidebar.text_input("Enter your Groq API Key:", type="password")
 
+# Initialize session state for messages if not already
 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?"}
+    st.session_state["messages"] = [
+        {"role": "assistant", "content": "Hi, I'm a chatbot who can search the web. How can I help you?"}
     ]
 
+# Display previous chat messages
 for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg['content'])
+    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})
+# Capture user input from chat
+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]
+    # Initialize the language model
+    if api_key:  # Ensure API key is entered
+        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)
+        # Initialize the search agent with tools and the language model
+        search_agent = initialize_agent(
+            tools,
+            llm,
+            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+            handle_parsing_errors=True  # Enable error handling for parsing issues
+        )
 
+        with st.chat_message("assistant"):
+            st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+            
+            try:
+                # Try running the agent
+                response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
+                st.session_state.messages.append({'role': 'assistant', "content": response})
+                st.write(response)
+            
+            except ValueError as e:
+                # Catch and display output parsing errors
+                st.error(f"An error occurred while parsing the LLM's output: {str(e)}")
+                st.session_state.messages.append({'role': 'assistant', "content": "Sorry, I encountered an error processing your request."})
+                st.write("Sorry, I encountered an error processing your request.")
+    else:
+        st.error("Please enter your Groq API Key in the settings.")