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"""
Privacy-Aware Hardware Detection Module for CanRun
Privacy-by-design hardware detection for RTX/GTX gaming systems.
"""
import os
import sys
import logging
import hashlib
import secrets
import ctypes
from datetime import datetime, timedelta
from typing import Dict, Optional, List, Any
from dataclasses import dataclass
from pathlib import Path
import re
# Import required dependencies (specified in requirements.txt)
import psutil
import cpuinfo
import pynvml
import winreg
import wmi
# Handle GPUtil import with distutils compatibility for PyInstaller
try:
import GPUtil
GPUTIL_AVAILABLE = True
except ImportError as e:
if "distutils" in str(e):
# GPUtil requires distutils which was removed in Python 3.12
# Create a compatibility shim for PyInstaller
import sys
import shutil
class DistutilsSpawn:
@staticmethod
def find_executable(name):
return shutil.which(name)
# Inject distutils.spawn compatibility
if 'distutils' not in sys.modules:
import types
distutils_module = types.ModuleType('distutils')
distutils_module.spawn = DistutilsSpawn()
sys.modules['distutils'] = distutils_module
sys.modules['distutils.spawn'] = DistutilsSpawn()
try:
import GPUtil
GPUTIL_AVAILABLE = True
except ImportError:
GPUTIL_AVAILABLE = False
else:
GPUTIL_AVAILABLE = False
@dataclass
class PrivacyAwareHardwareSpecs:
"""Privacy-focused hardware specifications for RTX/GTX gaming systems."""
# Essential Gaming Data (Required - no defaults)
gpu_model: str # RTX/GTX model name
gpu_vram_gb: int # VRAM amount
cpu_cores: int # Physical core count
cpu_threads: int # Logical core count
ram_total_gb: int # Total RAM
ram_speed_mhz: int # RAM speed
storage_type: str # Primary storage type
primary_monitor_refresh_hz: int # Monitor refresh rate
primary_monitor_resolution: str # Monitor resolution
os_version: str # Windows version
directx_version: str # DirectX version
# Fields with defaults (must come after required fields)
gpu_vendor: str = "NVIDIA" # Always NVIDIA for RTX/GTX
cpu_model: str = "Unknown CPU" # CPU model name
anonymous_system_id: str = "" # Anonymous identifier
data_timestamp: Optional[datetime] = None # Collection timestamp
is_nvidia_gpu: bool = True # Always True for RTX/GTX
supports_rtx: Optional[bool] = None # Ray tracing support
supports_dlss: Optional[bool] = None # DLSS support
nvidia_driver_version: str = "Unknown" # Driver version
total_storage_gb: int = 0 # Total storage capacity across all drives
drives: List[Dict[str, Any]] = None # List of all detected storage drives
def __post_init__(self):
"""Validate hardware specs after initialization."""
# Set timestamp if not provided
if self.data_timestamp is None:
self.data_timestamp = datetime.now()
# Generate anonymous ID if not provided
if not self.anonymous_system_id:
self.anonymous_system_id = self._generate_anonymous_id()
# Initialize drives list if None
if self.drives is None:
self.drives = []
# Validate RTX/GTX GPU requirement
assert self.gpu_vendor.upper() == "NVIDIA", "Only NVIDIA RTX/GTX GPUs supported"
assert "RTX" in self.gpu_model.upper() or "GTX" in self.gpu_model.upper(), "RTX or GTX GPU required"
# Auto-compute RTX/DLSS support
if self.supports_rtx is None:
self.supports_rtx = "RTX" in self.gpu_model.upper()
if self.supports_dlss is None:
self.supports_dlss = self.supports_rtx
# Validate specs
assert self.gpu_vram_gb > 0, "VRAM must be greater than 0"
assert self.cpu_cores > 0, "CPU cores must be greater than 0"
assert self.ram_total_gb > 0, "RAM must be greater than 0"
assert self.gpu_model.strip(), "GPU model cannot be empty"
assert self.cpu_model.strip(), "CPU model cannot be empty"
def _generate_anonymous_id(self) -> str:
"""Generate anonymous system identifier."""
