Spaces:
Running
Running
File size: 1,838 Bytes
9636a02 | 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 | """Centralised application configuration loaded from environment variables."""
from __future__ import annotations
from functools import lru_cache
from dotenv import load_dotenv
from pydantic import ConfigDict
from pydantic_settings import BaseSettings
load_dotenv()
class Settings(BaseSettings):
"""Application settings schema loaded from environment variables.
Params:
None: Values are read from process environment and optional `.env` file.
Returns:
Settings: Parsed and validated settings object.
"""
model_config = ConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
MEDASR_MODEL_ID: str = "google/medasr"
MEDGEMMA_4B_MODEL_ID: str = "google/medgemma-1.5-4b-it"
MEDGEMMA_27B_MODEL_ID: str = "google/medgemma-27b-text-it"
HF_TOKEN: str = ""
QUANTIZE_4BIT: bool = True
USE_FLASH_ATTENTION: bool = True
FHIR_SERVER_URL: str = "http://localhost:8080/fhir"
USE_MOCK_FHIR: bool = True
FHIR_TIMEOUT_S: int = 10
APP_HOST: str = "0.0.0.0"
APP_PORT: int = 7860
LOG_LEVEL: str = "INFO"
MAX_AUDIO_DURATION_S: int = 1800
PIPELINE_TIMEOUT_S: int = 120
DOC_GEN_MAX_TOKENS: int = 2048
DOC_GEN_TEMPERATURE: float = 0.3
WANDB_API_KEY: str = ""
WANDB_PROJECT: str = "clarke-finetuning"
LORA_RANK: int = 16
LORA_ALPHA: int = 32
LORA_DROPOUT: float = 0.05
TRAINING_EPOCHS: int = 3
LEARNING_RATE: float = 2e-4
BATCH_SIZE: int = 2
GRAD_ACCUM_STEPS: int = 8
MAX_SEQ_LENGTH: int = 4096
@lru_cache(maxsize=1)
def get_settings() -> Settings:
"""Return a cached settings instance for process-wide reuse.
Params:
None: Reads values from environment variables and `.env` if present.
Returns:
Settings: Cached configuration object.
"""
return Settings()
|