Full-stack application for predicting where humans look in images using DeepGaze saliency models. Includes heatmap overlays, gaze sequence prediction, hotspot detection, AOI analysis, rule-based insights, optional Claude AI design analysis, and professional PDF report generation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
30 lines
889 B
Python
30 lines
889 B
Python
from pydantic_settings import BaseSettings
|
|
|
|
|
|
class Settings(BaseSettings):
|
|
DATABASE_URL: str = "postgresql+asyncpg://olivas:olivas@localhost:5453/olivas"
|
|
UPLOAD_DIR: str = "./data/uploads"
|
|
DEVICE: str = "auto" # auto | cpu | cuda
|
|
ANTHROPIC_API_KEY: str = ""
|
|
CORS_ORIGINS: str = "http://localhost:1577"
|
|
BACKEND_HOST: str = "0.0.0.0"
|
|
BACKEND_PORT: int = 8000
|
|
|
|
@property
|
|
def device(self) -> str:
|
|
if self.DEVICE == "auto":
|
|
try:
|
|
import torch
|
|
return "cuda" if torch.cuda.is_available() else "cpu"
|
|
except ImportError:
|
|
return "cpu"
|
|
return self.DEVICE
|
|
|
|
@property
|
|
def cors_origins_list(self) -> list[str]:
|
|
return [o.strip() for o in self.CORS_ORIGINS.split(",")]
|
|
|
|
model_config = {"env_file": ".env", "env_file_encoding": "utf-8"}
|
|
|
|
|
|
settings = Settings()
|