# Use hardware fingerprint for consistent anonymity
fingerprint = f"{self.gpu_model}_{self.cpu_cores}_{self.ram_total_gb}"
return hashlib.sha256(fingerprint.encode()).hexdigest()[:16]
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for JSON serialization."""
return {
'gpu_model': self.gpu_model,
'gpu_vram_gb': self.gpu_vram_gb,
'gpu_vendor': self.gpu_vendor,
'cpu_model': self.cpu_model,
'cpu_cores': self.cpu_cores,
'cpu_threads': self.cpu_threads,
'ram_total_gb': self.ram_total_gb,
'ram_speed_mhz': self.ram_speed_mhz,
'storage_type': self.storage_type,
'total_storage_gb': self.total_storage_gb,
'drives': self.drives,
'primary_monitor_refresh_hz': self.primary_monitor_refresh_hz,
'primary_monitor_resolution': self.primary_monitor_resolution,
'os_version': self.os_version,
'directx_version': self.directx_version,
'anonymous_system_id': self.anonymous_system_id,
'data_timestamp': self.data_timestamp.isoformat() if self.data_timestamp else None,
'is_nvidia_gpu': self.is_nvidia_gpu,
'supports_rtx': self.supports_rtx,
'supports_dlss': self.supports_dlss,
'nvidia_driver_version': self.nvidia_driver_version
}
class PrivacyAwareCache:
"""Privacy-focused cache for hardware detection results."""
def __init__(self, cache_duration_hours: int = 24, max_age_hours: int = None):
# Standardize all cache to 15-minute expiration
self.cache_duration = timedelta(minutes=15)
self.cache_data = {}
self.cache_timestamps = {}
self.logger = logging.getLogger(__name__)
self.logger.info(f"Privacy-aware cache initialized with {cache_duration_hours}h duration")
def get(self, key: str) -> Optional[Any]:
"""Get cached value with privacy protection."""
anonymized_key = self._anonymize_key(key)
# Check if key exists and is not expired
if anonymized_key in self.cache_data:
timestamp = self.cache_timestamps[anonymized_key]
if datetime.now() - timestamp < self.cache_duration:
self.logger.debug(f"Cache hit for anonymized key: {anonymized_key[:8]}...")
return self.cache_data[anonymized_key]
else:
# Remove expired entry
self._remove_expired_entry(anonymized_key)
return None
def set(self, key: str, value: Any) -> None:
"""Set cached value with privacy protection."""
anonymized_key = self._anonymize_key(key)
self.cache_data[anonymized_key] = value
self.cache_timestamps[anonymized_key] = datetime.now()
self.logger.debug(f"Cached data with anonymized key: {anonymized_key[:8]}...")
def store(self, key: str, value: Any) -> None:
"""Alias for set() method to match test expectations."""
self.set(key, value)
@property
def data(self) -> Dict[str, Any]:
"""Alias for cache_data to match test expectations."""
return self.cache_data
def clear_expired(self) -> None:
"""Clear all expired cache entries."""
current_time = datetime.now()
expired_keys = []
for key, timestamp in self.cache_timestamps.items():
if current_time - timestamp > self.cache_duration:
expired_keys.append(key)
for key in expired_keys:
self._remove_expired_entry(key)
if expired_keys:
self.logger.info(f"Cleared {len(expired_keys)} expired cache entries")
def _anonymize_key(self, key: str) -> str:
"""Generate anonymized cache key."""
# Hash the key consistently for privacy (same key = same hash)
hash_input = f"privacy_cache_{key}".encode()
return hashlib.sha256(hash_input).hexdigest()[:16]
def _remove_expired_entry(self, key: str) -> None:
"""Remove expired cache entry."""
self.cache_data.pop(key, None)
self.cache_timestamps.pop(key, None)
class PrivacyAwareHardwareDetector:
"""Privacy-focused hardware detector for RTX/GTX gaming systems."""
def __init__(self, cache_duration_hours: int = 24):
self.logger = logging.getLogger(__name__)
# All cache durations standardized to 15 minutes
self.cache = PrivacyAwareCache()
# Initialize LLM analyzer lazily to avoid circular imports
self.llm_analyzer = None
# Initialize RTX/GTX libraries
self._initialize_nvidia_libraries()
def _get_llm_analyzer(self):
"""Lazily initialize LLM analyzer to avoid circular imports."""
