Update app.py

app.py

@@ -2,24 +2,17 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-from typing import TypedDict, Annotated, Sequence
-import operator
-from langchain_core.messages import BaseMessage, HumanMessage
-from langchain.agents import AgentExecutor, create_react_agent
+from typing import TypedDict, Annotated
+from langchain_core.messages import BaseMessage
+from langchain.agents import create_react_agent, AgentExecutor
 from langchain import hub
 from langchain_community.tools import DuckDuckGoSearchRun
 from langchain_huggingface import HuggingFaceEndpoint
-from langgraph.graph import StateGraph, END
-from langgraph.prebuilt import ToolNode
 
 # --- Main Application Logic ---
 
-# This defines the "memory" or state of our agent.
-class AgentState(TypedDict):
-    messages: Annotated[Sequence[BaseMessage], operator.add]
-
-# This function builds our agent using the standard ReAct framework
-def create_agent():
+# This function builds our agent and the executor that runs it.
+def create_agent_executor():
     print("Initializing ReAct Agent...")
     
     # 1. Set up the LLM (The "Brain")
@@ -33,45 +26,41 @@ def create_agent():
 
     # 2. Define the Tools
     tools = [DuckDuckGoSearchRun()]
-    tool_node = ToolNode(tools)
     print("Tools initialized.")
     
     # 3. Get the ReAct Prompt Template
-    # This prompt is designed to work with create_react_agent
+    # This prompt is specifically designed to make models think step-by-step
     prompt = hub.pull("hwchase17/react-chat")
-    print("Prompt template pulled: hwchase17/react-chat")
+    print("Prompt template pulled.")
 
-    # 4. Create the agent's logic
+    # 4. Create the Agent's logic using the ReAct framework
     agent_runnable = create_react_agent(llm, tools, prompt)
     print("Agent logic created.")
 
-    # 5. Define the Graph
-    graph = StateGraph(AgentState)
-    graph.add_node("agent", lambda state: {"messages": agent_runnable.invoke(state)['messages']})
-    graph.add_node("tools", tool_node)
-    
-    graph.set_entry_point("agent")
-
-    def should_continue(state):
-        last_message = state['messages'][-1]
-        if not hasattr(last_message, 'tool_calls') or not last_message.tool_calls:
-            return END
-        return "tools"
-
-    graph.add_conditional_edges("agent", should_continue)
-    graph.add_edge("tools", "agent")
-
-    # 6. Compile the graph into a runnable app
-    app = graph.compile()
-    print("LangGraph agent compiled and ready.")
-    return app
+    # 5. Create the Agent Executor
+    # This is the runtime that will loop through the agent's thoughts and tool uses.
+    # We are going back to the standard AgentExecutor, as the LangGraph build was overly complex
+    # and causing the new errors.
+    agent_executor = AgentExecutor(
+        agent=agent_runnable,
+        tools=tools,
+        verbose=True,
+        handle_parsing_errors=True,
+        max_iterations=5,
+    )
+    print("Agent Executor created. Initialization complete.")
+    return agent_executor
 
 # This function runs the agent for a single question.
 def run_agent_for_task(agent_executor, question: str) -> str:
     print(f"Agent received question: {question}")
     try:
-        response = agent_executor.invoke({"messages": [HumanMessage(content=question)]})
-        final_answer = response['messages'][-1].content
+        # For this agent, the input is a dictionary with "input" and an empty "chat_history"
+        response = agent_executor.invoke({
+            "input": question,
+            "chat_history": []
+        })
+        final_answer = response.get("output", "Error: Could not parse final answer.")
     except Exception as e:
         print(f"Error during agent execution: {e}")
         final_answer = f"Error: Agent failed to execute. {e}"
@@ -79,7 +68,7 @@ def run_agent_for_task(agent_executor, question: str) -> str:
     print(f"Agent returning answer: {final_answer}")
     return str(final_answer)
 
-# The rest of the file remains largely the same as the original template
+# The rest of the file runs the evaluation and is mostly unchanged from the template.
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
     if not profile:
@@ -87,7 +76,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     username = f"{profile.username}"
     
     try:
-        agent_executor = create_agent()
+        agent_executor = create_agent_executor()
     except Exception as e:
         return f"Error initializing agent: {e}", None
         
@@ -125,8 +114,9 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     except Exception as e:
         return f"Error during submission: {e}", pd.DataFrame(answers_payload)
 
+# --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Agent Evaluation Runner (Final LangGraph Version)")
+    gr.Markdown("# Agent Evaluation Runner (Final ReAct Version)")
     gr.LoginButton()
     run_button = gr.Button("Run Evaluation & Submit All Answers")
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)