Remove PyTorch dependency by using nvidia-smi for GPU check

cognios/requirements.txt

@@ -1,6 +1,5 @@
 psutil
 pandas
 scikit-learn
-torch
 streamlit
 pytest

cognios/src/ui.py

@@ -146,9 +146,16 @@
     proc_metrics = get_last_process_metrics(limit=15)
 
     # Check GPU availability
-    import torch
-    gpu_available = torch.cuda.is_available()
-    gpu_name = torch.cuda.get_device_name(0) if gpu_available else "No GPU Detected"
+    import subprocess
+    gpu_available = False
+    gpu_name = "No GPU Detected"
+    try:
+        result = subprocess.run(['nvidia-smi', '--query-gpu=name', '--format=csv,noheader'], capture_output=True, text=True)
+        if result.returncode == 0 and result.stdout.strip():
+            gpu_available = True
+            gpu_name = result.stdout.strip().split('\n')[0]
+    except (FileNotFoundError, Exception):
+        pass
 
     if sys_metrics:
         if len(sys_metrics[0]) == 6:

cognios/tests/test_all.py

@@ -103,7 +103,8 @@ def test_os_doctor():
     print("\n[OS Doctor Test] Successfully detected CPU spike anomaly!")
 
 def test_focus_os():
-    import torch
+    import pytest
+    torch = pytest.importorskip("torch")
     import os
     from cognios.src.focus_os import WorkloadCNN, Prioritizer