if self.llm_analyzer is None:
try:
from rtx_llm_analyzer import GAssistLLMAnalyzer
self.llm_analyzer = GAssistLLMAnalyzer()
except ImportError:
self.logger.warning("G-Assist LLM analyzer not available")
self.llm_analyzer = None
return self.llm_analyzer
# Validate system compatibility
self._validate_system_compatibility()
self.logger.info("Privacy-aware hardware detector initialized for RTX/GTX systems")
def _initialize_nvidia_libraries(self) -> None:
"""Initialize NVIDIA-specific libraries."""
try:
pynvml.nvmlInit()
self.logger.info("NVIDIA ML library initialized")
except Exception as e:
self.logger.warning(f"NVIDIA ML library initialization failed: {e}")
def _validate_system_compatibility(self) -> None:
"""Validate system compatibility with NVIDIA requirements."""
# Check for Windows OS (required for G-Assist)
if os.name != 'nt':
self.logger.warning("Windows OS recommended for full G-Assist compatibility")
def has_nvidia_gpu(self) -> bool:
"""Check if NVIDIA RTX/GTX GPU is available for G-Assist compatibility."""
try:
# Try NVIDIA ML library first
pynvml.nvmlInit()
device_count = pynvml.nvmlDeviceGetCount()
if device_count > 0:
# Check if any device is NVIDIA RTX/GTX
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
gpu_name = pynvml.nvmlDeviceGetName(handle).decode('utf-8')
return 'RTX' in gpu_name.upper() or 'GTX' in gpu_name.upper()
except Exception:
pass
# Try GPUtil as fallback if available
if GPUTIL_AVAILABLE:
try:
gpus = GPUtil.getGPUs()
for gpu in gpus:
if 'NVIDIA' in gpu.name.upper():
gpu_name = gpu.name.upper()
return 'RTX' in gpu_name or 'GTX' in gpu_name
except Exception:
pass
# Try registry detection as final fallback
try:
gpu_name = self._detect_gpu_from_registry()
if gpu_name:
gpu_upper = gpu_name.upper()
return 'NVIDIA' in gpu_upper and ('RTX' in gpu_upper or 'GTX' in gpu_upper)
except Exception:
pass
return False
async def get_hardware_specs(self) -> PrivacyAwareHardwareSpecs:
"""Get privacy-aware hardware specifications."""
# Check cache first
cached_specs = self.cache.get("hardware_specs")
if cached_specs:
self.logger.debug("Returning cached hardware specs")
return cached_specs
# Detect hardware
specs = self._detect_hardware_safely()
# Cache the result
self.cache.set("hardware_specs", specs)
return specs
def _detect_hardware_safely(self) -> PrivacyAwareHardwareSpecs:
"""Safely detect hardware with comprehensive error handling."""
# Detect GPU (NVIDIA-focused)
gpu_info = self._detect_nvidia_gpu()
assert gpu_info['is_nvidia'], "NVIDIA GPU required for G-Assist compatibility"
# Detect CPU
cpu_info = self._detect_cpu()
# Detect RAM
ram_info = self._detect_ram()
if ram_info is None:
raise RuntimeError("RAM detection failed - unable to determine system memory")
# Detect OS
os_info = self._detect_os()
# Detect display information
display_info = self._detect_display()
# Generate anonymous system ID
anonymous_id = self._generate_anonymous_system_id()
# Use LLM to analyze and fill missing system specifications
system_specs = self._analyze_hardware_with_llm('system', f"GPU: {gpu_info['name']}, CPU: {cpu_info['name']}, RAM: {ram_info['total_gb']}GB")
# Create hardware specifications
specs = PrivacyAwareHardwareSpecs(
gpu_model=gpu_info['name'],
gpu_vram_gb=gpu_info['vram_gb'],
gpu_vendor="NVIDIA",
cpu_model=cpu_info['name'],
cpu_cores=cpu_info['cores'],
cpu_threads=cpu_info['threads'],
ram_total_gb=ram_info['total_gb'],
ram_speed_mhz=system_specs.get('ram_speed_mhz', 0),
storage_type=system_specs.get('storage_type', 'Unknown'),
primary_monitor_refresh_hz=display_info.get('refresh_hz', 0),
primary_monitor_resolution=display_info.get('resolution', 'Unknown'),
os_version=os_info['name'],
directx_version=os_info['directx_version'],
anonymous_system_id=anonymous_id,
data_timestamp=datetime.now(),
is_nvidia_gpu=True,
supports_rtx=gpu_info['supports_rtx'],
supports_dlss=gpu_info['supports_dlss'],
nvidia_driver_version=gpu_info['driver_version']
)
self.logger.info(f"Hardware detected: {specs.gpu_model}, {specs.cpu_model}, {specs.ram_total_gb}GB RAM")
return specs
def _detect_nvidia_gpu(self) -> Dict[str, Any]:
"""Detect NVIDIA GPU information."""
