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config.py - Central configuration for Multimodal Surveillance Intelligence System
"""
import os
import torch
from pathlib import Path
from pydantic_settings import BaseSettings
from pydantic import Field
from loguru import logger
class Settings(BaseSettings):
# ── App ───────────────────────────────────────────────
APP_NAME: str = "Multimodal Surveillance Intelligence System"
APP_VERSION: str = "1.0.0"
DEBUG: bool = False
HOST: str = "0.0.0.0"
PORT: int = 8000
# ── Database (NeonDB PostgreSQL) ───────────────────────
DATABASE_URL: str = Field(
default="postgresql+asyncpg://user:password@hostname/dbname?ssl=require",
description="Database connection URL"
)
DB_ECHO: bool = False
# ── Security ───────────────────────────────────────────
SECRET_KEY: str = Field(default="change-in-production-super-secret-key", env="SECRET_KEY")
API_KEY: str = Field(default="surveillance-api-key-2024", env="API_KEY")
# ── Model Paths / Cache ───────────────────────────────
MODEL_CACHE_DIR: str = Field(default="./model_cache", env="HF_HOME")
# ── Vision Models ─────────────────────────────────────
DETECTION_MODEL: str = "facebook/detr-resnet-50"
REID_MODEL: str = "google/vit-base-patch16-224"
CLIP_MODEL: str = "openai/clip-vit-base-patch32"
DETECTION_CONFIDENCE: float = 0.7
DETECTION_BATCH_SIZE: int = 4
# ── NLP Models ────────────────────────────────────────
SEMANTIC_SEARCH_MODEL: str = "sentence-transformers/all-MiniLM-L6-v2"
QA_MODEL: str = "deepset/roberta-base-squad2"
REPORT_MODEL: str = "google/flan-t5-base"
SUMMARIZER_MODEL: str = "facebook/bart-large-cnn"
# ── Audio Models (Optional) ───────────────────────────
WHISPER_MODEL: str = "openai/whisper-small"
AUDIO_CLASS_MODEL: str = "superb/wav2vec2-base-superb-ks"
TTS_MODEL: str = "microsoft/speecht5_tts"
ENABLE_AUDIO: bool = False
# ── FAISS ─────────────────────────────────────────────
FAISS_INDEX_PATH: str = "./faiss_indexes"
REID_EMBEDDING_DIM: int = 768 # ViT-base hidden size
CLIP_EMBEDDING_DIM: int = 512 # CLIP embedding size
SEARCH_EMBEDDING_DIM: int = 384 # MiniLM embedding size
FAISS_NPROBE: int = 10
# ── Tracking ──────────────────────────────────────────
TRACKER_TYPE: str = "bytetrack" # "bytetrack" or "deepsort"
TRACK_THRESH: float = 0.5
TRACK_BUFFER: int = 30
MATCH_THRESH: float = 0.8
# ── WebSocket / Cameras ───────────────────────────────
MAX_CAMERAS: int = 16
FRAME_QUEUE_SIZE: int = 30
FPS_TARGET: int = 30
# ── Anomaly Detection ─────────────────────────────────
ANOMALY_THRESHOLD: float = 0.75
# ── CORS ──────────────────────────────────────────────
CORS_ORIGINS: list = ["*"]
class Config:
env_file = ".env"
env_file_encoding = "utf-8"
case_sensitive = False
settings = Settings()
# ── Device Detection ──────────────────────────────────────────
def get_device() -> torch.device:
if torch.cuda.is_available():
device = torch.device("cuda")
logger.info(f"🚀 GPU detected: {torch.cuda.get_device_name(0)} | VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
else:
device = torch.device("cpu")
logger.warning("⚠️ No GPU detected — running on CPU. Performance may be degraded.")
return device
DEVICE = get_device()
# ── Paths ─────────────────────────────────────────────────────
BASE_DIR = Path(__file__).resolve().parent
FAISS_DIR = BASE_DIR / settings.FAISS_INDEX_PATH
MODEL_CACHE = BASE_DIR / settings.MODEL_CACHE_DIR
FAISS_DIR.mkdir(parents=True, exist_ok=True)
MODEL_CACHE.mkdir(parents=True, exist_ok=True)
# Set HuggingFace cache
os.environ["HF_HOME"] = str(MODEL_CACHE)
os.environ["TRANSFORMERS_CACHE"] = str(MODEL_CACHE)
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