Update app.py

app.py

@@ -36,7 +36,19 @@ llm = ChatOpenAI(model_name="gpt-4.1-mini")
 # excel_tool_node = ToolNode([parse_excel])
 
 # image_tool_node = ToolNode([ocr_image])
-tool_node = ToolNode([ocr_image, parse_excel, web_search])
+t_node = ToolNode([ocr_image, parse_excel, web_search])
+
+def tool_node(state: AgentState, agent_output)-> AgentState:
+    """
+    Wrap ToolNode so it matches (state, agent_output) → next_state.
+    `agent_output` is expected to be a dict like {"tool": "...", ...}.
+    """
+    # Let the ToolNode run with that dict (ToolNode.run(dict) returns a string)
+    tool_result: str = tool_node.run(agent_output)
+    # Now you might want the LLM to reason over tool_result, but for simplicity...
+    # We'll just store tool_result in messages.
+    return {"messages": [tool_result]}
+
 
 # 5) Build the StateGraph
 graph = StateGraph(AgentState)
@@ -48,16 +60,14 @@ graph.add_node("tools", tool_node)
 #    Wrap the user_input into state["messages"]
 graph.add_edge(
     START,
-    "agent",
-    lambda state, user_input: {"messages": [user_input]}
+    "agent"
 )
 
 # Edge C: "tools" → "agent"
 #    Whatever string the tool returns becomes the next prompt to the LLM
 graph.add_edge(
     "tools",
-    "agent",
-    lambda state, tool_output: tool_output
+    "agent"
 )
 
 # 7) Use add_conditional_edges out of "agent" instead of two separate edges