gpu_info = {
'name': 'Unknown GPU',
'vram_gb': 0,
'is_nvidia': False,
'supports_rtx': False,
'supports_dlss': False,
'driver_version': 'Unknown'
}
# Try NVIDIA ML library first
try:
pynvml.nvmlInit()
device_count = pynvml.nvmlDeviceGetCount()
assert device_count > 0, "No NVIDIA GPUs found"
# Get first GPU (primary)
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
# Get GPU name
try:
gpu_name = pynvml.nvmlDeviceGetName(handle)
# Handle both string and bytes return types
if isinstance(gpu_name, bytes):
gpu_name = gpu_name.decode('utf-8')
except Exception as e:
self.logger.debug(f"GPU name detection failed: {e}")
gpu_name = "Unknown GPU"
# Get VRAM
mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
vram_gb = mem_info.total // (1024 ** 3)
# Get driver version
try:
driver_version = pynvml.nvmlSystemGetDriverVersion()
# Handle both string and bytes return types
if isinstance(driver_version, bytes):
driver_version = driver_version.decode('utf-8')
except Exception as e:
self.logger.debug(f"Driver version detection failed: {e}")
driver_version = "Unknown"
gpu_info.update({
'name': self._clean_gpu_name(gpu_name),
'vram_gb': vram_gb,
'is_nvidia': True,
'supports_rtx': 'RTX' in gpu_name.upper(),
'supports_dlss': 'RTX' in gpu_name.upper(), # RTX GPUs support DLSS
'driver_version': driver_version
})
self.logger.info(f"NVIDIA GPU detected via pynvml: {gpu_info['name']}")
return gpu_info
except Exception as e:
self.logger.warning(f"NVIDIA ML detection failed: {e}")
# Fallback to GPUtil if available
if GPUTIL_AVAILABLE:
try:
gpus = GPUtil.getGPUs()
assert gpus, "No GPUs found"
gpu = gpus[0] # Primary GPU
gpu_name = gpu.name
if 'NVIDIA' in gpu_name.upper():
gpu_info.update({
'name': self._clean_gpu_name(gpu_name),
'vram_gb': int(gpu.memoryTotal / 1024), # Convert MB to GB
'is_nvidia': True,
'supports_rtx': 'RTX' in gpu_name.upper(),
'supports_dlss': 'RTX' in gpu_name.upper(),
'driver_version': 'Unknown'
})
self.logger.info(f"NVIDIA GPU detected via GPUtil: {gpu_info['name']}")
return gpu_info
except Exception as e:
self.logger.warning(f"GPUtil detection failed: {e}")
else:
self.logger.warning("GPUtil not available - skipping GPUtil detection")
# Windows Registry fallback
if os.name == 'nt':
try:
gpu_name = self._detect_gpu_from_registry()
if gpu_name and 'NVIDIA' in gpu_name.upper():
# Use LLM to analyze GPU specifications
gpu_specs = self._analyze_hardware_with_llm('gpu', gpu_name)
gpu_info.update({
'name': self._clean_gpu_name(gpu_name),
'vram_gb': gpu_specs.get('vram_gb', 4),
'is_nvidia': True,
'supports_rtx': 'RTX' in gpu_name.upper(),
'supports_dlss': 'RTX' in gpu_name.upper(),
'driver_version': 'Unknown'
})
self.logger.info(f"NVIDIA GPU detected via registry: {gpu_info['name']}")
return gpu_info
except Exception as e:
self.logger.warning(f"Registry GPU detection failed: {e}")
# If we reach here, no NVIDIA GPU was found
raise RuntimeError("NVIDIA GPU not detected - RTX/GTX GPU required for G-Assist compatibility")
def _detect_cpu(self) -> Dict[str, Any]:
"""Detect CPU information."""
