Update app.py

app.py

@@ -80,8 +80,8 @@ def tool_node(state: AgentState, tool_request: dict) -> AgentState:
     # 3.a) Run the actual ToolNode on that dict:
     result_text = underlying_tool_node.run(tool_request)
 
-    # 3.b) Update state.messages to note the tool’s output,
-    #      and clear tool_request so we don’t loop.
+    # 3.b) Update state.messages to note the tool's output,
+    #      and clear tool_request so we don't loop.
     return {
         "messages": [f"TOOL ({tool_request['tool']}): {result_text}"],
         "tool_request": None,
@@ -93,13 +93,13 @@ graph = StateGraph(AgentState)
 graph.add_node("agent", agent_node)
 graph.add_node("tools", tool_node)
 
-# 5) Simple START → “agent” edge (no third argument needed)
+# 5) Simple START → "agent" edge (no third argument needed)
 graph.add_edge(START, "agent")
 
-# 6) Simple “tools” → “agent” edge (again, no third argument)
+# 6) Simple "tools" → "agent" edge (again, no third argument)
 graph.add_edge("tools", "agent")
 
-# 7) Conditional branching out of “agent,” exactly like the tutorial
+# 7) Conditional branching out of "agent," exactly like the tutorial
 def route_agent(state: AgentState, agent_out):
     """
     When the LLM (agent_node) runs, it returns an AgentState where
@@ -130,8 +130,8 @@ compiled_graph = graph.compile()
 # 9) Define respond_to_input so that Gradio (and the Hugging Face submission) can call it
 def respond_to_input(user_input: str) -> str:
     initial_state: AgentState = {"messages": [], "tool_request": None, "tool_result": None}
-    # ✔️ In v0.4.7 (and 0.3.x+), you must use .run():
-    final_state = compiled_graph.run(initial_state, user_input)
+    # ✔️ use .invoke() in v0.4.7
+    final_state = compiled_graph.invoke(initial_state, user_input)
     # Return the last assistant message
     last = final_state["messages"][-1]
     return last.replace("ASSISTANT: ", "")