Update app.py

app.py

@@ -721,9 +721,8 @@ def debug():
             'db_exists': os.path.exists(db.db_path),
             'users': [dict(u) for u in users],
             'model_loaded': model is not None,
-            'model_error': _model_error,
-            'model_thread_alive': _model_thread.is_alive(),
-            'h5_files': [f for f in os.listdir('/app') if f.endswith('.h5')]
+            'h5_files': [f for f in os.listdir(os.path.dirname(os.path.abspath(__file__))) if f.endswith('.h5')],
+            'app_dir': os.path.dirname(os.path.abspath(__file__))
         })
     except Exception as e:
         return jsonify({'error': str(e), 'db_path': db.db_path})
@@ -732,28 +731,12 @@ def debug():
 # ------------------------------------------------------------------ #
 #  ARRANQUE
 # ------------------------------------------------------------------ #
-# Cargar modelo en background al arrancar
-import threading
 
-_model_error = None
 
-def _load_model_background():
-    global _model_error
-    print("Iniciando carga de modelo en background...")
-    try:
-        result = load_model()
-        if result:
-            print("Modelo cargado exitosamente en background")
-        else:
-            _model_error = "load_model() retornó False — ver logs del servidor"
-            print(_model_error)
-    except Exception as e:
-        import traceback
-        _model_error = traceback.format_exc()
-        print(f"Error cargando modelo en background: {_model_error}")
-
-_model_thread = threading.Thread(target=_load_model_background, daemon=True)
-_model_thread.start()
+# Cargar modelo al importar el módulo (funciona con gunicorn)
+print("=== Cargando modelo TensorFlow ===")
+load_model()
+print(f"=== Modelo {'CARGADO' if model is not None else 'NO CARGADO'} ===")
 
 if __name__ == '__main__':
     port = int(os.environ.get('PORT', 7860))