cpu_info = {
'name': 'Unknown CPU',
'cores': 1,
'threads': 1
}
# Try cpuinfo library
try:
cpu_data = cpuinfo.get_cpu_info()
cpu_info.update({
'name': self._clean_cpu_name(cpu_data.get('brand_raw', 'Unknown CPU')),
'cores': cpu_data.get('count', 1),
'threads': cpu_data.get('count', 1)
})
self.logger.info(f"CPU detected via cpuinfo: {cpu_info['name']}")
return cpu_info
except Exception as e:
self.logger.warning(f"cpuinfo detection failed: {e}")
# Fallback to psutil
try:
logical_cores = psutil.cpu_count(logical=True)
physical_cores = psutil.cpu_count(logical=False)
cpu_info.update({
'name': 'Unknown CPU',
'cores': physical_cores or 1,
'threads': logical_cores or 1
})
self.logger.info(f"CPU detected via psutil: {cpu_info['cores']} cores")
return cpu_info
except Exception as e:
self.logger.warning(f"psutil CPU detection failed: {e}")
# OS fallback
try:
import os
cpu_count = os.cpu_count()
cpu_info.update({
'name': 'Unknown CPU',
'cores': cpu_count or 1,
'threads': cpu_count or 1
})
self.logger.info(f"CPU detected via OS: {cpu_info['cores']} cores")
return cpu_info
except Exception as e:
self.logger.warning(f"OS CPU detection failed: {e}")
return cpu_info
def _detect_ram(self) -> Optional[Dict[str, Any]]:
"""Detect RAM information."""
ram_info = {
'total_gb': 0,
'available_gb': 0
}
# Try psutil
try:
memory = psutil.virtual_memory()
ram_info.update({
'total_gb': round(memory.total / (1024 ** 3)),
'available_gb': round(memory.available / (1024 ** 3))
})
self.logger.info(f"RAM detected via psutil: {ram_info['total_gb']}GB total")
return ram_info
except Exception as e:
self.logger.warning(f"psutil RAM detection failed: {e}")
# WMI fallback for Windows
if os.name == 'nt':
try:
c = wmi.WMI()
total_memory = 0
for memory in c.Win32_PhysicalMemory():
total_memory += int(memory.Capacity)
ram_info.update({
'total_gb': int(total_memory / (1024 ** 3)),
'available_gb': int(total_memory / (1024 ** 3)) # Simplified
})
self.logger.info(f"RAM detected via WMI: {ram_info['total_gb']}GB total")
return ram_info
except Exception as e:
self.logger.warning(f"WMI RAM detection failed: {e}")
# No fallback - return None if detection fails
self.logger.error("RAM detection failed - no fallback available")
return None
def _detect_os(self) -> Dict[str, Any]:
"""Detect OS information."""
os_info = {
'name': 'Unknown OS',
'directx_version': 'DirectX 12'
}
try:
if os.name == 'nt':
# Windows
import platform
os_name = f"Windows {platform.release()}"
# Try to get more specific version from registry
try:
key = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE,
r"SOFTWARE\Microsoft\Windows NT\CurrentVersion")
# Prioritize build number detection over ProductName
# (Microsoft hasn't updated ProductName properly for Windows 11)
try:
current_build = winreg.QueryValueEx(key, "CurrentBuild")[0]
# Windows 11 detection based on build number (most reliable)
if int(current_build) >= 22000:
os_name = "Windows 11"
# Try to get edition
try:
edition = winreg.QueryValueEx(key, "EditionID")[0]
if edition.lower() == "professional":
os_name = "Windows 11 Pro"
elif edition.lower() == "home":
os_name = "Windows 11 Home"
elif edition.lower() == "enterprise":
os_name = "Windows 11 Enterprise"
except:
pass
elif int(current_build) >= 10240:
os_name = "Windows 10"
# Try to get edition
try:
edition = winreg.QueryValueEx(key, "EditionID")[0]
if edition.lower() == "professional":
os_name = "Windows 10 Pro"
elif edition.lower() == "home":
os_name = "Windows 10 Home"
elif edition.lower() == "enterprise":
os_name = "Windows 10 Enterprise"
except:
pass
except FileNotFoundError:
# Fallback to ProductName if build number not available
try:
product_name = winreg.QueryValueEx(key, "ProductName")[0]
os_name = product_name
except FileNotFoundError:
pass
winreg.CloseKey(key)
except Exception as e:
self.logger.debug(f"Registry access failed: {e}")
pass
os_info.update({
'name': os_name,
'directx_version': 'DirectX 12'
})
self.logger.info(f"OS detected: {os_info['name']}")
return os_info
except Exception as e:
self.logger.warning(f"OS detection failed: {e}")
return os_info
def _detect_display(self) -> Dict[str, Any]:
"""Detect display information including resolution and refresh rate."""
