feat(audit): Text-Paste-Mode pro Row — Crawler optional umgehen

Hintergrund: VW liefert ueber URL-Crawler nur 6 Vendors statt der 100+
die in der echten Cookie-Tabelle stehen. Wenn der User die Tabelle aber
direkt von der Site kopieren kann (was bei den meisten OEM-Sites moeglich
ist), umgehen wir den Crawler komplett und parsen den Text deterministisch.

Backend:
* doc_type_classifier.py — 7 Pattern-Gruppen (§5 TMG, Art.13 DSGVO,
  AGB-Klauseln, Widerrufs-Frist, Cookie-Tabellen-Header, etc). Wenn der
  User Text ins falsche Doc-Type-Feld kopiert (Impressum->DSE),
  detect_mismatch liefert detected + action ('reclassify' bei sehr hoher
  Konfidenz, 'warn' bei medium).
* cookies_table_parser.py — Tab/Pipe/Komma/Semicolon-Separator-Auto-
  Detection, Spalten-Mapping per Header-Keyword. Aggregiert Cookie-
  Eintraege zu Vendor-Records (mit _guess_vendor-Fallback). Voll
  deterministisch, kein LLM.
* doc_input_warnings.py — Mail-Block ueber dem Audit, der Mismatches +
  Auto-Reclassifies dem User transparent macht.
* Pipeline: text gewinnt ueber url (war schon im Schema vermerkt), neue
  Felder declared_doc_type / input_source / reclassify_hint in doc_entries.
  Pasted-Tabellen-Vendors haben Vorrang vor Library-Fallback + LLM-Cascade
  (sind 100% genau).

Frontend (DocCheckTab):
* Pro Row Mode-Toggle 'URL' / 'Text einfuegen' (lila wenn aktiv).
* Textarea (h-32, monospace) im text-mode mit kontext-spezifischem
  Placeholder (Cookie-Hinweis ggue. anderen Doc-Types) und Live-
  Zeichen-/Wort-Counter.
* Submit-Button accepted entries mit URL ODER text.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

