feat: Unified Compliance-Check — 8 document types in one form
New 3-tab structure: Website-Scan, Compliance-Check, Banner-Check. Compliance-Check Tab (replaces Dokumenten-Pruefung + Impressum-Check): - 8 document rows: DSI, Impressum, Social Media, Cookie, AGB, Nutzungsbedingungen, Widerruf, DSB-Kontakt - Each row: URL input + "Text laden" + file upload + manual text - "Text laden" extracts via consent-tester, shows in editable textarea - User verifies/corrects text before checking - Empty fields = "not present" → own finding Business Profiler (business_profiler.py): - Detects B2B/B2C/B2G from all documents together - Recognizes regulated professions, online shops, editorial content - Context-aware: INFO checks become PASS/FAIL based on profile Backend: /compliance-check + /extract-text endpoints Frontend: ComplianceCheckTab.tsx + DocumentRow.tsx API proxies: compliance-check/route.ts + extract-text/route.ts Also: Impressum regex fixes (Telefon, AG, Geschaeftsfuehrung) and INFO severity for context-dependent checks. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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"""
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Business Profiler — detect business model from document texts.
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Pure keyword-based detection (deterministic, no LLM). Analyzes
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DSE, Impressum, AGB, Widerruf etc. together to build a profile
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that drives context-aware compliance checks.
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Example:
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profile = await detect_business_profile({"dse": "...", "impressum": "..."})
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profile.business_type # "b2c"
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profile.has_online_shop # True
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"""
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from __future__ import annotations
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import re
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from dataclasses import dataclass, field
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@dataclass
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class BusinessProfile:
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business_type: str = "unknown" # b2b, b2c, b2g, nonprofit, unknown
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industry: str = "unknown" # it_services, retail, healthcare, legal, craft, public, unknown
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has_online_shop: bool = False
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has_editorial_content: bool = False
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is_regulated_profession: bool = False
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regulated_profession_type: str = "" # arzt, anwalt, steuerberater, architekt, ""
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needs_odr: bool = False # Online-Streitbeilegung
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detected_services: list[str] = field(default_factory=list)
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confidence: float = 0.0
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# ── Keyword lists ────────────────────────────────────────────────────
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_B2C_KEYWORDS = [
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"verbraucher", "warenkorb", "bestellung", "lieferung", "widerruf",
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"shop", "kaufpreis", "rueckgabe", "rückgabe", "endkunde", "kaeufer",
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"käufer", "privatkunde", "zahlungspflichtig bestellen",
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]
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_B2B_KEYWORDS = [
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"unternehmen", "geschaeftskunden", "geschäftskunden", "gewerblich",
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"auftrag", "auftraggeber", "auftragnehmer", "geschaeftspartner",
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"geschäftspartner", "firmenkunde", "b2b",
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]
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_B2G_KEYWORDS = [
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"behoerde", "behörde", "koerperschaft", "körperschaft", "oeffentlich",
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"öffentlich", "gemeinde", "amt", "stadtverwaltung", "landesbehoerde",
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"landesbehörde", "kommunal",
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]
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_NONPROFIT_KEYWORDS = [
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"gemeinnuetzig", "gemeinnützig", "verein", "stiftung", "e.v.",
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"spende", "ehrenamtlich", "satzung",
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]
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_REGULATED_PROFESSIONS = {
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"rechtsanwalt": "anwalt",
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"anwalt": "anwalt",
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"anwaeltin": "anwalt",
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"anwältin": "anwalt",
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"kanzlei": "anwalt",
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"rechtsanwaltskammer": "anwalt",
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"arzt": "arzt",
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"ärztin": "arzt",
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"aerztin": "arzt",
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"praxis": "arzt",
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"aerztekammer": "arzt",
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"ärztekammer": "arzt",
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"steuerberater": "steuerberater",
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"steuerberaterin": "steuerberater",
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"steuerberaterkammer": "steuerberater",
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"architekt": "architekt",
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"architektin": "architekt",
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"architektenkammer": "architekt",
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"notar": "notar",
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"notariat": "notar",
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"apotheke": "apotheker",
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"apotheker": "apotheker",
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}
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_ONLINE_SHOP_KEYWORDS = [
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"warenkorb", "checkout", "bestellung", "lieferung", "versand",
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"paypal", "kreditkarte", "klarna", "sofortueberweisung",
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"sofortüberweisung", "zahlungsarten", "versandkosten",
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"lieferzeit", "retour", "paketdienst",
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]
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_EDITORIAL_KEYWORDS = [
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"blog", "ratgeber", "news", "redaktion", "artikel", "magazin",
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"beitrag", "kommentar", "podcast", "newsletter", "autor",
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]
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_INDUSTRY_KEYWORDS = {
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"it_services": ["software", "saas", "cloud", "hosting", "server", "api", "app"],
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"retail": ["shop", "warenkorb", "versand", "lieferung", "einzelhandel"],
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"healthcare": ["arzt", "praxis", "patient", "gesundheit", "therapie", "klinik"],
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"legal": ["kanzlei", "rechtsanwalt", "mandant", "anwalt"],
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"craft": ["handwerk", "meister", "werkstatt", "montage", "gewerk"],
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"public": ["behoerde", "behörde", "kommune", "verwaltung", "buerger", "bürger"],
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"finance": ["bank", "versicherung", "finanz", "kredit", "anlage"],
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"education": ["schule", "bildung", "unterricht", "lehrplan", "schueler", "schüler"],
