21a844cb8a
A previous `git pull --rebase origin main` dropped 177 local commits,
losing 3400+ files across admin-v2, backend, studio-v2, website,
klausur-service, and many other services. The partial restore attempt
(660295e2) only recovered some files.
This commit restores all missing files from pre-rebase ref 98933f5e
while preserving post-rebase additions (night-scheduler, night-mode UI,
NightModeWidget dashboard integration).
Restored features include:
- AI Module Sidebar (FAB), OCR Labeling, OCR Compare
- GPU Dashboard, RAG Pipeline, Magic Help
- Klausur-Korrektur (8 files), Abitur-Archiv (5+ files)
- Companion, Zeugnisse-Crawler, Screen Flow
- Full backend, studio-v2, website, klausur-service
- All compliance SDKs, agent-core, voice-service
- CI/CD configs, documentation, scripts
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
1018 lines
49 KiB
TypeScript
1018 lines
49 KiB
TypeScript
'use client'
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/**
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* Magic Help Admin Page
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*
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* Comprehensive admin interface for TrOCR Handwriting Recognition and Exam Correction.
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* Features:
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* - Model status monitoring
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* - OCR testing with image upload
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* - Training data management
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* - Fine-tuning controls
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* - Architecture documentation
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* - Configuration settings
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*/
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import { useState, useEffect, useCallback } from 'react'
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import AdminLayout from '@/components/admin/AdminLayout'
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type TabId = 'overview' | 'test' | 'training' | 'architecture' | 'settings'
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interface TrOCRStatus {
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status: 'available' | 'not_installed' | 'error'
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model_name?: string
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model_id?: string
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device?: string
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is_loaded?: boolean
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has_lora_adapter?: boolean
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training_examples_count?: number
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error?: string
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install_command?: string
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}
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interface OCRResult {
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text: string
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confidence: number
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processing_time_ms: number
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model: string
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has_lora_adapter: boolean
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}
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interface TrainingExample {
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image_path: string
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ground_truth: string
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teacher_id: string
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created_at: string
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}
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interface MagicSettings {
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autoDetectLines: boolean
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confidenceThreshold: number
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maxImageSize: number
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loraRank: number
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loraAlpha: number
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learningRate: number
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epochs: number
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batchSize: number
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enableCache: boolean
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cacheMaxAge: number
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}
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const DEFAULT_SETTINGS: MagicSettings = {
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autoDetectLines: true,
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confidenceThreshold: 0.7,
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maxImageSize: 4096,
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loraRank: 8,
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loraAlpha: 32,
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learningRate: 0.00005,
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epochs: 3,
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batchSize: 4,
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enableCache: true,
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cacheMaxAge: 3600,
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}
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export default function MagicHelpPage() {
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const [activeTab, setActiveTab] = useState<TabId>('overview')
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const [status, setStatus] = useState<TrOCRStatus | null>(null)
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const [loading, setLoading] = useState(true)
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const [ocrResult, setOcrResult] = useState<OCRResult | null>(null)
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const [ocrLoading, setOcrLoading] = useState(false)
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const [examples, setExamples] = useState<TrainingExample[]>([])
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const [trainingImage, setTrainingImage] = useState<File | null>(null)
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const [trainingText, setTrainingText] = useState('')
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const [fineTuning, setFineTuning] = useState(false)
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const [settings, setSettings] = useState<MagicSettings>(DEFAULT_SETTINGS)
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const [settingsSaved, setSettingsSaved] = useState(false)
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const fetchStatus = useCallback(async () => {
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try {
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const res = await fetch('/api/klausur/trocr/status')
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const data = await res.json()
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setStatus(data)
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} catch {
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setStatus({ status: 'error', error: 'Failed to fetch status' })
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} finally {
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setLoading(false)
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}
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}, [])
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const fetchExamples = useCallback(async () => {
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try {
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const res = await fetch('/api/klausur/trocr/training/examples')
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const data = await res.json()
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setExamples(data.examples || [])
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} catch (error) {
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console.error('Failed to fetch examples:', error)
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}
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}, [])
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useEffect(() => {
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fetchStatus()
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fetchExamples()
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// Load settings from localStorage
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const saved = localStorage.getItem('magic-help-settings')