backend-compliance/compliance/services/cookies_table_parser.py

@@ -0,0 +1,217 @@
+"""
+Parst Cookie-Tabellen die der User direkt ins Frontend kopiert.
+
+Typische Quellen:
+* Browser-Copy aus VW/BMW/Mercedes Cookie-Richtlinie (Tab-getrennt)
+* Excel-Export aus Borlabs / OneTrust / Cookiebot Admin (CSV / Pipe)
+* Markdown-Tabelle aus interner Doku
+
+Erkennt 4 Spalten-Layouts (heuristisch):
+1. [Name, Kategorie, Beschreibung, Speicherdauer, Provider]
+2. [Name, Provider, Zweck, Speicherdauer]
+3. [Name, Beschreibung, Speicherdauer]
+4. nur [Name, Speicherdauer]
+
+Output: gleiche Vendor-Record-Struktur wie vendor_extractor / LLM —
+damit der Rest der Pipeline (VVT-Tabelle, Library-Mismatch-Check) ohne
+Aenderung weiterlaeuft.
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+
+logger = logging.getLogger(__name__)
+
+
+_CATEGORY_LABELS = (
+    "notwendig", "essential", "funktional", "tracking", "marketing",
+    "statistik", "analyse", "analytics", "performance", "werbung",
+    "advertising", "targeting", "preferences", "social_media",
+    "strictly necessary", "personalisierung",
+)
+
+
+def _looks_like_separator(line: str) -> str | None:
+    """Detect the column-separator of a tabular line."""
+    if "\t" in line and line.count("\t") >= 2:
+        return "\t"
+    if " | " in line and line.count(" | ") >= 2:
+        return " | "
+    if ";" in line and line.count(";") >= 2 and "," not in line[:20]:
+        return ";"
+    if "," in line and line.count(",") >= 3:
+        return ","
+    return None
+
+
+def _normalize_category(s: str) -> str:
+    sl = s.lower().strip()
+    for cat in _CATEGORY_LABELS:
+        if cat in sl:
+            if cat in ("notwendig", "essential", "strictly necessary"):
+                return "essential"
+            if cat in ("tracking", "marketing", "werbung",
+                       "advertising", "targeting"):
+                return "marketing"
+            if cat in ("statistik", "analyse", "analytics", "performance"):
+                return "statistics"
+            if cat == "funktional":
+                return "functional"
+            if cat == "social_media":
+                return "social_media"
+    return sl[:30]
+
+
+def _parse_persistence(s: str) -> str:
+    """Extracts 'Speicherdauer' notation."""
+    m = re.search(
+        r"(\d+\s*(sekunde|minute|stunde|tag|woche|monat|jahr|day|month|year)[^\s,;|]{0,5})",
+        s, re.I,
+    )
+    if m:
+        return m.group(1).strip()[:80]
+    if re.search(r"\bsession\b", s, re.I):
+        return "Session"
+    if re.search(r"permanent", s, re.I):
+        return "Permanent"
+    return ""
+
+
+def parse_cookie_table(text: str) -> list[dict]:
+    """Returns vendor-records aus einer copy-pasted Cookie-Tabelle.
+
+    Bei nicht-tabellarischem Text: return [].
+    """
+    if not text or len(text) < 100:
+        return []
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    if not lines:
+        return []
+
+    # Sample 30 lines to detect separator
+    sample = lines[:60]
+    sep_counts: dict[str, int] = {}
+    for ln in sample:
+        sep = _looks_like_separator(ln)
+        if sep:
+            sep_counts[sep] = sep_counts.get(sep, 0) + 1
+    if not sep_counts or max(sep_counts.values()) < 3:
+        return []
+
+    sep = max(sep_counts, key=sep_counts.get)
+    logger.info("cookies_table_parser: detected separator '%s' (%d hits)",
+                sep, sep_counts[sep])
+
+    # Parse rows
+    rows: list[list[str]] = []
+    for ln in lines:
+        if sep in ln:
+            parts = [p.strip().strip('"') for p in ln.split(sep)]
+            if len(parts) >= 2 and parts[0]:
+                rows.append(parts)
+
+    if len(rows) < 3:
+        return []
+
+    # Detect column layout from header (first row) or by content
+    header_row = [c.lower() for c in rows[0]]
+    has_header = any(h in " ".join(header_row) for h in
+                     ("cookie", "name", "anbieter", "provider", "zweck",
+                      "kategorie", "speicherdauer", "dauer"))
+    data_rows = rows[1:] if has_header else rows
+
+    # Map columns by header keyword or by position
+    col_idx = {"name": 0, "provider": -1, "category": -1,
+               "purpose": -1, "persistence": -1}
+    if has_header:
+        for i, h in enumerate(header_row):
+            if "name" in h or "cookie" in h:
+                col_idx["name"] = i
+            elif "anbieter" in h or "provider" in h or "domain" in h:
+                col_idx["provider"] = i
+            elif "kategorie" in h or "type" in h or "art" in h:
+                col_idx["category"] = i
+            elif "zweck" in h or "purpose" in h or "beschreib" in h:
+                col_idx["purpose"] = i
+            elif "speicher" in h or "dauer" in h or "lebens" in h or "expir" in h:
+                col_idx["persistence"] = i
+
+    # Aggregate by vendor (or by name if no vendor column)
+    by_vendor: dict[str, dict] = {}
+    for r in data_rows:
+        if len(r) < 2:
+            continue
+        name = r[col_idx["name"]] if col_idx["name"] < len(r) else r[0]
+        name = (name or "").strip()
+        if not name or len(name) > 120 or len(name) < 2:
+            continue
+        provider = ""
+        if col_idx["provider"] >= 0 and col_idx["provider"] < len(r):
+            provider = r[col_idx["provider"]].strip()
+        if not provider:
+            # Heuristik: wenn Spalte 'Anbieter' fehlt, raten aus Cookie-Name
+            provider = _guess_vendor(name)
+        if not provider:
+            provider = "Unbekannter Anbieter"
+
+        category = ""
+        purpose = ""
+        persistence = ""
+        if col_idx["category"] >= 0 and col_idx["category"] < len(r):
+            category = _normalize_category(r[col_idx["category"]])
+        if col_idx["purpose"] >= 0 and col_idx["purpose"] < len(r):
+            purpose = r[col_idx["purpose"]][:500]
+        if col_idx["persistence"] >= 0 and col_idx["persistence"] < len(r):
+            persistence = _parse_persistence(r[col_idx["persistence"]])
+        if not category:
+            # Inferieren aus purpose-Text
+            category = _normalize_category(purpose)
+
+        entry = by_vendor.setdefault(provider, {
+            "name": provider, "country": "",
+            "purpose": purpose[:300] if purpose else "",
+            "category": category,
+            "opt_out_url": "", "privacy_policy_url": "",
+            "persistence": persistence,
+            "cookies": [],
+            "source": "table_paste",
+        })
+        entry["cookies"].append({
+            "name": name, "purpose": purpose[:200],
+            "expiry": persistence, "is_third_party": True,
+        })
+
+    out = list(by_vendor.values())
+    logger.info("cookies_table_parser: %d vendors / %d cookies parsed",
+                len(out), sum(len(v["cookies"]) for v in out))
+    return out
+
+
+_VENDOR_GUESS = (
+    ("_ga", "Google"), ("_gid", "Google"), ("_gcl_", "Google"),
+    ("ANID", "Google"), ("AID", "Google"), ("FPGCLDC", "Google"),
+    ("IDE", "Google DoubleClick"), ("DSID", "Google"),
+    ("_fbp", "Meta / Facebook"), ("fr", "Meta / Facebook"),
+    ("_pin_unauth", "Pinterest"), ("_uetsid", "Microsoft Bing"),
+    ("_uetvid", "Microsoft Bing"), ("MUID", "Microsoft"),
+    ("tt_", "TikTok"), ("li_at", "LinkedIn"),
+    ("OptanonConsent", "OneTrust"), ("cookieconsent", "Borlabs / Cookie-CMP"),
+    ("eta_", "etracker"), ("matomo", "Matomo"),
+    ("_hjid", "Hotjar"), ("_hj", "Hotjar"),
+    ("__cf", "Cloudflare"), ("datadome", "DataDome"),
+    ("incap_", "Imperva Incapsula"),
+    ("ajs_", "Segment"), ("amp_", "Amplitude"),
+    ("sat_track", "Adobe Experience Cloud"),
+    ("AMCV_", "Adobe Experience Cloud"),
+    ("s_cc", "Adobe Analytics"), ("s_sq", "Adobe Analytics"),
+)
+
+
+def _guess_vendor(cookie_name: str) -> str:
+    nl = cookie_name.lower()
+    for prefix, vendor in _VENDOR_GUESS:
+        if nl.startswith(prefix.lower()) or prefix.lower() in nl:
+            return vendor
+    return ""