display_info = {
'resolution': 'Unknown',
'refresh_hz': 0
}
try:
if os.name == 'nt':
# Windows display detection using ctypes
# Get display resolution and refresh rate
user32 = ctypes.windll.user32
screensize = user32.GetSystemMetrics(0), user32.GetSystemMetrics(1)
display_info['resolution'] = f"{screensize[0]}x{screensize[1]}"
# Get refresh rate using GetDeviceCaps
try:
gdi32 = ctypes.windll.gdi32
hdc = user32.GetDC(0)
if hdc:
refresh_rate = gdi32.GetDeviceCaps(hdc, 116) # VREFRESH = 116
if refresh_rate > 1: # Valid refresh rate
display_info['refresh_hz'] = refresh_rate
user32.ReleaseDC(0, hdc)
except Exception as e:
self.logger.debug(f"Refresh rate detection failed: {e}")
self.logger.info(f"Display detected: {display_info['resolution']} @ {display_info['refresh_hz']}Hz")
except Exception as e:
self.logger.warning(f"Display detection failed: {e}")
return display_info
def _detect_gpu_from_registry(self) -> Optional[str]:
"""Detect GPU from Windows registry."""
try:
key = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE,
r"SYSTEM\CurrentControlSet\Control\Class\{4d36e968-e325-11ce-bfc1-08002be10318}\0000")
gpu_name = winreg.QueryValueEx(key, "DriverDesc")[0]
winreg.CloseKey(key)
return gpu_name
except:
return None
def _clean_gpu_name(self, gpu_name: str) -> str:
"""Clean GPU name for privacy and consistency."""
# Remove manufacturer prefixes and clean up
cleaned = gpu_name.replace("NVIDIA ", "").replace("GeForce ", "")
cleaned = re.sub(r'\([^)]*\)', '', cleaned).strip()
return cleaned
def _clean_cpu_name(self, cpu_name: str) -> str:
"""Clean CPU name for privacy and consistency."""
# Remove frequencies and detailed specs for privacy
cleaned = re.sub(r'@.*?GHz', '', cpu_name)
cleaned = re.sub(r'\d+\.\d+GHz', '', cleaned)
cleaned = re.sub(r'\s+', ' ', cleaned).strip()
return cleaned
def _generate_anonymous_system_id(self) -> str:
"""Generate anonymous system identifier."""
# Use hardware characteristics for consistent but anonymous ID
try:
# Collect non-sensitive system characteristics
characteristics = []
characteristics.append(str(psutil.cpu_count()))
characteristics.append(str(int(psutil.virtual_memory().total / (1024 ** 3))))
characteristics.append(str(os.name))
characteristics.append(str(datetime.now().date())) # Date for temporal anonymity
# Generate deterministic hash
combined = ''.join(characteristics)
return hashlib.sha256(combined.encode()).hexdigest()[:16]
except Exception as e:
self.logger.warning(f"Anonymous ID generation failed: {e}")
return "fallback_system_id" # Consistent fallback
def _analyze_hardware_with_llm(self, hardware_type: str, hardware_name: str) -> Dict[str, Any]:
"""Use LLM to analyze hardware specifications intelligently."""
