Update app.py

app.py

@@ -54,75 +54,95 @@ graph.add_edge(START, "agent")
 graph.add_edge("agent", END)
 compiled_graph = graph.compile()
 
-# 3) The corrected respond_to_input:
 def respond_to_input(user_input: str) -> str:
-    
+    """
+    Wrap compiled_graph.invoke(…) (and the “second pass”) in a try/except
+    so that if the agent ever returns something unparsable (a plain str),
+    we catch it and avoid the `'str' object has no attribute 'items'` error.
+    """
+
+    # A) Build a SystemMessage that forces the model to emit bare JSON if it calls a tool:
     system_msg = SystemMessage(
         content=(
             "You are an assistant with access to exactly these tools:\n"
             "  1) web_search(query:str)\n"
             "  2) parse_excel(path:str,sheet_name:str)\n"
             "  3) ocr_image(path:str)\n\n"
-            "⚠️ If (and only if) you need to call a tool, output exactly one JSON object "
-            "and nothing else. For example:\n"
+            "⚠️ **IMPORTANT** ⚠️: If (and only if) you need to call a tool, "
+            "output exactly one JSON object and nothing else. For example:\n"
             "```json\n"
             '{"tool":"web_search","query":"Mercedes Sosa albums 2000-2009"}\n'
             "```\n"
-            "That JSON must start at the very first character of your response and end at the last character—"
+            "That JSON must start at the very first character of your response and end at the very last character—"
             "no quotes, no code fences, and no extra explanation.\n\n"
-            "If you do not need any tool, simply reply with your final answer as plain text."
+            "If you do NOT need any tool, simply reply with your final answer as plain text."
         )
     )
-    
+
+    # B) Initialize state with just that SystemMessage. We'll pass user_input separately below:
     initial_state = { "messages": [system_msg] }
-    final_state = compiled_graph.invoke(initial_state, user_input)
-    print("in respond_to_input : ")
-  
-    print("===== AGENT MESSAGES =====")
-    for i, msg in enumerate(final_state["messages"]):
-        if isinstance(msg, AIMessage):
-            print(f"  [AIMessage #{i}]: {repr(msg.content)}")
-    print("==========================")
-
-    # E) Take the very last AIMessage that the agent produced
-    last_msg = None
-    for msg in reversed(final_state["messages"]):
-        if isinstance(msg, AIMessage):
-            last_msg = msg.content
-            break
-
-    # F) Try to parse that last message as a JSON‐dict for a tool call
-    tool_dict = parse_tool_json(last_msg or "")
-    if tool_dict:
-        # G) If valid JSON, run the tool
-        print(">> Parsed tool call:", tool_dict)
-        result = tool_node.run(tool_dict)
-
-        # H) Feed back the tool’s output as the next AIMessage, then re‐invoke
-        # (Note: we pass an empty string as user_input because we’re continuing the same turn)
-        continuation_state = {
-            "messages": [
-                *final_state["messages"],
-                AIMessage(content=result)
-            ]
-        }
-        second_pass = compiled_graph.invoke(continuation_state, "")
-
-        # I) Log that second pass
-        print("===== AGENT SECOND PASS =====")
-        for i, msg in enumerate(second_pass["messages"]):
+
+    try:
+        # C) First pass: invoke the graph with the user’s question:
+        final_state = compiled_graph.invoke(initial_state, user_input)
+
+        # D) Log every AIMessage for debugging (optional—remove in production):
+        print("===== AGENT MESSAGES (First Pass) =====")
+        for i, msg in enumerate(final_state["messages"]):
             if isinstance(msg, AIMessage):
-                print(f"  [AIMessage2 #{i}]: {repr(msg.content)}")
-        print("==============================")
+                print(f"[AIMessage #{i}]: {repr(msg.content)}")
+        print("=========================================")
 
-        # J) Return the final AIMessage from that second pass
-        for msg in reversed(second_pass["messages"]):
+        # E) Find the very last AIMessage the agent produced:
+        last_msg = None
+        for msg in reversed(final_state["messages"]):
             if isinstance(msg, AIMessage):
-                return msg.content or ""
-        return ""
-    else:
-        # K) If it wasn’t valid JSON, just return the plain last_msg
-        return last_msg or ""
+                last_msg = msg.content
+                break
+
+        # F) Try to parse that last_msg as a tool‐call JSON:
+        tool_dict = parse_tool_json(last_msg or "")
+        if tool_dict:
+            # G) If valid JSON, run the tool:
+            print(">> Parsed tool call:", tool_dict)
+            tool_result = tool_node.run(tool_dict)
+
+            # H) Log output of the tool (optional):
+            print(f">> Tool '{tool_dict['tool']}' returned: {repr(tool_result)}")
+
+            # I) Second pass: feed the tool’s result back into the graph,
+            #    with an empty user_input because we’re continuing the same turn.
+            continuation_state = {
+                "messages": [
+                    *final_state["messages"],
+                    AIMessage(content=tool_result)
+                ]
+            }
+            second_pass = compiled_graph.invoke(continuation_state, "")
+
+            # J) Log every AIMessage from the second pass (optional):
+            print("===== AGENT MESSAGES (Second Pass) =====")
+            for i, msg in enumerate(second_pass["messages"]):
+                if isinstance(msg, AIMessage):
+                    print(f"[AIMessage2 #{i}]: {repr(msg.content)}")
+            print("=========================================")
+
+            # K) Return the last AIMessage from the second pass as the final answer:
+            for msg in reversed(second_pass["messages"]):
+                if isinstance(msg, AIMessage):
+                    return msg.content or ""
+            return ""
+
+        else:
+            # L) If it wasn’t valid JSON, just treat last_msg as plain‐text final answer:
+            return last_msg or ""
+
+    except Exception as e:
+        # M) If *any* part of the above raises (e.g. because the LLM returned a string
+        #    that the routing layer tried to treat as a dict), we catch it here:
+        print(f"‼️ respond_to_input error: {repr(e)}")
+        return ""  # or return a default fallback string if you prefer
+
 
 class BasicAgent:
     def __init__(self):