Revert "quito logica de ejecutar la consulta"

This reverts commit af9a0423088ef5970342a9e0e944871527d6b479.

app.py

@@ -542,14 +542,79 @@ async def stream_agent_response(question: str, chat_history: List[List[str]]) ->
             # Check if the response contains an SQL query and it truly looks like SQL
             sql_query = extract_sql_query(response_text)
             if sql_query and looks_like_sql(sql_query):
-                logger.info("SQL query detected; showing as Markdown only (no execution).")
-                # Show the SQL as Markdown inside the chatbot, without executing it
-                response_text += f"\n\n### 🔍 Resultado de la consulta:\n```sql\n{sql_query}\n```"
+                logger.info(f"Detected SQL query: {sql_query}")
+                db_connection, _ = setup_database_connection()
+                if db_connection:
+                    query_result = execute_sql_query(sql_query, db_connection)
+                    
+                    # Add the query and its result to the response
+                    response_text += f"\n\n### 🔍 Resultado de la consulta:\n```sql\n{sql_query}\n```\n\n{query_result}"
+                    
+                    # Try to generate an interactive chart if the result is tabular
+                    try:
+                        if isinstance(query_result, str) and '|' in query_result and '---' in query_result:
+                            # Convert markdown table to DataFrame
+                            
+                            # Clean up the markdown table
+                            lines = [line.strip() for line in query_result.split('\n') 
+                                    if line.strip() and '---' not in line and '|' in line]
+                            if len(lines) > 1:  # At least header + 1 data row
+                                # Get column names from the first line
+                                columns = [col.strip() for col in lines[0].split('|')[1:-1]]
+                                # Get data rows
+                                data = []
+                                for line in lines[1:]:
+                                    values = [val.strip() for val in line.split('|')[1:-1]]
+                                    if len(values) == len(columns):
+                                        data.append(dict(zip(columns, values)))
+                                
+                                if data and len(columns) >= 2:
+                                    # Determine chart type from user's question (supports pie chart)
+                                    q_lower = question.lower()
+                                    if any(k in q_lower for k in ["gráfico circular", "grafico circular", "pie", "pastel"]):
+                                        desired_type = 'pie'
+                                    elif any(k in q_lower for k in ["línea", "linea", "line"]):
+                                        desired_type = 'line'
+                                    elif any(k in q_lower for k in ["dispersión", "dispersion", "scatter"]):
+                                        desired_type = 'scatter'
+                                    elif any(k in q_lower for k in ["histograma", "histogram"]):
+                                        desired_type = 'histogram'
+                                    else:
+                                        desired_type = 'bar'
+
+                                    # Choose x/y columns (assume first is category, second numeric)
+                                    x_col = columns[0]
+                                    # pick first numeric column different to x
+                                    y_col = None
+                                    for col in columns[1:]:
+                                        try:
+                                            pd.to_numeric(data[0][col])
+                                            y_col = col
+                                            break
+                                        except Exception:
+                                            continue
+                                    if y_col:
+                                        chart_fig = generate_chart(
+                                            data=data,
+                                            chart_type=desired_type,
+                                            x=x_col,
+                                            y=y_col,
+                                            title=f"{y_col} por {x_col}"
+                                        )
+                                        if chart_fig is not None:
+                                            logger.info(f"Chart generated from SQL table: type={desired_type}, x={x_col}, y={y_col}, rows={len(data)}")
+                                            chart_state = {"data": data, "x_col": x_col, "y_col": y_col, "title": f"{y_col} por {x_col}", "chart_type": desired_type}
+                    except Exception as e:
+                        logger.error(f"Error generating chart: {str(e)}", exc_info=True)
+                        # Don't fail the whole request if chart generation fails
+                        response_text += "\n\n⚠️ No se pudo generar la visualización de los datos."
+                else:
+                    response_text += "\n\n⚠️ No se pudo conectar a la base de datos para ejecutar la consulta."
             elif sql_query and not looks_like_sql(sql_query):
                 logger.info("Detected code block but it does not look like SQL; skipping execution.")
 
             # If we still have no chart but the user clearly wants one,
-            # try a second pass to get ONLY a SQL query from the agent and present it as Markdown.
+            # try a second pass to get ONLY a SQL query from the agent and execute it.
             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"])
@@ -564,12 +629,40 @@ async def stream_agent_response(question: str, chat_history: List[List[str]]) ->
                         sql_only_text = str(sql_only_resp)
                         sql_query2 = extract_sql_query(sql_only_text)
                         if sql_query2 and looks_like_sql(sql_query2):
-                            logger.info("Second pass SQL detected; showing as Markdown only (no execution).")
-                            # Append query as Markdown to response_text for visibility
-                            response_text += (
-                                f"\n\n### 🔍 Resultado de la consulta (2ª pasada):\n"
-                                f"```sql\n{sql_query2}\n```"
-                            )
+                            logger.info(f"Second pass SQL detected: {sql_query2}")
+                            db_connection, _ = setup_database_connection()
+                            if db_connection:
+                                query_result = execute_sql_query(sql_query2, db_connection)
+                                # Append query and result to response_text for transparency
+                                response_text += f"\n\n### 🔍 Resultado de la consulta (2ª pasada):\n```sql\n{sql_query2}\n```\n\n{query_result}"
+                                # Try robust markdown table parse
+                                data_list = parse_markdown_table(query_result) if isinstance(query_result, str) else None
+                                if data_list:
+                                    # Infer columns
+                                    columns = list(data_list[0].keys())
+                                    x_col = columns[0]
+                                    y_col = None
+                                    for col in columns[1:]:
+                                        try:
+                                            pd.to_numeric(data_list[0][col])
+                                            y_col = col
+                                            break
+                                        except Exception:
+                                            continue
+                                    if y_col:
+                                        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_list,
+                                            chart_type=desired_type,
+                                            x=x_col,
+                                            y=y_col,
+                                            title=f"{y_col} por {x_col}"
+                                        )
+                                        if chart_fig is not None:
+                                            logger.info("Chart generated from second-pass SQL execution (markdown parse).")
+                                            chart_state = {"data": data_list, "x_col": x_col, "y_col": y_col, "title": f"{y_col} por {x_col}", "chart_type": desired_type}
+                            else:
+                                logger.info("No DB connection on second pass; skipping.")
                     except Exception as e:
                         logger.error(f"Second-pass SQL synthesis failed: {e}")