Update app.py

app.py

@@ -1,54 +1,82 @@
-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
-
-# Used the inbuilt tools of Arxiv and Wikipedia
-api_wrapper_arxiv = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=250)
-arxiv = ArxivQueryRun(api_wrapper=api_wrapper_arxiv)
-
-api_wrapper_wiki = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=250)
-wiki = WikipediaQueryRun(api_wrapper=api_wrapper_wiki)
-
-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":"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})
-    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})
-        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
+
+# Used the inbuilt tools of Arxiv and Wikipedia
+api_wrapper_arxiv = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=250)
+arxiv = ArxivQueryRun(api_wrapper=api_wrapper_arxiv)
+
+api_wrapper_wiki = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=250)
+wiki = WikipediaQueryRun(api_wrapper=api_wrapper_wiki)
+
+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":"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})
+    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})
+        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]
+    )
+
+
+