Delete src/config.py

src/config.py

@@ -1,172 +0,0 @@
-"""
-src/config.py — Single source of truth for every constant and environment variable.
-
-Rules:
-  - No magic numbers anywhere else in the codebase.
-  - Every threshold, dimension, and limit is documented here with the reason it exists.
-  - Environment variables are loaded once at startup; never call os.getenv() elsewhere.
-"""
-
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-
-
-# ════════════════════════════════════════════════════════════════════
-#  ENVIRONMENT VARIABLES
-# ════════════════════════════════════════════════════════════════════
-
-DEFAULT_PINECONE_KEY   = os.getenv("DEFAULT_PINECONE_KEY",   "")
-DEFAULT_CLOUDINARY_URL = os.getenv("DEFAULT_CLOUDINARY_URL", "")
-SUPABASE_URL           = os.getenv("SUPABASE_URL",           "")
-SUPABASE_SERVICE_KEY   = os.getenv("SUPABASE_SERVICE_KEY",   "")
-HF_TOKEN               = os.getenv("HF_TOKEN",               "")
-
-# Set ENABLE_ADAFACE=1 to enable full ArcFace+AdaFace 1024-D fusion.
-# When disabled, ArcFace(512) + zeros(512) = 1024-D (fully functional,
-# zero-padding is cosine-neutral and doesn't corrupt similarity scores).
-ENABLE_ADAFACE = os.getenv("ENABLE_ADAFACE", "0").strip() == "1"
-
-# InsightFace ONNX runtime is NOT thread-safe. Keep at 1 unless you
-# switch to a thread-safe inference backend (e.g. Triton, torchserve).
-MAX_CONCURRENT_INFERENCES = int(os.getenv("MAX_CONCURRENT_INFERENCES", "1"))
-
-
-# ════════════════════════════════════════════════════════════════════
-#  PINECONE INDEX NAMES
-# ════════════════════════════════════════════════════════════════════
-
-IDX_FACES   = "enterprise-faces"
-IDX_OBJECTS = "enterprise-objects"
-
-
-# ════════════════════════════════════════════════════════════════════
-#  VECTOR DIMENSIONS
-#  These MUST match the Pinecone index dimensions.
-#  If you change a dimension you MUST reset the corresponding index.
-# ════════════════════════════════════════════════════════════════════
-
-FACE_DIM       = 512   # ArcFace-R100 raw output
-ADAFACE_DIM    = 512   # AdaFace IR-50 raw output
-FUSED_FACE_DIM = 1024  # concat(ArcFace, AdaFace) → stored in enterprise-faces
-
-SIGLIP_DIM    = 768    # google/siglip-base-patch16-224 output
-DINOV2_DIM    = 768    # facebook/dinov2-base CLS token output
-FUSED_OBJ_DIM = 1536   # concat(SigLIP, DINOv2) → stored in enterprise-objects
-
-# Allow env-var overrides in case the index was created at different dims
-IDX_FACES_DIM   = int(os.getenv("IDX_FACES_DIM",   str(FUSED_FACE_DIM)))
-IDX_OBJECTS_DIM = int(os.getenv("IDX_OBJECTS_DIM", str(FUSED_OBJ_DIM)))
-
-
-# ════════════════════════════════════════════════════════════════════
-#  OBJECT LANE — YOLO + EMBEDDING
-# ════════════════════════════════════════════════════════════════════
-
-# Longest edge of an image before embedding.
-# 640px balances accuracy vs GPU memory; SigLIP/DINOv2 were pretrained at 224px
-# so going above ~640 yields diminishing returns.
-MAX_IMAGE_SIZE = 640
-
-# Max YOLO segmentation crops per image (full image is always crop 0).
-# Beyond ~6 crops the embeddings become redundant and slow down inference.
-MAX_CROPS = 6
-
-YOLO_PERSON_CLASS_ID = 0    # COCO class 0 = "person"
-YOLO_MIN_CROP_PX     = 30   # ignore detections smaller than 30×30 px
-YOLO_CONF_THRESHOLD  = 0.5  # YOLO confidence gate
-
-
-# ════════════════════════════════════════════════════════════════════
-#  FACE LANE — DETECTION
-# ════════════════════════════════════════════════════════════════════
-
-# Multi-scale pyramid: SCRFD runs at each resolution, results are merged
-# and deduplicated by IoU. Larger scales catch smaller faces.
