breakpilot-compliance/consent-tester/services/multi_browser_scanner.py

"""Multi-browser consent-scan orchestrator (browser-matrix stage 1).

Runs the existing single-browser `consent_scanner.run_consent_test`
once per profile from `browser_profiles.resolve_profiles` and
aggregates the per-browser results with the worst-of rule:

  * any HIGH-violation on any browser → robustness_score capped to <60
  * Pre-Consent + Reject-Respekt are weighted 80% combined
  * Banner-Design only contributes if the banner was detected at all

Returns a unified ScanResponse-compatible dict plus a fresh
`browser_matrix` block (one entry per profile) so the backend mail
renderer can show "Chrome 95% · Firefox 92% · WebKit 78% · Mobile-Safari 65%".

Heuristic only — the real per-test scoring (T1..T7 from the EDPB
taskforce report) is mocked here as a placeholder until the consent
scanner emits structured per-test results.
"""

from __future__ import annotations

import asyncio
import logging
from typing import Any, Callable, Awaitable

from .browser_profiles import resolve_profiles

logger = logging.getLogger(__name__)

# Worst-of capping: if pre-consent or reject-respect has ANY hard fail,
# overall robustness can never exceed this value.
_HARD_FAIL_CAP = 55

# Per-dimension weights — Sales/Risk-tuned (see strategy doc):
#   Pre-Consent-Compliance   50%
#   Reject-Respekt           30%
#   Banner-Design / Dark     20%
_WEIGHTS = {"pre_consent": 0.5, "reject_respect": 0.3, "banner_design": 0.2}


def _extract_dimensions(banner_result: dict) -> dict[str, float]:
    """Best-effort: derive 3 sub-scores from the existing scan output.

    Falls back to neutral 0.5 when the input is too sparse.
    """
    if not banner_result:
        return {"pre_consent": 0.5, "reject_respect": 0.5,
                "banner_design": 0.5}
    phases = banner_result.get("phases") or {}
    before = phases.get("before_consent") or phases.get("before") or {}
    after_reject = phases.get("after_reject") or {}
    bv = (banner_result.get("banner_checks") or {}).get("violations") or []
    pre_cookies = len(before.get("cookies") or [])
    rej_cookies = len(after_reject.get("cookies") or [])
    pre_consent = max(0.0, 1.0 - min(1.0, pre_cookies / 10.0))
    reject_respect = max(0.0, 1.0 - min(1.0, rej_cookies / 5.0))
    banner_design = max(0.0, 1.0 - min(1.0, len(bv) / 5.0))
    return {
        "pre_consent": round(pre_consent, 3),
        "reject_respect": round(reject_respect, 3),
        "banner_design": round(banner_design, 3),
    }


def _score(dimensions: dict[str, float]) -> int:
    base = (
        dimensions["pre_consent"] * _WEIGHTS["pre_consent"]
        + dimensions["reject_respect"] * _WEIGHTS["reject_respect"]
        + dimensions["banner_design"] * _WEIGHTS["banner_design"]
    )
    pct = int(round(base * 100))
    if (dimensions["pre_consent"] < 0.5
            or dimensions["reject_respect"] < 0.5):
        pct = min(pct, _HARD_FAIL_CAP)
    return pct


def _verbal(score: int) -> str:
    if score >= 95:
        return "Im Prüfumfang keine wesentlichen Mängel"
    if score >= 80:
        return "Niedriges Risiko, Korrektur empfohlen"
    if score >= 60:
        return "Mittlere Mängel, kurzfristige Korrektur"
    if score >= 30:
        return "Schwere Mängel, sofortige Korrektur"
    return "Bußgeldrelevante Verstöße"


async def run_matrix(
    scanner: Callable[..., Awaitable[Any]],
    url: str,
    requested_profiles: list[str] | None = None,
    **scanner_kwargs: Any,
) -> dict:
    """Run `scanner(url, profile=…, **kw)` once per profile in parallel.

    `scanner` must be the existing consent_scanner.run_consent_test
    or a shim with the same signature; it must accept a `browser_profile`
    kwarg. Returns:

      {
        "browser_matrix": [
          {"profile_id": ..., "label": ..., "scan": <raw scan dict>,
           "dimensions": {...}, "score": int, "verbal": str},
          ...
        ],
        "aggregate": {
          "worst_score": int, "worst_profile": "...",
          "best_score": int, "best_profile": "...",
          "verbal": "...",
        },
      }
    """
    profiles = resolve_profiles(requested_profiles)
    if not profiles:
        return {"browser_matrix": [], "aggregate": {}}

    async def _run_one(prof: dict) -> dict:
        try:
            scan = await scanner(
                url, browser_profile=prof, **scanner_kwargs,
            )
        except TypeError:
            # Backward-compat: scanner that doesn't accept the kwarg
            scan = await scanner(url, **scanner_kwargs)
        except Exception as e:
            logger.warning("matrix profile %s failed: %s", prof["id"], e)
            return {
                "profile_id": prof["id"], "label": prof["label"],
                "engine": prof.get("engine"),
                "is_mobile": bool(prof.get("device")),
                "summary": None, "error": str(e)[:200],
                "dimensions": {"pre_consent": 0, "reject_respect": 0,
                                "banner_design": 0},
                "score": 0, "verbal": "Scan fehlgeschlagen",
            }
        dims = _extract_dimensions(scan or {})
        score = _score(dims)
        # Nur den kompakten `summary` an die Zeile heben — die vollen
        # phases/Cookie-Listen werden für das Scoring konsumiert und dann
        # verworfen (sonst bläht 6× volle Cookie-Liste die JSONB-Persistenz).
        summary = (scan or {}).get("summary") if isinstance(scan, dict) else None
        return {
            "profile_id": prof["id"], "label": prof["label"],
            "engine": prof.get("engine"),
            "is_mobile": bool(prof.get("device")),
            "summary": summary, "dimensions": dims, "score": score,
            "verbal": _verbal(score),
        }

    results = await asyncio.gather(*[_run_one(p) for p in profiles])
    sorted_by_score = sorted(results, key=lambda r: r["score"])
    worst = sorted_by_score[0]
    best = sorted_by_score[-1]
    return {
        "browser_matrix": results,
        "aggregate": {
            "worst_score": worst["score"],
            "worst_profile": worst["profile_id"],
            "best_score": best["score"],
            "best_profile": best["profile_id"],
            "verbal": worst["verbal"],
            "profiles_run": len(results),
        },
    }