refactor api

api/src/production/settings.py

@@ -0,0 +1,57 @@
+"""All server settings in one place. Values marked (env) can be set with an
+environment variable of the same name, e.g. in compose.yaml."""
+
+import os
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+API_ROOT = Path(__file__).resolve().parents[2]    # api/ locally, /app in Docker
+
+BUILD_PATH = Path(os.getenv("BUILD_PATH", API_ROOT / "build_models"))     # (env)
+DATABASE_DIR = Path(os.getenv("DATABASE_DIR", API_ROOT / "database"))    # (env)
+DATABASE = DATABASE_DIR / "reviews.db"
+
+load_dotenv(DATABASE_DIR / ".env")
+API_KEY = os.getenv("API_KEY")  # (env) if missing, protected endpoints reject every request
+
+ORIGINS = [
+    "https://marvinkrausser.com",
+]
+
+# ---- Models ----
+BIRD_MODEL_FILE = "bird_cnn"
+BIRD_IMAGE_SIZE = 64
+BIRD_C_HIDDEN = 16
+
+FACE_MODEL_FILE = "face_detection_yolo"
+FACE_IMAGE_SIZE = 64
+FACE_GRID = 6
+FACE_C_HIDDEN = 16
+FACE_SIZE_ACTIVATION = "exp"   # must match how the deployed weights were trained
+FACE_THRESHOLD = 0.9
+
+# ---- Resource limits (the server is weak, keep everything small and bounded) ----
+# Number of predictions (bird or face) that may run at the same time. (env)
+PARALLEL_INFERENCES = max(1, int(os.getenv("PARALLEL_INFERENCES", "1")))
+# Extra requests allowed to wait for a free slot; anything beyond is rejected. (env)
+QUEUED_INFERENCES = max(0, int(os.getenv("QUEUED_INFERENCES", "1")))
+# PyTorch threads used by EACH running prediction. Total CPU use is roughly
+# PARALLEL_INFERENCES * TORCH_THREADS, so keep the product <= your CPU cores. (env)
+TORCH_THREADS = max(1, int(os.getenv("TORCH_THREADS", "1")))
+
+MAX_UPLOAD_BYTES = int(max(1, float(os.getenv("MAX_UPLOAD_MEGABYTES", "15"))) * 1024 * 1024)  # (env)
+MAX_FRAME_BYTES = 1 * 1024 * 1024
+MAX_IMAGE_PIXELS = 20_000_000
+DECODE_DRAFT_SIZE = (256, 256)  # JPEG decodes at reduced scale, still larger than the 64px model input
+
+MAX_WEBSOCKETS = 3
+MAX_WEBSOCKETS_PER_IP = 1
+FACE_MAX_FPS = float(os.getenv("FACE_MAX_FPS", "5.5"))  # (env) per websocket connection
+FACE_MIN_INTERVAL = 1 / FACE_MAX_FPS
+
+# ---- Rate limits: at most LIMIT requests per WINDOW seconds per IP ----
+PREDICT_RATE_LIMIT = max(1, int(os.getenv("PREDICT_RATE_LIMIT", "10")))          # (env)
+PREDICT_RATE_WINDOW = max(1.0, float(os.getenv("PREDICT_RATE_WINDOW", "60")))    # (env)
+REVIEW_RATE_LIMIT = max(1, int(os.getenv("REVIEW_RATE_LIMIT", "5")))             # (env)
+REVIEW_RATE_WINDOW = max(1.0, float(os.getenv("REVIEW_RATE_WINDOW", "60")))      # (env)