src/deepfake_scanner/config.py

"""
Central configuration for DeepFakeScanner.

All runtime knobs are read from environment variables with sensible defaults
so the service can run unchanged from local dev → HF Spaces → Cloud Run.

Read this once at import; do NOT scatter os.getenv() calls throughout the code.
"""
from __future__ import annotations

import os
from dataclasses import dataclass


def _env_bool(name: str, default: bool) -> bool:
    raw = os.getenv(name)
    if raw is None:
        return default
    return raw.strip().lower() in {"1", "true", "yes", "on"}


def _env_int(name: str, default: int) -> int:
    raw = os.getenv(name)
    if raw is None:
        return default
    try:
        return int(raw)
    except ValueError:
        return default


@dataclass(frozen=True)
class Settings:
    # ------------------------------------------------------------------ API
    api_title: str = "DeepFakeScanner"
    api_version: str = "0.2.0"
    max_upload_bytes: int = _env_int("MAX_UPLOAD_BYTES", 10 * 1024 * 1024)  # 10 MB

    # --------------------------------------------------------- Image policy
    # Privacy-by-default: visitor uploads are NEVER persisted.
    # Logged-in paid users will be able to opt in later via a separate flag.
    store_uploads: bool = _env_bool("STORE_UPLOADS", False)

    # ---------------------------------------------------------- Detectors
    # Stage 1: only the CLIP-based classifier is wired up.
    # Stage 2 will flip these on as the detectors are trained / verified.
    enable_clip_detector: bool = _env_bool("ENABLE_CLIP_DETECTOR", True)
    enable_frequency_detector: bool = _env_bool("ENABLE_FREQUENCY_DETECTOR", False)
    enable_face_swap_detector: bool = _env_bool("ENABLE_FACE_SWAP_DETECTOR", False)

    # The CLIP backbone we use for feature extraction.
    # OpenAI CLIP ViT-B/32 weights are MIT-licensed (commercially safe).
    clip_model_name: str = os.getenv("CLIP_MODEL_NAME", "openai/clip-vit-base-patch32")

    # Path to the trained classifier-head checkpoint. ClipClassifier auto-
    # loads this in __init__ if the file exists. The same env var works
    # locally (dev machine) and inside the inference Docker image.
    # Stage 3A: head_v3a.pt (multi-generator: Flux+SDXL+SD3.5+AuraFlow).
    head_checkpoint_path: str = os.getenv(
        "HEAD_CHECKPOINT_PATH", "data/checkpoints/head_v3a.pt"
    )

    # Fallback for production: if the local checkpoint file isn't present,
    # ClipClassifier downloads the weights from this HF Hub model repo at
    # startup. The repo is PRIVATE — the container authenticates with the
    # HF_TOKEN secret set in the HF Space settings (huggingface_hub reads
    # HF_TOKEN from the environment automatically). Setting this to an
    # empty string disables the fallback (used in tests).
    # Stage 3A: head_v3a.pt. The Stage 2 head_v1.pt remains in the same
    # private repo for rollback — set HEAD_CHECKPOINT_HF_FILENAME=head_v1.pt
    # (and MODEL_VERSION=v0.3.0-stage2) to revert without a redeploy.
    head_checkpoint_hf_repo: str = os.getenv(
        "HEAD_CHECKPOINT_HF_REPO", "Veridicate/scanner-head-v1"
    )
    head_checkpoint_hf_filename: str = os.getenv(
        "HEAD_CHECKPOINT_HF_FILENAME", "head_v3a.pt"
    )

    # ---------------------------------------------------------- Provenance
    enable_c2pa_check: bool = _env_bool("ENABLE_C2PA_CHECK", True)

    # ------------------------------------------------------------ Storage
    # Postgres URL for scan records (None → in-memory fallback for local dev).
    database_url: str | None = os.getenv("DATABASE_URL")

    # S3-compatible blob storage (e.g. Cloudflare R2). Only used when
    # store_uploads is True AND a paid user opts in.
    blob_endpoint: str | None = os.getenv("BLOB_ENDPOINT")
    blob_bucket: str | None = os.getenv("BLOB_BUCKET")

    # ---------------------------------------------------- Model identifier
    # Surfaced in API responses so clients can pin behaviour to a version.
    # v0.4.0-stage3a = CLIP head trained on a multi-generator dataset
    # (50k Flux + 20k SDXL + 20k SD 3.5 + 10k AuraFlow + matched authentic).
    # In-distribution test accuracy 98.43%; +5.55 pp vs the Stage 2 head on
    # the SDXL hold-out (a generator never seen in training). See
    # docs/stage3a-implementation.md.
    # Previous: v0.3.0-stage2 (Flux-only, head_v1.pt) — still available in the
    # HF Hub repo for rollback (see head_checkpoint_hf_filename above).
    model_version: str = os.getenv("MODEL_VERSION", "v0.4.0-stage3a")

    # --------------------------------------------------------------- CORS
    # Browser preflight allowlist for the static-HTML frontend. The default
    # covers the production domain, any Cloudflare Pages preview, and
    # localhost (any port) for local dev. Override via env var if a new
    # origin needs access.
    cors_allow_origin_regex: str = os.getenv(
        "CORS_ALLOW_ORIGIN_REGEX",
        r"^(https://(www\.)?veridicate\.com|https://[a-z0-9-]+\.pages\.dev|https://[a-z0-9-]+\.dulipcf\.workers\.dev|http://localhost(:\d+)?|http://127\.0\.0\.1(:\d+)?)$",
    )


settings = Settings()