-# DET_SIZE_PRIMARY is the InsightFace prep() size; the others are used
-# by temporarily overriding det_model.input_size mid-pipeline.
-DET_SIZE_PRIMARY   = (1280, 1280)
-DET_SIZE_SECONDARY = (960,  960)
-DET_SIZE_FALLBACK  = (640,  640)
-DET_SCALES         = [DET_SIZE_PRIMARY, DET_SIZE_SECONDARY, DET_SIZE_FALLBACK]
-
-# Suppress duplicate detections across scales/flips.
-# IoU > 0.45 → same face detected twice; keep the higher-confidence one.
-IOU_DEDUP_THRESHOLD = 0.45
-
-
-# ════════════════════════════════════════════════════════════════════
-#  FACE LANE — QUALITY GATES (applied during detection/encoding)
-# ════════════════════════════════════════════════════════════════════
-
-# Faces smaller than this (in either dimension) carry too little information
-# for a reliable 512-D embedding.
-MIN_FACE_SIZE = 20  # px
-
-# Hard limit on faces stored per source image.
-MAX_FACES_PER_IMAGE = 12
-
-# InsightFace det_score gate. Lowered from 0.60 to accept partially
-# occluded faces, angles, sunglasses, and smiles.
-FACE_QUALITY_GATE = 0.35
-
-
-# ════════════════════════════════════════════════════════════════════
-#  FACE LANE — SEARCH THRESHOLDS (applied to Pinecone cosine scores)
-#  These are separate from FACE_QUALITY_GATE (which gates detection).
-#  These gate how similar a stored face must be to the query face.
-# ════════════════════════════════════════════════════════════════════
-
-# det_score ≥ 0.85 → high-quality frontal face → use stricter match threshold
-FACE_DET_SCORE_HQ_SPLIT = 0.85
-FACE_THRESHOLD_HIGH     = 0.40   # for high-quality faces
-FACE_THRESHOLD_LOW      = 0.32   # for lower-quality faces
-
-FACE_TOP_K_FETCH = 50  # fetch more candidates then filter; multi-face merge needs headroom
-
-
-# ════════════════════════════════════════════════════════════════════
-#  OBJECT LANE — SEARCH THRESHOLDS
-# ════════════════════════════════════════════════════════════════════
-
-OBJECT_SCORE_THRESHOLD = 0.45
-OBJECT_TOP_K           = 10
-
-
-# ════════════════════════════════════════════════════════════════════
-#  FACE CROP THUMBNAILS
-#  Stored in Pinecone metadata as base64 JPEG for UI display.
-# ════════════════════════════════════════════════════════════════════
-
-FACE_CROP_THUMB_SIZE = 112   # px — matches face model input size
-FACE_CROP_QUALITY    = 80    # JPEG quality; balances size vs clarity
-FACE_CROP_PADDING    = 0.20  # 20% padding around tight bbox for UI legibility
-ADAFACE_CROP_PADDING = 0.10  # 10% padding for model input (wants tight crop)
-
-
-# ════════════════════════════════════════════════════════════════════
-#  API LIMITS
-# ════════════════════════════════════════════════════════════════════
-
-# Hard cap on files per upload request. Each file spawns concurrent
-# Cloudinary + AI tasks; uncapped batches can exhaust RAM.
-MAX_FILES_PER_UPLOAD = 20
-
-
-# ════════════════════════════════════════════════════════════════════
-#  LRU PINECONE CLIENT POOL
-# ════════════════════════════════════════════════════════════════════
-
-# Each Pinecone() client holds open TCP connections.
-# Too many exhaust file descriptors; 64 covers typical multi-tenant load.
-PINECONE_POOL_MAX = 64
-
-
-# ════════════════════════════════════════════════════════════════════
-#  IN-MEMORY INFERENCE CACHE
-# ═══════════════════════════════════════════════════════════���════════
-
-# Cache keyed by (file_hash, detect_faces). 128 entries ≈ last ~128 unique
-# images; prevents re-running multi-second AI inference on duplicate uploads.
-INFERENCE_CACHE_SIZE = 128