added permission wrapper

modules/markovmemory.py

@@ -6,11 +6,14 @@ from modules.globalvars import *
 import logging
 import modules.keys as k
 from modules.settings import Settings as SettingsManager
+
 settings_manager = SettingsManager()
 settings = settings_manager.settings
 
 
 logger = logging.getLogger("goober")
+
+
 # Get file size and line count for a given file path
 def get_file_info(file_path):
     try:
@@ -21,6 +24,7 @@ def get_file_info(file_path):
     except Exception as e:
         return {"error": str(e)}
 
+
 # Load memory data from file, or use default dataset if not loaded yet
 def load_memory():
     data = []
@@ -34,36 +38,42 @@ def load_memory():
 
     return data
 
+
 # Save memory data to MEMORY_FILE
 def save_memory(memory):
     with open(settings["bot"]["active_memory"], "w") as f:
         json.dump(memory, f, indent=4)
 
+
 def train_markov_model(memory, additional_data=None):
     if not memory:
         return None
     filtered_memory = [line for line in memory if isinstance(line, str)]
     if additional_data:
-        filtered_memory.extend(line for line in additional_data if isinstance(line, str))
+        filtered_memory.extend(
+            line for line in additional_data if isinstance(line, str)
+        )
     if not filtered_memory:
         return None
     text = "\n".join(filtered_memory)
     model = markovify.NewlineText(text, state_size=2)
     return model
 
+
 # Save the Markov model to a pickle file
-def save_markov_model(model, filename='markov_model.pkl'):
-    with open(filename, 'wb') as f:
+def save_markov_model(model, filename="markov_model.pkl"):
+    with open(filename, "wb") as f:
         pickle.dump(model, f)
     logger.info(f"Markov model saved to {filename}.")
 
+
 # Load the Markov model from a pickle file
-def load_markov_model(filename='markov_model.pkl'):
+def load_markov_model(filename="markov_model.pkl"):
     try:
-        with open(filename, 'rb') as f:
+        with open(filename, "rb") as f:
             model = pickle.load(f)
         logger.info(f"{k.model_loaded()} {filename}.{RESET}")
         return model
     except FileNotFoundError:
         logger.error(f"{filename} {k.not_found()}{RESET}")
-        return None
+        return None