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Neues Modul das den regulatorischen Spielraum fuer KI-Use-Cases deterministisch berechnet und optimale Konfigurationen vorschlaegt. Kernfeatures: - 13-Dimensionen Constraint-Space (DSGVO + AI Act) - 3-Zonen-Analyse: Verboten / Eingeschraenkt / Erlaubt - Deterministische Optimizer-Engine (kein LLM im Kern) - 28 Constraint-Regeln aus DSGVO, AI Act, EDPB Guidelines - 28 Tests (Golden Suite + Meta-Tests) - REST API: /sdk/v1/maximizer/* (9 Endpoints) - Frontend: 3-Zonen-Visualisierung, Dimension-Form, Score-Gauges [migration-approved] Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
164 lines
7.7 KiB
TypeScript
164 lines
7.7 KiB
TypeScript
'use client'
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import React, { useState } from 'react'
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import { useRouter } from 'next/navigation'
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import { ZoneBadge } from '@/components/sdk/compliance-optimizer/ZoneBadge'
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interface DimensionField {
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key: string
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label: string
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options: { value: string; label: string }[]
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type?: 'select' | 'toggle'
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}
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const DIMENSIONS: DimensionField[] = [
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{ key: 'automation_level', label: 'Automatisierungsgrad', options: [
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{ value: 'none', label: 'Keine' }, { value: 'assistive', label: 'Assistierend' },
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{ value: 'partial', label: 'Teilautomatisiert' }, { value: 'full', label: 'Vollautomatisiert' },
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]},
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{ key: 'decision_binding', label: 'Entscheidungsbindung', options: [
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{ value: 'non_binding', label: 'Unverbindlich' }, { value: 'human_review_required', label: 'Mensch entscheidet' },
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{ value: 'fully_binding', label: 'Vollstaendig bindend' },
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]},
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{ key: 'decision_impact', label: 'Entscheidungswirkung', options: [
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{ value: 'low', label: 'Niedrig' }, { value: 'medium', label: 'Mittel' }, { value: 'high', label: 'Hoch' },
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]},
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{ key: 'domain', label: 'Branche', options: [
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{ value: 'hr', label: 'HR / Personal' }, { value: 'finance', label: 'Finanzen' },
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{ value: 'education', label: 'Bildung' }, { value: 'health', label: 'Gesundheit' },
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{ value: 'marketing', label: 'Marketing' }, { value: 'general', label: 'Allgemein' },
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]},
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{ key: 'data_type', label: 'Datensensitivitaet', options: [
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{ value: 'non_personal', label: 'Keine personenbezogenen' }, { value: 'personal', label: 'Personenbezogen' },
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{ value: 'sensitive', label: 'Besondere Kategorien (Art. 9)' }, { value: 'biometric', label: 'Biometrisch' },
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]},
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{ key: 'human_in_loop', label: 'Menschliche Kontrolle', options: [
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{ value: 'required', label: 'Erforderlich' }, { value: 'optional', label: 'Optional' }, { value: 'none', label: 'Keine' },
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]},
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{ key: 'explainability', label: 'Erklaerbarkeit', options: [
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{ value: 'high', label: 'Hoch' }, { value: 'basic', label: 'Basis' }, { value: 'none', label: 'Keine' },
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]},
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{ key: 'risk_classification', label: 'Risikoklasse (AI Act)', options: [
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{ value: 'minimal', label: 'Minimal' }, { value: 'limited', label: 'Begrenzt' },
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{ value: 'high', label: 'Hoch' }, { value: 'prohibited', label: 'Verboten' },
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]},
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{ key: 'legal_basis', label: 'Rechtsgrundlage (DSGVO)', options: [
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{ value: 'consent', label: 'Einwilligung' }, { value: 'contract', label: 'Vertrag' },
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{ value: 'legal_obligation', label: 'Rechtl. Verpflichtung' },
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{ value: 'legitimate_interest', label: 'Berechtigtes Interesse' },
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{ value: 'public_interest', label: 'Oeffentl. Interesse' },
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]},
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{ key: 'model_type', label: 'Modelltyp', options: [
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{ value: 'rule_based', label: 'Regelbasiert' }, { value: 'statistical', label: 'Statistisch / ML' },
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{ value: 'blackbox_llm', label: 'Blackbox / LLM' },
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]},
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{ key: 'deployment_scope', label: 'Einsatzbereich', options: [
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{ value: 'internal', label: 'Intern' }, { value: 'external', label: 'Extern (Kunden)' },
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{ value: 'public', label: 'Oeffentlich' },
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]},
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]
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const TOGGLE_DIMENSIONS = [
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{ key: 'transparency_required', label: 'Transparenzpflicht' },
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{ key: 'logging_required', label: 'Protokollierungspflicht' },
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]
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export default function NewOptimizationPage() {
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const router = useRouter()
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const [title, setTitle] = useState('')
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const [config, setConfig] = useState<Record<string, string>>({
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automation_level: 'assistive', decision_binding: 'non_binding', decision_impact: 'low',
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domain: 'general', data_type: 'non_personal', human_in_loop: 'required',
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explainability: 'basic', risk_classification: 'minimal', legal_basis: 'contract',
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transparency_required: 'false', logging_required: 'false',
