Update app.py

app.py

@@ -1,23 +1,19 @@
 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 create_react_agent, AgentExecutor
+from langchain import hub
 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)
+## 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")
+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")
 """
@@ -27,27 +23,95 @@ Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/str
 
 ## 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")
+
+# Add clear chat button
+if st.sidebar.button("Clear Chat History"):
+    st.session_state.messages = [
+        {"role": "assistant", "content": "Hi, I'm a chatbot who can search the web. How can I help you?"}
+    ]
+    st.rerun()
 
+# Initialize session state
 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 chat messages
 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})
+# Chat input
+if prompt := st.chat_input(placeholder="What is machine learning?"):
+    # Check if API key is provided
+    if not api_key:
+        st.error("Please enter your Groq API Key in the sidebar.")
+        st.stop()
+    
+    # Add user message to chat
+    st.session_state.messages.append({"role": "user", "content": prompt})
     st.chat_message("user").write(prompt)
+    
+    # Initialize LLM and tools
+    llm = ChatGroq(groq_api_key=api_key, model_name="Llama3-8b-8192", streaming=True)
+    tools = [search, arxiv, wiki]
+    
+    # Get the react prompt template
+    try:
+        react_prompt = hub.pull("hwchase17/react")
+    except:
+        # Fallback prompt if hub is not accessible
+        from langchain.prompts import PromptTemplate
+        template = """Answer the following questions as best you can. You have access to the following tools:
+
+{tools}
+
+Use the following format:
 
-    llm=ChatGroq(groq_api_key=api_key,model_name="Llama3-8b-8192",streaming=True)
-    tools=[search,arxiv,wiki]
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: the action to take, should be one of [{tool_names}]
+Action Input: the input to the action
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can repeat N times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
 
-    search_agent=initialize_agent(tools,llm,agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,handling_parsing_errors=True)
+Begin!
 
+Question: {input}
+Thought:{agent_scratchpad}"""
+        
+        react_prompt = PromptTemplate(
+            template=template,
+            input_variables=["input", "tools", "tool_names", "agent_scratchpad"]
+        )
+    
+    # Create agent
+    agent = create_react_agent(llm, tools, react_prompt)
+    agent_executor = AgentExecutor(
+        agent=agent,
+        tools=tools,
+        verbose=True,
+        handle_parsing_errors=True,
+        max_iterations=5
+    )
+    
+    # Generate response
     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)
+        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+        try:
+            response = agent_executor.invoke(
+                {"input": prompt},
+                {"callbacks": [st_cb]}
+            )
+            answer = response['output']
+            st.session_state.messages.append({'role': 'assistant', "content": answer})
+            st.write(answer)
+        except Exception as e:
+            st.error(f"An error occurred: {str(e)}")
+            st.session_state.messages.append({
+                'role': 'assistant',
+                "content": f"Sorry, I encountered an error: {str(e)}"
+            })