File size: 3,071 Bytes
4a8b134 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | """
Application Configuration
"""
import os
from typing import Optional
import logging
logger = logging.getLogger(__name__)
def _detect_gpu():
"""Detect if GPU/CUDA is available"""
try:
import torch
return torch.cuda.is_available()
except (ImportError, Exception):
return False
def _get_max_drugs(use_gpu: bool) -> int:
"""Get max drugs based on GPU availability"""
if use_gpu:
return 600 # GPU can handle more drugs
else:
return 200 # CPU limited for performance
class Settings:
"""Application settings"""
# Application Info
APP_NAME: str = "Drug Repurposing AI System"
APP_VERSION: str = "1.0.0"
APP_DESCRIPTION: str = "AI-powered drug repurposing system using Deep Learning and Open Targets"
# API Configuration
API_TITLE: str = "Drug Repurposing API"
API_VERSION: str = "v1"
DEBUG: bool = os.getenv("DEBUG", "False").lower() == "true"
# Server Configuration
HOST: str = os.getenv("HOST", "0.0.0.0")
PORT: int = int(os.getenv("PORT", "8000"))
# API Keys and URLs
OPENTARGETS_API_URL: str = "https://api.platform.opentargets.org/api/v4/graphql"
UNIPROT_API_URL: str = "https://rest.uniprot.org/uniprotkb/search"
# GPU Detection
HAS_GPU: bool = _detect_gpu()
DEVICE: str = "cuda" if HAS_GPU else "cpu"
# Model Configuration
DEEP_PURPOSE_MODEL: str = os.getenv("DEEP_PURPOSE_MODEL", "MPNN_CNN_BindingDB")
USE_MOCK_MODEL: bool = os.getenv("USE_MOCK_MODEL", "False").lower() == "true" # PRODUCTION: Always False
USE_MOCK_DRUGS: bool = os.getenv("USE_MOCK_DRUGS", "False").lower() == "true" # PRODUCTION: Always False
# Screening Parameters
DEFAULT_TOP_TARGETS: int = 10
DEFAULT_TOP_RESULTS: int = 15
DEFAULT_MIN_SCORE: float = 0.0
MAX_TARGETS: int = 50
MAX_DRUGS_FOR_DEMO: int = _get_max_drugs(HAS_GPU)
BATCH_SIZE: int = 32 if HAS_GPU else 8 # Batch size for predictions
# TDC Configuration
TDC_DATASET: str = os.getenv("TDC_DATASET", "Half_Life_Obach")
TDC_TIMEOUT: int = 300 # Timeout for TDC downloads in seconds
# Logging Configuration
LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
LOG_FORMAT: str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
# CORS Configuration
CORS_ORIGINS: list = ["*"]
CORS_CREDENTIALS: bool = True
CORS_METHODS: list = ["*"]
CORS_HEADERS: list = ["*"]
# Timeout Settings
API_TIMEOUT: int = 60
REQUEST_TIMEOUT: int = 300
# Production Mode
PRODUCTION_MODE: bool = os.getenv("PRODUCTION_MODE", "False").lower() == "true"
@classmethod
def get_settings(cls) -> 'Settings':
"""Get application settings instance"""
return cls()
settings = Settings.get_settings()
# Log GPU status on startup
if settings.HAS_GPU:
logger.info(f"✅ GPU/CUDA detected. Max drugs: {settings.MAX_DRUGS_FOR_DEMO}")
else:
logger.info(f"⚠️ No GPU detected. CPU mode. Max drugs: {settings.MAX_DRUGS_FOR_DEMO}")
|