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model_type: 'rule_based', deployment_scope: 'internal',
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})
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const [preview, setPreview] = useState<Record<string, { zone: string }> | null>(null)
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const [submitting, setSubmitting] = useState(false)
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async function handlePreview() {
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try {
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const body = { ...config, transparency_required: config.transparency_required === 'true', logging_required: config.logging_required === 'true' }
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const res = await fetch('/api/sdk/v1/maximizer/evaluate', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(body) })
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if (res.ok) {
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const data = await res.json()
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setPreview(data.zone_map || {})
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}
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} catch { /* silent */ }
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}
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async function handleSubmit() {
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setSubmitting(true)
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try {
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const body = {
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config: { ...config, transparency_required: config.transparency_required === 'true', logging_required: config.logging_required === 'true' },
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title: title || 'Optimierung ' + new Date().toLocaleDateString('de-DE'),
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}
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const res = await fetch('/api/sdk/v1/maximizer/optimize', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(body) })
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if (res.ok) {
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const data = await res.json()
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router.push(`/sdk/compliance-optimizer/${data.id}`)
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}
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} finally {
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setSubmitting(false)
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}
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}
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return (
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<div className="max-w-4xl mx-auto p-6">
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<h1 className="text-2xl font-bold text-gray-900 mb-1">Neue Optimierung</h1>
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<p className="text-sm text-gray-500 mb-6">Konfigurieren Sie Ihren KI-Use-Case und finden Sie den maximalen regulatorischen Spielraum.</p>
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<div className="mb-4">
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<label className="block text-sm font-medium text-gray-700 mb-1">Titel</label>
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<input type="text" value={title} onChange={(e) => setTitle(e.target.value)} placeholder="z.B. HR Bewerber-Ranking"
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className="w-full border border-gray-300 rounded-lg px-3 py-2 text-sm" />
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</div>
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<div className="grid grid-cols-1 md:grid-cols-2 gap-4 mb-6">
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{DIMENSIONS.map((dim) => (
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<div key={dim.key}>
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<label className="block text-sm font-medium text-gray-700 mb-1">
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{dim.label}
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{preview && preview[dim.key] && (
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<span className="ml-2"><ZoneBadge zone={preview[dim.key].zone as 'FORBIDDEN' | 'RESTRICTED' | 'SAFE'} /></span>
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)}
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</label>
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<select
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value={config[dim.key]}
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onChange={(e) => { setConfig({ ...config, [dim.key]: e.target.value }); setPreview(null) }}
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className="w-full border border-gray-300 rounded-lg px-3 py-2 text-sm bg-white"
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>
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{dim.options.map((o) => <option key={o.value} value={o.value}>{o.label}</option>)}
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</select>
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</div>
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))}
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{TOGGLE_DIMENSIONS.map((dim) => (
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<div key={dim.key} className="flex items-center gap-3">
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<input type="checkbox" checked={config[dim.key] === 'true'}
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onChange={(e) => { setConfig({ ...config, [dim.key]: String(e.target.checked) }); setPreview(null) }}
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className="h-4 w-4 rounded border-gray-300 text-blue-600" />
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<label className="text-sm font-medium text-gray-700">
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{dim.label}
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{preview && preview[dim.key] && (
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<span className="ml-2"><ZoneBadge zone={preview[dim.key].zone as 'FORBIDDEN' | 'RESTRICTED' | 'SAFE'} /></span>
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)}
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</label>
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</div>
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))}
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</div>
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<div className="flex gap-3">
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<button onClick={handlePreview} className="border border-gray-300 text-gray-700 px-4 py-2 rounded-lg hover:bg-gray-50 text-sm">
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Vorschau (3-Zonen-Check)
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</button>
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<button onClick={handleSubmit} disabled={submitting}
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className="bg-blue-600 text-white px-6 py-2 rounded-lg hover:bg-blue-700 text-sm font-medium disabled:opacity-50">
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{submitting ? 'Optimiere...' : 'Optimieren'}
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</button>
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</div>
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</div>
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)
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}
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