up1

app.py

@@ -441,22 +441,19 @@ async def stream_agent_response(question: str, chat_history: List[List[str]]) ->
             f"3. El modelo de lenguaje esté disponible\n\n"
             f"Error: {agent_error}"
         )
-        assistant_message = {"role": "assistant", "content": error_msg}
-        return [assistant_message]
+        return error_msg, None
         
     # Update the agent's memory with the full conversation history
     try:
-        # Clear existing memory
+        # Rebuild agent memory from chat history pairs
         if hasattr(agent, 'memory') and agent.memory is not None:
             agent.memory.clear()
-            
-        # Add all messages to memory
-        for i in range(0, len(messages)-1, 2):  # Process in pairs (user, assistant)
-            if i+1 < len(messages):
-                agent.memory.save_context(
-                    {"input": messages[i].content},
-                    {"output": messages[i+1].content}
-                )
+            for i in range(0, len(messages)-1, 2):  # (user, assistant)
+                if i+1 < len(messages):
+                    agent.memory.save_context(
+                        {"input": messages[i].content},
+                        {"output": messages[i+1].content}
+                    )
     except Exception as e:
         logger.error(f"Error updating agent memory: {str(e)}", exc_info=True)
     
@@ -467,7 +464,8 @@ async def stream_agent_response(question: str, chat_history: List[List[str]]) ->
         
         # Execute the agent with proper error handling
         try:
-            response = await agent.ainvoke({"input": question, "chat_history": chat_history})
+            # Let the agent use its memory; don't pass raw chat_history
+            response = await agent.ainvoke({"input": question})
             logger.info(f"Agent response type: {type(response)}")
             logger.info(f"Agent response content: {str(response)[:500]}...")
             
@@ -554,6 +552,52 @@ async def stream_agent_response(question: str, chat_history: List[List[str]]) ->
                 else:
                     response_text += "\n\n⚠️ No se pudo conectar a la base de datos para ejecutar la consulta."
             
+            # Fallback: if user asked for a chart (e.g., pie) and we didn't get SQL or chart yet,
+            # parse the most recent assistant text for lines like "LABEL: NUMBER" (bulleted or plain).
+            if chart_fig is None:
+                q_lower = question.lower()
+                wants_chart = any(k in q_lower for k in ["gráfico", "grafico", "chart", "graph", "pastel", "pie"])
+                if wants_chart and chat_history:
+                    # Find the most recent assistant message with usable numeric pairs
+                    candidate_text = ""
+                    for pair in reversed(chat_history):
+                        if len(pair) >= 2 and isinstance(pair[1], str) and pair[1].strip():
+                            candidate_text = pair[1]
+                            break
+                    if candidate_text:
+                        raw_lines = candidate_text.split('\n')
+                        # Normalize lines: strip bullets and markdown symbols
+                        norm_lines = []
+                        for l in raw_lines:
+                            s = l.strip()
+                            if not s:
+                                continue
+                            s = s.lstrip("•*-\t ")
+                            # Remove surrounding markdown emphasis from labels later
+                            norm_lines.append(s)
+                        data = []
+                        for l in norm_lines:
+                            # Accept patterns like "**LABEL**: 123" or "LABEL: 1,234"
+                            m = re.match(r"^(.+?):\s*([0-9][0-9.,]*)$", l)
+                            if m:
+                                label = m.group(1).strip()
+                                # Strip common markdown emphasis
+                                label = re.sub(r"[*_`]+", "", label).strip()
+                                try:
+                                    val = float(m.group(2).replace(',', ''))
+                                except Exception:
+                                    continue
+                                data.append({"label": label, "value": val})
+                        if len(data) >= 2:
+                            desired_type = 'pie' if any(k in q_lower for k in ["gráfico circular", "grafico circular", "pie", "pastel"]) else 'bar'
+                            chart_fig = generate_chart(
+                                data=data,
+                                chart_type=desired_type,
+                                x="label",
+                                y="value",
+                                title="Distribución"
+                            )
+            
             # Update the assistant's message with the response
             assistant_message["content"] = response_text