Update app.py

app.py

@@ -7,7 +7,10 @@ from langchain.callbacks import StreamlitCallbackHandler
 import os
 from dotenv import load_dotenv
 
-# Used the inbuilt tools of Arxiv and Wikipedia
+# --- Load API keys (optional if you're using a .env) ---
+load_dotenv()
+
+# --- Tool setup ---
 api_wrapper_arxiv = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=250)
 arxiv = ArxivQueryRun(api_wrapper=api_wrapper_arxiv)
 
@@ -15,68 +18,39 @@ api_wrapper_wiki = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=25
 wiki = WikipediaQueryRun(api_wrapper=api_wrapper_wiki)
 
 search = DuckDuckGoSearchRun(name="Search")
+tools = [search, arxiv, wiki]
+
+# --- Model setup ---
+api_key = os.getenv("GROQ_API_KEY", "")  # or let user input via Streamlit
+llm = ChatGroq(groq_api_key=api_key, model_name="Llama3-8b-8192", streaming=True)
+search_agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True)
+
+# -------- GAIA REQUIRED FUNCTION --------
+def run_agent(query: str) -> str:
+    """GAIA-compatible agent runner."""
+    return search_agent.run(query)
 
+# -------- Streamlit App --------
 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")
+user_api_key = st.sidebar.text_input("Enter your Groq API Key:", type="password")
+if user_api_key:
+    llm.groq_api_key = user_api_key
 
 if "messages" not in st.session_state:
-    st.session_state["messages"] = [
-        {"role":"assistant", "content":"Hi, I am a Chatbot who can search the web. How can I help you ?"}
-    ]
+    st.session_state["messages"] = [{"role": "assistant", "content": "Hi, I am 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})
+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, handle_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})
+        response = run_agent(prompt)
+        st.session_state.messages.append({"role": "assistant", "content": response})
         st.write(response)
-    
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-