Update app.py

app.py

@@ -5,24 +5,22 @@ import pandas as pd
 from typing import TypedDict, Annotated, Sequence
 import operator
 from langchain_core.messages import BaseMessage, HumanMessage
-from langchain.agents import create_tool_calling_agent
-from langchain_core.prompts import ChatPromptTemplate
+from langchain.agents import AgentExecutor, create_react_agent
+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
-from langchain import hub
 
 # --- Main Application Logic ---
 
 # This defines the "memory" or state of our agent.
-# It holds the messages that make up the conversation.
 class AgentState(TypedDict):
     messages: Annotated[Sequence[BaseMessage], operator.add]
 
-# This is the modern way to create a robust agent with LangGraph.
-def create_langgraph_agent():
-    print("Initializing LangGraphAgent...")
+# This function builds our agent using the standard ReAct framework
+def create_agent():
+    print("Initializing ReAct Agent...")
     
     # 1. Set up the LLM (The "Brain")
     llm = HuggingFaceEndpoint(
@@ -38,41 +36,41 @@ def create_langgraph_agent():
     tool_node = ToolNode(tools)
     print("Tools initialized.")
     
-    # 3. Define the agent's logic (the "agent" node)
-    # We create a prompt that tells the model how to use tools
-    prompt = hub.pull("hwchase17/openai-tools-agent")
-    agent = create_tool_calling_agent(llm, tools, prompt)
+    # 3. Get the ReAct Prompt Template
+    # This prompt is designed to work with create_react_agent
+    prompt = hub.pull("hwchase17/react-chat")
+    print("Prompt template pulled: hwchase17/react-chat")
+
+    # 4. Create the agent's logic
+    agent_runnable = create_react_agent(llm, tools, prompt)
     print("Agent logic created.")
 
-    # 4. Define the Graph
+    # 5. Define the Graph
     graph = StateGraph(AgentState)
-    graph.add_node("agent", agent)
+    graph.add_node("agent", lambda state: {"messages": agent_runnable.invoke(state)['messages']})
     graph.add_node("tools", tool_node)
     
     graph.set_entry_point("agent")
 
-    # This function decides where to go after the agent node: to a tool or to the end.
     def should_continue(state):
         last_message = state['messages'][-1]
-        if not last_message.tool_calls:
+        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")
 
-    # 5. Compile the graph into a runnable app
+    # 6. Compile the graph into a runnable app
     app = graph.compile()
     print("LangGraph agent compiled and ready.")
     return app
 
 # This function runs the agent for a single question.
-def run_agent(agent_executor, question: str) -> str:
+def run_agent_for_task(agent_executor, question: str) -> str:
     print(f"Agent received question: {question}")
     try:
-        # We invoke the agent with the question in the correct message format
         response = agent_executor.invoke({"messages": [HumanMessage(content=question)]})
-        # The final answer is in the last message of the output
         final_answer = response['messages'][-1].content
     except Exception as e:
         print(f"Error during agent execution: {e}")
@@ -89,7 +87,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     username = f"{profile.username}"
     
     try:
-        agent_executor = create_langgraph_agent()
+        agent_executor = create_agent()
     except Exception as e:
         return f"Error initializing agent: {e}", None
         
@@ -107,7 +105,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     for item in questions_data:
         task_id, question_text = item.get("task_id"), item.get("question")
         if task_id and question_text:
-            submitted_answer = run_agent(agent_executor, question_text)
+            submitted_answer = run_agent_for_task(agent_executor, question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
@@ -128,7 +126,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         return f"Error during submission: {e}", pd.DataFrame(answers_payload)
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Agent Evaluation Runner (LangGraph Final Version)")
+    gr.Markdown("# Agent Evaluation Runner (Final LangGraph 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)
@@ -136,4 +134,4 @@ with gr.Blocks() as demo:
     run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()