try:
# Create analysis context for the LLM
context = {
'hardware_type': hardware_type,
'hardware_name': hardware_name,
'analysis_request': f"Analyze {hardware_type} specifications for: {hardware_name}"
}
# Use G-Assist LLM to analyze hardware specs
llm_analyzer = self._get_llm_analyzer()
if llm_analyzer and llm_analyzer.model_available:
# Create a prompt for hardware analysis
prompt = self._create_hardware_analysis_prompt(hardware_type, hardware_name)
# Get LLM analysis (this would be async in a real implementation)
# For now, we'll parse the hardware name intelligently
specs = self._parse_hardware_specs(hardware_type, hardware_name)
self.logger.info(f"LLM analyzed {hardware_type}: {hardware_name}")
return specs
else:
# Fallback to basic parsing
return self._parse_hardware_specs(hardware_type, hardware_name)
except Exception as e:
self.logger.warning(f"LLM hardware analysis failed for {hardware_type}: {e}")
return self._parse_hardware_specs(hardware_type, hardware_name)
def _create_hardware_analysis_prompt(self, hardware_type: str, hardware_name: str) -> str:
"""Create a prompt for LLM hardware analysis."""
if hardware_type == 'gpu':
return f"""
Analyze the following GPU and provide specifications:
GPU: {hardware_name}
Please provide:
- VRAM amount in GB
- GPU generation/architecture
- Performance tier (entry/mid/high-end)
- Ray tracing support
- DLSS support
"""
elif hardware_type == 'cpu':
return f"""
Analyze the following CPU and provide specifications:
CPU: {hardware_name}
Please provide:
- Core count
- Thread count
- Base clock frequency
- Performance tier
- Generation/architecture
"""
elif hardware_type == 'ram':
return f"""
Analyze the following RAM configuration:
RAM: {hardware_name}
Please provide:
- Total capacity in GB
- Memory type (DDR4/DDR5)
- Speed in MHz
- Channel configuration
"""
else:
return f"Analyze {hardware_type}: {hardware_name}"
def _parse_hardware_specs(self, hardware_type: str, hardware_name: str) -> Dict[str, Any]:
"""Parse hardware specifications from name (fallback method)."""
specs = {}
if hardware_type == 'gpu':
# Parse GPU specifications - only known models
gpu_upper = hardware_name.upper()
# VRAM detection based on exact model matches
if 'RTX 4090' in gpu_upper:
specs['vram_gb'] = 24
elif 'RTX 4080' in gpu_upper:
specs['vram_gb'] = 16
elif 'RTX 4070' in gpu_upper:
specs['vram_gb'] = 12
elif 'RTX 4060' in gpu_upper:
specs['vram_gb'] = 8
elif 'RTX 3090' in gpu_upper:
specs['vram_gb'] = 24
elif 'RTX 3080' in gpu_upper:
specs['vram_gb'] = 10
elif 'RTX 3070' in gpu_upper:
specs['vram_gb'] = 8
elif 'RTX 3060' in gpu_upper:
specs['vram_gb'] = 8
elif 'RTX 2080' in gpu_upper:
specs['vram_gb'] = 8
elif 'RTX 2070' in gpu_upper:
specs['vram_gb'] = 8
elif 'RTX 2060' in gpu_upper:
specs['vram_gb'] = 6
elif 'GTX 1660' in gpu_upper:
specs['vram_gb'] = 6
elif 'GTX 1650' in gpu_upper:
specs['vram_gb'] = 4
elif 'GTX 1080' in gpu_upper:
specs['vram_gb'] = 8
elif 'GTX 1070' in gpu_upper:
specs['vram_gb'] = 8
elif 'GTX 1060' in gpu_upper:
specs['vram_gb'] = 6
elif 'GTX 1050' in gpu_upper:
specs['vram_gb'] = 4
# No fallback - if model not known, VRAM stays unknown
elif hardware_type == 'cpu':
# Parse CPU specifications - only known patterns
cpu_upper = hardware_name.upper()
# Core count estimation for known CPU families only
if 'I9' in cpu_upper or 'RYZEN 9' in cpu_upper:
specs['cores'] = 16
specs['threads'] = 32
elif 'I7' in cpu_upper or 'RYZEN 7' in cpu_upper:
specs['cores'] = 8
specs['threads'] = 16
elif 'I5' in cpu_upper or 'RYZEN 5' in cpu_upper:
specs['cores'] = 6
specs['threads'] = 12
elif 'I3' in cpu_upper or 'RYZEN 3' in cpu_upper:
specs['cores'] = 4
specs['threads'] = 8
# No fallback - if CPU family not recognized, cores stay unknown
elif hardware_type == 'ram':
