Update app.py

app.py

@@ -1,58 +1,76 @@
+# ------------------------------------------------------------
+# 🔎 LangChain + Groq + Streamlit (Stable Version for Hugging Face)
+# ------------------------------------------------------------
+
 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_core.prompts import PromptTemplate
 from langchain.agents import initialize_agent, AgentType
 from langchain.callbacks import StreamlitCallbackHandler
 from dotenv import load_dotenv
-import os
 
+# ------------------------------------------------------------
+# 🌍 Load environment variables
+# ------------------------------------------------------------
 load_dotenv()
 
-st.title("🔎 LangChain - Chat with Search")
+st.set_page_config(page_title="Search Engine LLM", page_icon="🔎")
+st.title("🔎 LangChain - Chat with Search (Groq + Streamlit)")
 st.sidebar.title("Settings")
+
+# API key input
 api_key = st.sidebar.text_input("Enter your Groq API Key:", type="password")
 
-# ---- Tools ----
-arxiv = ArxivQueryRun(api_wrapper=ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200))
-wiki = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200))
+# ------------------------------------------------------------
+# 🧰 Initialize Tools
+# ------------------------------------------------------------
+arxiv_wrapper = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
+arxiv = ArxivQueryRun(api_wrapper=arxiv_wrapper)
+
+wiki_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200)
+wiki = WikipediaQueryRun(api_wrapper=wiki_wrapper)
+
 search = DuckDuckGoSearchRun(name="Search")
 tools = [search, arxiv, wiki]
 
-# ---- Session ----
+# ------------------------------------------------------------
+# 💬 Session state
+# ------------------------------------------------------------
 if "messages" not in st.session_state:
     st.session_state["messages"] = [
-        {"role": "assistant",
-         "content": "Hi, I'm a chatbot that can search Arxiv, Wikipedia, and the web!"}
+        {"role": "assistant", "content": "Hi 👋 I'm a chatbot that can search Arxiv, Wikipedia, and the web!"}
     ]
 
+# Display previous chat history
 for msg in st.session_state.messages:
     st.chat_message(msg["role"]).write(msg["content"])
 
-# ---- Chat ----
-if user_input := st.chat_input("Ask your question..."):
+# ------------------------------------------------------------
+# 💡 User Input
+# ------------------------------------------------------------
+if user_input := st.chat_input("Ask me anything!"):
     st.session_state.messages.append({"role": "user", "content": user_input})
     st.chat_message("user").write(user_input)
 
-    llm = ChatGroq(groq_api_key=api_key, model_name="Llama3-8b-8192", streaming=True)
-
-    prompt = PromptTemplate.from_template(
-        "You are a helpful assistant with access to the following tools: search, arxiv, and wikipedia.\n"
-        "Use them if needed before answering.\n\nQuestion: {input}"
-    )
-
-    # ✅ Uses the stable zero-shot ReAct agent
-    agent = initialize_agent(
-        tools=tools,
-        llm=llm,
-        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-        verbose=True,
-        handle_parsing_errors=True,
-    )
-
-    with st.chat_message("assistant"):
-        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
-        response = agent.run(user_input, callbacks=[st_cb])
-        st.session_state["messages"].append({"role": "assistant", "content": response})
-        st.write(response)
+    if not api_key:
+        st.error("Please enter your Groq API key in the sidebar before chatting.")
+    else:
+        # Initialize Groq LLM
+        llm = ChatGroq(groq_api_key=api_key, model_name="Llama3-8b-8192", streaming=True)
+
+        # Create the agent (ReAct-style)
+        agent = initialize_agent(
+            tools,
+            llm,
+            agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+            handle_parsing_errors=True,
+            verbose=True
+        )
+
+        # Stream response
+        with st.chat_message("assistant"):
+            st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+            response = agent.run(user_input, callbacks=[st_cb])
+            st.session_state.messages.append({"role": "assistant", "content": response})
+            st.write(response)