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File size: 1,540 Bytes
4fc93b8 94ddfa7 4fc93b8 7c5da40 c0426da f4cb174 7c5da40 4fc93b8 | 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 | import os
from typing import Optional
from functools import lru_cache
class Settings:
"""Application settings with environment variable support."""
# Application
APP_NAME: str = "Deepfake Detection Service"
APP_VERSION: str = "1.0.0"
DEBUG: bool = os.getenv("DEBUG", "True").lower() == "true"
# Server
HOST: str = os.getenv("HOST", "127.0.0.1")
PORT: int = int(os.getenv("PORT", "8000"))
# File handling
DOWNLOAD_TIMEOUT: int = int(os.getenv("DOWNLOAD_TIMEOUT", "30"))
MAX_FILE_SIZE: int = int(os.getenv("MAX_FILE_SIZE", str(100 * 1024 * 1024))) # 100 MB
# Redis configuration (for future queuing)
REDIS_ENABLED: bool = os.getenv("REDIS_ENABLED", "False").lower() == "true"
REDIS_URL: str = os.getenv("REDIS_URL", "redis://localhost:6379")
REDIS_QUEUE_NAME: str = os.getenv("REDIS_QUEUE_NAME", "deepfake_analysis")
# Logging
LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
LOG_FILE: Optional[str] = os.getenv("LOG_FILE", None)
AVAILABLE_MODELS = {
"text": ["yaya36095/xlm-roberta-text-detector",
"almanach/xlmr-chatgptdetect-noisy",
"bibbbu/multilingual-ai-human-detector_xlm-roberta-base"],
"image": ["capcheck/ai-image-detection",
"Hemg/Deepfake-image"],
}
MAX_CONTENT_SIZES = {
"text": 5000,
"image": 100 * 1024 * 1024,
}
@lru_cache()
def get_settings() -> Settings:
"""Get cached application settings."""
return Settings()
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