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}
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_TRACKING_SERVICES = {
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"google analytics": "Google Analytics",
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"google tag manager": "Google Tag Manager",
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"matomo": "Matomo",
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"facebook pixel": "Facebook Pixel",
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"meta pixel": "Meta Pixel",
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"hotjar": "Hotjar",
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"hubspot": "HubSpot",
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"mailchimp": "Mailchimp",
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"linkedin insight": "LinkedIn Insight",
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"google ads": "Google Ads",
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"google adsense": "Google AdSense",
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"google maps": "Google Maps",
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"youtube": "YouTube",
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"vimeo": "Vimeo",
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"cloudflare": "Cloudflare",
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"sentry": "Sentry",
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"intercom": "Intercom",
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"zendesk": "Zendesk",
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"stripe": "Stripe",
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"paypal": "PayPal",
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}
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# ── Detection logic ──────────────────────────────────────────────────
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def _count_hits(text: str, keywords: list[str]) -> int:
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return sum(1 for kw in keywords if kw in text)
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async def detect_business_profile(documents: dict[str, str]) -> BusinessProfile:
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"""Analyze all document texts together to detect business model.
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Args:
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documents: dict mapping doc_type -> text (e.g. {"dse": "...", "impressum": "..."})
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"""
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profile = BusinessProfile()
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if not documents:
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return profile
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# Merge all texts for keyword search
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full_text = "\n".join(documents.values()).lower()
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full_text = full_text.replace("\xad", "") # strip soft hyphens
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# ── Tracking services ────────────────────────────────────────
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for pattern, label in _TRACKING_SERVICES.items():
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if pattern in full_text:
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profile.detected_services.append(label)
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# ── Online shop ──────────────────────────────────────────────
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shop_hits = _count_hits(full_text, _ONLINE_SHOP_KEYWORDS)
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profile.has_online_shop = shop_hits >= 3
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# ── Editorial content ────────────────────────────────────────
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editorial_hits = _count_hits(full_text, _EDITORIAL_KEYWORDS)
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profile.has_editorial_content = editorial_hits >= 2
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# ── Regulated profession ─────────────────────────────────────
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for keyword, prof_type in _REGULATED_PROFESSIONS.items():
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if keyword in full_text:
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profile.is_regulated_profession = True
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profile.regulated_profession_type = prof_type
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break
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# ── Business type ────────────────────────────────────────────
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b2c_score = _count_hits(full_text, _B2C_KEYWORDS)
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b2b_score = _count_hits(full_text, _B2B_KEYWORDS)
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b2g_score = _count_hits(full_text, _B2G_KEYWORDS)
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nonprofit_score = _count_hits(full_text, _NONPROFIT_KEYWORDS)
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# Missing documents as signal
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has_agb = "agb" in documents
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has_widerruf = "widerruf" in documents
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if not has_agb:
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b2c_score -= 1 # No AGB → less likely B2C
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if not has_widerruf:
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b2c_score -= 1 # No Widerruf → less likely B2C shop
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if profile.has_online_shop:
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b2c_score += 3 # Strong B2C signal
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scores = {
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"b2c": b2c_score,
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"b2b": b2b_score,
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"b2g": b2g_score,
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"nonprofit": nonprofit_score,
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}
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best = max(scores, key=scores.get) # type: ignore[arg-type]
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best_val = scores[best]
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if best_val >= 2:
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profile.business_type = best
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total = sum(max(0, v) for v in scores.values())
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profile.confidence = round(best_val / total, 2) if total > 0 else 0.5
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else:
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profile.business_type = "unknown"
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profile.confidence = 0.2
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# ── ODR (Online-Streitbeilegung) ─────────────────────────────
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# Required for B2C with online shop (EU Regulation 524/2013)
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profile.needs_odr = (
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profile.business_type == "b2c" and profile.has_online_shop
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)
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# ── Industry ─────────────────────────────────────────────────
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industry_scores: dict[str, int] = {}
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for industry, keywords in _INDUSTRY_KEYWORDS.items():
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hits = _count_hits(full_text, keywords)
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if hits >= 2:
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industry_scores[industry] = hits
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if industry_scores:
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profile.industry = max(industry_scores, key=industry_scores.get) # type: ignore[arg-type]
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elif profile.is_regulated_profession:
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prof_map = {"anwalt": "legal", "arzt": "healthcare",
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"steuerberater": "finance", "architekt": "craft"}
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profile.industry = prof_map.get(profile.regulated_profession_type, "unknown")
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return profile
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