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cf93910 c476eae cf93910 9e4f343 cf93910 | 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 | """
Application-wide settings for SanketSetu backend.
Override any value by setting the corresponding environment variable.
"""
from __future__ import annotations
import os
from pathlib import Path
# ---------------------------------------------------------------------------
# TensorFlow / Keras startup optimisations
# Set these BEFORE any import that might pull in tensorflow.
# ---------------------------------------------------------------------------
os.environ.setdefault("KERAS_BACKEND", "tensorflow")
os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "3") # silence C++ TF logs
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "") # CPU-only: skip GPU scan
os.environ.setdefault("TF_ENABLE_ONEDNN_OPTS", "0") # disable oneDNN init check
os.environ.setdefault("OMP_NUM_THREADS", "4") # cap CPU thread pool
# ---------------------------------------------------------------------------
# Paths
# ---------------------------------------------------------------------------
BASE_DIR = Path(__file__).resolve().parent.parent.parent # repo root
WEIGHTS_DIR = os.getenv("WEIGHTS_DIR", str(BASE_DIR))
# Individual model paths (relative to repo root)
# Note: the actual files on disk use .pth extension (identical content to .pkl)
PIPELINE_A_MODEL = os.path.join(WEIGHTS_DIR, "Mediapipe_XGBoost", "model.pth")
PIPELINE_B_AE = os.path.join(WEIGHTS_DIR, "CNN_Autoencoder_LightGBM", "autoencoder_model.pth")
PIPELINE_B_LGBM = os.path.join(WEIGHTS_DIR, "CNN_Autoencoder_LightGBM", "lgbm_model.pth")
PIPELINE_C_CNN = os.path.join(WEIGHTS_DIR, "CNN_PreTrained", "cnn_model.pth")
PIPELINE_C_SVM = os.path.join(WEIGHTS_DIR, "CNN_PreTrained", "svm_model.pth")
# ---------------------------------------------------------------------------
# Inference thresholds
# ---------------------------------------------------------------------------
# If Pipeline A confidence falls below this, Pipeline B is also called.
CONFIDENCE_THRESHOLD: float = float(os.getenv("CONFIDENCE_THRESHOLD", "0.70"))
# If ensemble after B still below this, Pipeline C is attempted (if image provided).
SECONDARY_THRESHOLD: float = float(os.getenv("SECONDARY_THRESHOLD", "0.60"))
# ---------------------------------------------------------------------------
# Pipeline mode
# ---------------------------------------------------------------------------
# "A" → only XGBoost (fastest)
# "B" → only Autoencoder + LGBM
# "C" → only CNN + SVM (image required)
# "ensemble" → A first, fallback to B, then C
PIPELINE_MODE: str = os.getenv("PIPELINE_MODE", "ensemble")
# ---------------------------------------------------------------------------
# WebSocket / server
# ---------------------------------------------------------------------------
MAX_WS_CONNECTIONS: int = int(os.getenv("MAX_WS_CONNECTIONS", "100"))
WS_SEND_RATE_LIMIT: int = int(os.getenv("WS_SEND_RATE_LIMIT", "15")) # max frames/sec per client
# Allowed CORS origins (comma-separated list in env var)
# Default includes Vercel frontend and the Hugging Face Space URL.
# Replace YOUR_HF_USERNAME and YOUR_SPACE_NAME with your actual HF values.
_cors_env = os.getenv(
"CORS_ORIGINS",
"http://localhost:5173,http://localhost:3000,"
"https://sanketsetu.vercel.app,"
"https://devrajsinh2012-sanket-setu.hf.space",
)
CORS_ORIGINS: list[str] = [o.strip() for o in _cors_env.split(",") if o.strip()]
# ---------------------------------------------------------------------------
# Logging
# ---------------------------------------------------------------------------
LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
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