INFO:__main__:Database connection successful INFO:__main__:Response generation complete

app.py

@@ -12,6 +12,10 @@ import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
+try:
+    from sqlalchemy import text as sa_text
+except Exception:
+    sa_text = None
 
 try:
     # Intentar importar dependencias opcionales
@@ -319,21 +323,36 @@ else:
 
 logger.info("="*50)
 
+def looks_like_sql(s: str) -> bool:
+    """Heuristic to check if a string looks like an executable SQL statement."""
+    if not s:
+        return False
+    s_strip = s.strip().lstrip("-- ")
+    # common starters
+    return bool(re.match(r"^(WITH|SELECT|INSERT|UPDATE|DELETE|CREATE|ALTER|DROP|TRUNCATE)\b", s_strip, re.IGNORECASE))
+
+
 def extract_sql_query(text):
-    """Extrae consultas SQL del texto usando expresiones regulares."""
+    """Extrae consultas SQL del texto. Acepta solo bloques etiquetados como ```sql
+    o cadenas que claramente parezcan SQL. Evita ejecutar texto genérico.
+    """
     if not text:
         return None
-    
-    # Buscar código SQL entre backticks
-    sql_match = re.search(r'```(?:sql)?\s*(.*?)```', text, re.DOTALL)
-    if sql_match:
-        return sql_match.group(1).strip()
-    
-    # Si no hay backticks, buscar una consulta SQL simple
-    sql_match = re.search(r'(SELECT|INSERT|UPDATE|DELETE|CREATE|ALTER|DROP|TRUNCATE).*?;', text, re.IGNORECASE | re.DOTALL)
-    if sql_match:
-        return sql_match.group(0).strip()
-    
+
+    # Buscar TODOS los bloques en backticks y elegir los que sean 'sql'
+    for m in re.finditer(r"```(\w+)?\s*(.*?)```", text, re.DOTALL | re.IGNORECASE):
+        lang = (m.group(1) or '').lower()
+        body = (m.group(2) or '').strip()
+        if lang in {"sql", "postgresql", "mysql"} and looks_like_sql(body):
+            return body
+
+    # Si no hay bloques etiquetados, buscar una consulta SQL simple con palabras clave
+    simple = re.search(r"(WITH|SELECT|INSERT|UPDATE|DELETE|CREATE|ALTER|DROP|TRUNCATE)[\s\S]*?;", text, re.IGNORECASE)
+    if simple:
+        candidate = simple.group(0).strip()
+        if looks_like_sql(candidate):
+            return candidate
+
     return None
 
 def execute_sql_query(query, db_connection):
@@ -343,7 +362,11 @@ def execute_sql_query(query, db_connection):
         
     try:
         with db_connection._engine.connect() as connection:
-            result = connection.execute(query)
+            # Ensure SQLAlchemy receives a SQL expression
+            if sa_text is not None and isinstance(query, str):
+                result = connection.execute(sa_text(query))
+            else:
+                result = connection.execute(query)
             rows = result.fetchall()
             
             # Convertir los resultados a un formato legible
@@ -481,9 +504,9 @@ async def stream_agent_response(question: str, chat_history: List[List[str]]) ->
             
             logger.info(f"Extracted response text: {response_text[:200]}...")
             
-            # Check if the response contains an SQL query
+            # Check if the response contains an SQL query and it truly looks like SQL
             sql_query = extract_sql_query(response_text)
-            if sql_query:
+            if sql_query and looks_like_sql(sql_query):
                 logger.info(f"Detected SQL query: {sql_query}")
                 # Execute the query and update the response
                 db_connection, _ = setup_database_connection()
@@ -551,6 +574,8 @@ async def stream_agent_response(question: str, chat_history: List[List[str]]) ->
                         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.")
             
             # 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).