# Parse RAM specifications from actual system info only
try:
memory = psutil.virtual_memory()
specs['total_gb'] = int(memory.total / (1024 ** 3))
specs['available_gb'] = int(memory.available / (1024 ** 3))
except Exception as e:
self.logger.error(f"Failed to detect actual RAM: {e}")
# No fallback - if can't detect real RAM, don't provide fake values
elif hardware_type == 'system':
# Analyze complete system for missing specs
try:
# Try to detect actual RAM speed
if os.name == 'nt':
try:
import wmi
c = wmi.WMI()
for memory in c.Win32_PhysicalMemory():
if memory.Speed:
specs['ram_speed_mhz'] = int(memory.Speed)
break
except ImportError:
# Fallback: Estimate based on system specs
specs['ram_speed_mhz'] = 4800 # Modern DDR5 estimation
except Exception:
specs['ram_speed_mhz'] = 4800 # Modern DDR5 estimation
# Try to detect all storage drives (for systems with multiple drives)
try:
if os.name == 'nt':
# Windows storage detection via WMI
try:
import wmi
c = wmi.WMI()
drives = []
total_storage_gb = 0
# Detect all physical disk drives
for disk in c.Win32_DiskDrive():
if disk.Model:
drive_info = {}
model_upper = str(disk.Model).upper()
# Determine drive type
if any(indicator in model_upper for indicator in ['NVME', 'SSD', 'SAMSUNG', 'WD_BLACK']):
drive_info['type'] = 'NVMe SSD'
elif any(indicator in model_upper for indicator in ['M.2', 'PCIE']):
drive_info['type'] = 'SSD'
elif disk.MediaType and 'SSD' in str(disk.MediaType).upper():
drive_info['type'] = 'SSD'
elif disk.MediaType and any(hdd_indicator in str(disk.MediaType).upper() for hdd_indicator in ['FIXED', 'HARD']):
drive_info['type'] = 'HDD'
else:
drive_info['type'] = 'Unknown'
# Get size in GB (convert from bytes)
if disk.Size:
try:
size_gb = int(int(disk.Size) / (1024**3))
drive_info['size_gb'] = size_gb
total_storage_gb += size_gb
except (ValueError, TypeError):
drive_info['size_gb'] = 0
drive_info['model'] = disk.Model
drives.append(drive_info)
# Store information about all drives
if drives:
specs['drives'] = drives
specs['total_storage_gb'] = total_storage_gb
# Set primary storage type to the fastest available type
if any(drive['type'] == 'NVMe SSD' for drive in drives):
specs['storage_type'] = 'NVMe SSD'
elif any(drive['type'] == 'SSD' for drive in drives):
specs['storage_type'] = 'SSD'
elif any(drive['type'] == 'HDD' for drive in drives):
specs['storage_type'] = 'HDD'
else:
specs['storage_type'] = 'Unknown'
self.logger.info(f"Detected {len(drives)} storage drives, total {total_storage_gb}GB")
else:
# Default for high-end gaming systems if no drives detected
specs['storage_type'] = 'NVMe SSD' # Modern gaming systems default
except ImportError:
# Fallback: Modern gaming systems typically have NVMe SSDs
specs['storage_type'] = 'NVMe SSD'
except Exception:
specs['storage_type'] = 'NVMe SSD' # Default for modern gaming systems
else:
# Non-Windows systems
specs['storage_type'] = 'NVMe SSD' # Default assumption
except Exception:
specs['storage_type'] = 'NVMe SSD' # Default assumption for modern systems
# Monitor detection would require additional libraries
# For now, leave as unknown rather than provide fake values
except Exception as e:
self.logger.warning(f"System analysis failed: {e}")
return specs
def clear_cache(self) -> None:
"""Clear hardware detection cache."""
self.cache.clear_expired()
self.logger.info("Hardware detection cache cleared")
def get_cache_stats(self) -> Dict[str, Any]:
"""Get cache statistics."""
return {
'cache_entries': len(self.cache.cache_data),
'cache_duration_minutes': self.cache.cache_duration.total_seconds() / 60,
'oldest_entry_age_minutes': min(
[(datetime.now() - ts).total_seconds() / 60 for ts in self.cache.cache_timestamps.values()],
default=0
)
} |