Refactored config path retrieval, removed deprecated files, and impro… (#211)

Author: DefinetlyNotAI

CODE/Logicytics.py

@@ -6,23 +6,20 @@
     exit(1)
 
 
-def check_gpu():
+def check_gpu() -> str:
     """Check if CUDA is available and print the device information.
 
         This function attempts to detect CUDA capability and prints whether
         GPU acceleration is available, along with the device name if applicable.
     """
     try:
         if torch.cuda.is_available():
-            print(f"CUDA is available. Using GPU: {torch.cuda.get_device_name(0)}")
+            return f"CUDA is available. Using GPU: {torch.cuda.get_device_name(0)}"
         else:
-            print("CUDA is not available. Using CPU.")
+            return "CUDA is not available. Using CPU."
     except RuntimeError as err:
-        print(f"Error initializing CUDA: {err}")
+        return f"Error initializing CUDA: {err}"
 
 
 if __name__ == '__main__':
-    check_gpu()
-else:
-    raise ImportError("This training script is meant to be run directly "
-                      "and cannot be imported. Please execute it as a standalone script.")
+    print(check_gpu())

CODE/VulnScan/tools/_test_gpu_acceleration.py

@@ -19,18 +19,19 @@
 from sklearn.tree import DecisionTreeClassifier
 from torch.utils.data import Dataset, DataLoader
 
-# NN seems to be the best choice for this task
-
 # Set up logging
 from logicytics import Log, DEBUG
 
+# NN seems to be the best choice for this task
+
 logger = Log(
     {"log_level": DEBUG,
      "filename": "../../../ACCESS/LOGS/VulnScan_Train.log",
      "colorlog_fmt_parameters":
          "%(log_color)s%(levelname)-8s%(reset)s %(yellow)s%(asctime)s %(blue)s%(message)s",
      }
 )
+vectorizer = None
 
 
 # Dataset Class for PyTorch models
@@ -361,7 +362,8 @@ def validate_data():
         logger.error("CUDA must be a boolean")
         exit(1)
 
-    allowed_models = ["NeuralNetwork", "LogReg", "RandomForest", "ExtraTrees", "GBM", "XGBoost", "DecisionTree", "NaiveBayes"]
+    allowed_models = ["NeuralNetwork", "LogReg", "RandomForest", "ExtraTrees", "GBM", "XGBoost", "DecisionTree",
+                      "NaiveBayes"]
     if MODEL_NAME not in allowed_models:
         logger.error(f"MODEL_NAME must be one of: {', '.join(allowed_models)}")
         exit(1)