Update app.py

app.py

@@ -168,18 +168,14 @@ def inspect_node(state: AgentState) -> AgentState:
       • Return {"final_answer":"<final>"} if done, OR
       • Return exactly one tool key to run next (wiki_query / ocr_path / excel_path & excel_sheet_name / audio_path).
     """
+
     global tool_counter
 
-    # 0) MAX‐TOOLS GUARD: if we've already run 5 tools, ask LLM to finalize
+    # If we've already run 5 tools, ask GPT for a strictly‐formatted JSON final_answer
     if tool_counter >= 5:
-        # Build a fresh prompt that includes:
-        #  - The user’s original question
-        #  - Any tool results collected so far
-        #  - The interim answer
-        # Then ask for a polished final_answer.
         messages_for_llm = []
 
-        # (1) Put back the user question
+        # Re‐insert the user’s question
         question = ""
         for msg in reversed(state.get("messages", [])):
             if isinstance(msg, HumanMessage):
@@ -187,7 +183,7 @@ def inspect_node(state: AgentState) -> AgentState:
                 break
         messages_for_llm.append(SystemMessage(content=f"USER_QUESTION: {question}"))
 
-        # (2) Add any tool results
+        # Add any tool results so far
         if sr := state.get("web_search_result"):
             messages_for_llm.append(SystemMessage(content=f"WEB_SEARCH_RESULT: {sr}"))
         if orc := state.get("ocr_result"):
@@ -199,29 +195,26 @@ def inspect_node(state: AgentState) -> AgentState:
         if wr := state.get("wiki_result"):
             messages_for_llm.append(SystemMessage(content=f"WIKIPEDIA_RESULT: {wr}"))
 
-        # (3) Always show the interim answer
+        # Show the interim answer
         interim = state.get("interim_answer", "")
         messages_for_llm.append(SystemMessage(content=f"INTERIM_ANSWER: {interim}"))
-        messages_for_llm.append(SystemMessage(content="Please write a polished final answer, with no explanation at all."))
-        # (4) Prompt GPT to produce a polished final answer
-        
-        prompt = (
+
+        # Now ask for JSON ONLY (no reasoning, no extra text)
+        final_prompt = (
             "You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."
-            "You have the user’s question, any relevant tool results above,"
-            " and a draft answer (INTERIM_ANSWER).\n"
-            "Using all of that, write a concise, polished final answer. "
-            "Do not include any explanation or commentary, just the answer."
-            "Return exactly:\n"
-            "  {\"final_answer\":\"<your final answer>\"}\n"
-            "and nothing else.\n"
+            "Using only the information above—including the USER_QUESTION, "
+            "any TOOL_RESULT, and the INTERIM_ANSWER—produce a concise final answer. "
+            "Return exactly one JSON object and nothing else, in this format:\n\n"
+            "{\"final_answer\":\"<your final answer>\"}\n"
+            "Do not include any other words or punctuation outside that JSON."
         )
-        messages_for_llm.append(SystemMessage(content=prompt))
+        messages_for_llm.append(SystemMessage(content=final_prompt))
 
         llm_response = llm(messages_for_llm)
         raw = llm_response.content.strip()
         new_msgs = state["messages"] + [AIMessage(content=raw)]
 
-        # Parse the LLM’s JSON
+        # Try to parse exactly one JSON with "final_answer"
         try:
             parsed = json.loads(raw)
             if isinstance(parsed, dict) and "final_answer" in parsed:
@@ -229,9 +222,8 @@ def inspect_node(state: AgentState) -> AgentState:
         except json.JSONDecodeError:
             pass
 
-        # If parsing fails, at least return the interim as fallback
+        # Fallback to returning the interim in case JSON parse fails
         return {"messages": new_msgs, "final_answer": interim}
-
     # ——————————— If tool_counter < 5, proceed as before ———————————
     messages_for_llm = []