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9c1c0ef | 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 | from __future__ import annotations
from functools import lru_cache
from pathlib import Path
from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict
class Settings(BaseSettings):
model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
app_name: str = "DataPilot AI"
environment: str = "development"
artifact_root: Path = Path("artifacts")
database_url: str = "sqlite:///artifacts/datapilot.db"
max_upload_mb: int = Field(default=25, ge=1, le=250)
max_rows: int = Field(default=100_000, ge=100)
max_columns: int = Field(default=250, ge=2)
max_categories_per_feature: int = Field(default=100, ge=10, le=10_000)
max_encoded_features: int = Field(default=5_000, ge=100, le=100_000)
api_key: str | None = None
requests_per_minute: int = Field(default=30, ge=1, le=10_000)
random_state: int = 42
test_size: float = Field(default=0.2, gt=0.05, lt=0.5)
max_critic_retries: int = Field(default=1, ge=0, le=3)
min_classification_score: float = 0.55
min_regression_score: float = 0.15
optuna_trials: int = Field(default=8, ge=0, le=50)
enable_mlflow: bool = False
mlflow_tracking_uri: str = "file:./artifacts/mlruns"
gemini_api_key: str | None = None
gemini_model: str = "gemini-2.5-flash"
cors_origins: str = "http://localhost:8501"
def ensure_directories(self) -> None:
self.artifact_root.mkdir(parents=True, exist_ok=True)
@lru_cache
def get_settings() -> Settings:
settings = Settings()
settings.ensure_directories()
return settings
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