Update app.py

app.py

@@ -787,75 +787,75 @@ Your goal: Provide the EXACT answer in the EXACT format requested.
         raise
     # --- *** END UPDATE *** ---
     
-    # --- Node 1: The Agent ---
-    def agent_node(state: AgentState):
-        current_turn = state.get('turn', 0) + 1
-        print(f"\n{'='*60}")
-        print(f"AGENT TURN {current_turn}/{MAX_TURNS}")
-        print('='*60)
+        # --- Node 1: The Agent ---
+        def agent_node(state: AgentState):
+            current_turn = state.get('turn', 0) + 1
+            print(f"\n{'='*60}")
+            print(f"AGENT TURN {current_turn}/{MAX_TURNS}")
+            print('='*60)
+            
+            # Note: Max turns is also checked in should_continue, but good to have here
+            if current_turn > MAX_TURNS:
+                return {"messages": [SystemMessage(content="Max turns reached.")]}
+    
+            max_retries = 3
+            ai_message = None
+            for attempt in range(max_retries):
+                try:
+                    # Call the single, powerful LLM
+                    ai_message = self.llm_with_tools.invoke(state["messages"])
+                    break
+                except Exception as e:
+                    print(f"⚠️ LLM attempt {attempt+1}/{max_retries} failed: {e}")
+                    if attempt == max_retries - 1:
+                        ai_message = AIMessage(
+                            content=f"Error: LLM failed after {max_retries} attempts: {e}"
+                        )
+                    time.sleep(2 ** attempt)
+            
+            # --- Fallback Parsing Logic ---
+            if not ai_message.tool_calls and isinstance(ai_message.content, str) and ai_message.content.strip():
+                parsed_tool_calls = parse_tool_call_from_string(ai_message.content, self.tools)
+                if parsed_tool_calls:
+                    print("🔧 Fallback SUCCESS: Rebuilding tool call(s).")
+                    ai_message.tool_calls = parsed_tool_calls
+                    ai_message.content = "" # Clear the text content
+                else:
+                    print(f"⚠️ Fallback FAILED: Could not parse any tool call from content:\n{ai_message.content[:200]}...")
+            
+            if ai_message.tool_calls:
+                 print(f"🔧 Agent Tool Call: {ai_message.tool_calls[0]['name']}")
+            else:
+                print(f"💭 Agent Reasoning: {ai_message.content[:200]}...")
+            
+            return {"messages": [ai_message], "turn": current_turn}
+    
+        # --- Tool Node ---
+        tool_node = ToolNode(self.tools)
         
-        # Note: Max turns is also checked in should_continue, but good to have here
-        if current_turn > MAX_TURNS:
-            return {"messages": [SystemMessage(content="Max turns reached.")]}
-
-        max_retries = 3
-        ai_message = None
-        for attempt in range(max_retries):
-            try:
-                # Call the single, powerful LLM
-                ai_message = self.llm_with_tools.invoke(state["messages"])
-                break
-            except Exception as e:
-                print(f"⚠️ LLM attempt {attempt+1}/{max_retries} failed: {e}")
-                if attempt == max_retries - 1:
-                    ai_message = AIMessage(
-                        content=f"Error: LLM failed after {max_retries} attempts: {e}"
-                    )
-                time.sleep(2 ** attempt)
+        # --- Build Graph ---
+        print("Building Single-Agent graph...")
+        graph_builder = StateGraph(AgentState)
         
-        # --- Fallback Parsing Logic ---
-        if not ai_message.tool_calls and isinstance(ai_message.content, str) and ai_message.content.strip():
-            parsed_tool_calls = parse_tool_call_from_string(ai_message.content, self.tools)
-            if parsed_tool_calls:
-                print("🔧 Fallback SUCCESS: Rebuilding tool call(s).")
-                ai_message.tool_calls = parsed_tool_calls
-                ai_message.content = "" # Clear the text content
-            else:
-                print(f"⚠️ Fallback FAILED: Could not parse any tool call from content:\n{ai_message.content[:200]}...")
+        graph_builder.add_node("agent", agent_node)
+        graph_builder.add_node("tools", tool_node)
         
-        if ai_message.tool_calls:
-             print(f"🔧 Agent Tool Call: {ai_message.tool_calls[0]['name']}")
-        else:
-            print(f"💭 Agent Reasoning: {ai_message.content[:200]}...")
+        graph_builder.add_edge(START, "agent")
         
-        return {"messages": [ai_message], "turn": current_turn}
-
-    # --- Tool Node ---
-    tool_node = ToolNode(self.tools)
-    
-    # --- Build Graph ---
-    print("Building Single-Agent graph...")
-    graph_builder = StateGraph(AgentState)
-    
-    graph_builder.add_node("agent", agent_node)
-    graph_builder.add_node("tools", tool_node)
-    
-    graph_builder.add_edge(START, "agent")
-    
-    graph_builder.add_conditional_edges(
-        "agent",
-        should_continue, # Use the reverted conditional function
-        {
-            "tools": "tools",
-            "agent": "agent", # For loop prevention
-            END: END
-        }
-    )
-    
-    graph_builder.add_edge("tools", "agent") # Loop back to agent
-    
-    self.graph = graph_builder.compile()
-    print("✅ Single-Agent graph compiled successfully.")
+        graph_builder.add_conditional_edges(
+            "agent",
+            should_continue, # Use the reverted conditional function
+            {
+                "tools": "tools",
+                "agent": "agent", # For loop prevention
+                END: END
+            }
+        )
+        
+        graph_builder.add_edge("tools", "agent") # Loop back to agent
+        
+        self.graph = graph_builder.compile()
+        print("✅ Single-Agent graph compiled successfully.")
 
     def __call__(self, question: str) -> str:
         print(f"\n--- Starting Agent Run for Question ---")