mhv2

app.py

@@ -482,6 +482,30 @@ def is_db_intent(question: str) -> bool:
     except Exception:
         return False
 
+def extract_label_value_pairs(text: str) -> List[Dict[str, Union[str, float]]]:
+    """Extract pairs like 'LABEL: NUMBER' from free text."""
+    pairs: List[Dict[str, Union[str, float]]] = []
+    if not text:
+        return pairs
+    try:
+        raw_lines = text.split('\n')
+        for line in raw_lines:
+            s = line.strip()
+            if not s:
+                continue
+            s = s.lstrip('•*\t -')
+            m = re.match(r"^(.+?):\s*([0-9][0-9.,]*)$", s)
+            if m:
+                label = re.sub(r"[*_`]+", "", m.group(1)).strip()
+                try:
+                    value = float(m.group(2).replace(',', ''))
+                    pairs.append({"label": label, "value": value})
+                except Exception:
+                    continue
+        return pairs
+    except Exception:
+        return pairs
+
 def generate_plot(data, x_col, y_col, title, x_label, y_label):
     """Generate a plot from data and return the file path."""
     plt.figure(figsize=(10, 6))
@@ -746,44 +770,21 @@ async def stream_agent_response(question: str, chat_history: List[List[str]], se
                         logger.error(f"Second-pass SQL synthesis failed: {e}")
             
             # Fallback: if user asked for a chart and we didn't get SQL or chart yet,
-            # parse the most recent assistant text for lines like "LABEL: NUMBER" (bulleted or plain).
+            # use the MOST RECENT bot text (this turn or previous) and parse label:value pairs.
             if chart_fig is None:
                 wants_chart, desired_type = detect_chart_preferences(question)
                 if wants_chart:
-                    # Find the most recent assistant message with usable numeric pairs
                     candidate_text = ""
-                    if chat_history:
+                    # Prefer THIS TURN's response_text if it's non-empty
+                    if isinstance(response_text, str) and response_text.strip():
+                        candidate_text = response_text
+                    # Otherwise look back at the latest assistant turn in history
+                    if not candidate_text and chat_history:
                         for pair in reversed(chat_history):
                             if len(pair) >= 2 and isinstance(pair[1], str) and pair[1].strip():
                                 candidate_text = pair[1]
                                 break
-                    # Also consider current response_text as a data source
-                    if not candidate_text and isinstance(response_text, str) and response_text.strip():
-                        candidate_text = response_text
-                    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})
+                    data = extract_label_value_pairs(candidate_text)
                         logger.info(f"Fallback parse from text: extracted {len(data)} items for potential chart")
                         if len(data) >= 2:
                             chart_fig = generate_chart(
@@ -793,8 +794,14 @@ async def stream_agent_response(question: str, chat_history: List[List[str]], se
                                 y="value",
                                 title="Distribución"
                             )
-                            if chart_fig is not None:
-                                logger.info(f"Chart generated from text fallback: type={desired_type}, items={len(data)}")
+                        if chart_fig is not None and not response_text.strip():
+                            # Provide a helpful text if the agent didn't produce one
+                            response_text = (
+                                f"Gráfico {('de barras' if desired_type=='bar' else desired_type)} "
+                                f"con {len(data)} categorías basado en los datos previos."
+                            )
+                        if chart_fig is not None:
+                            logger.info(f"Chart generated from text fallback: type={desired_type}, items={len(data)}")
             
             # Update the assistant's message with the response
             assistant_message["content"] = response_text