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if (saved) {
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try {
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setSettings(JSON.parse(saved))
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} catch {
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// ignore parse errors
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}
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}
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}, [fetchStatus, fetchExamples])
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const handleFileUpload = async (file: File) => {
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setOcrLoading(true)
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setOcrResult(null)
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const formData = new FormData()
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formData.append('file', file)
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try {
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const res = await fetch(`/api/klausur/trocr/extract?detect_lines=${settings.autoDetectLines}`, {
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method: 'POST',
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body: formData,
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})
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const data = await res.json()
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if (data.text !== undefined) {
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setOcrResult(data)
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} else {
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setOcrResult({ text: `Error: ${data.detail || 'Unknown error'}`, confidence: 0, processing_time_ms: 0, model: '', has_lora_adapter: false })
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}
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} catch (error) {
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setOcrResult({ text: `Error: ${error}`, confidence: 0, processing_time_ms: 0, model: '', has_lora_adapter: false })
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} finally {
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setOcrLoading(false)
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}
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}
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const handleAddTrainingExample = async () => {
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if (!trainingImage || !trainingText.trim()) {
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alert('Please provide both an image and the correct text')
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return
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}
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const formData = new FormData()
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formData.append('file', trainingImage)
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try {
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const res = await fetch(`/api/klausur/trocr/training/add?ground_truth=${encodeURIComponent(trainingText)}`, {
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method: 'POST',
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body: formData,
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})
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const data = await res.json()
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if (data.example_id) {
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alert(`Training example added! Total: ${data.total_examples}`)
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setTrainingImage(null)
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setTrainingText('')
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fetchStatus()
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fetchExamples()
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} else {
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alert(`Error: ${data.detail || 'Unknown error'}`)
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}
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} catch (error) {
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alert(`Error: ${error}`)
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}
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}
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const handleFineTune = async () => {
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if (!confirm('Start fine-tuning? This may take several minutes.')) return
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setFineTuning(true)
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try {
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const res = await fetch('/api/klausur/trocr/training/fine-tune', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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epochs: settings.epochs,
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learning_rate: settings.learningRate,
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lora_rank: settings.loraRank,
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lora_alpha: settings.loraAlpha,
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}),
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})
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const data = await res.json()
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if (data.status === 'success') {
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alert(`Fine-tuning successful!\nExamples used: ${data.examples_used}\nEpochs: ${data.epochs}`)
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fetchStatus()
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} else {
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alert(`Fine-tuning failed: ${data.message}`)
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}
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} catch (error) {
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alert(`Error: ${error}`)
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} finally {
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setFineTuning(false)
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}
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}
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const saveSettings = () => {
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localStorage.setItem('magic-help-settings', JSON.stringify(settings))
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setSettingsSaved(true)
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setTimeout(() => setSettingsSaved(false), 2000)
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}
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const getStatusBadge = () => {
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if (!status) return null
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switch (status.status) {
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case 'available':
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return <span className="px-2 py-1 text-xs font-medium rounded-full bg-green-500/20 text-green-400">Available</span>
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case 'not_installed':
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return <span className="px-2 py-1 text-xs font-medium rounded-full bg-red-500/20 text-red-400">Not Installed</span>
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case 'error':
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return <span className="px-2 py-1 text-xs font-medium rounded-full bg-yellow-500/20 text-yellow-400">Error</span>
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}
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}
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const tabs = [
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{ id: 'overview' as TabId, label: 'Übersicht', icon: '📊' },
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{ id: 'test' as TabId, label: 'OCR Test', icon: '🔍' },
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{ id: 'training' as TabId, label: 'Training', icon: '🎯' },
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{ id: 'architecture' as TabId, label: 'Architektur', icon: '🏗️' },
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{ id: 'settings' as TabId, label: 'Einstellungen', icon: '⚙️' },
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]
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return (
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<AdminLayout>
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<div className="space-y-6">
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{/* Header */}
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<div className="flex items-center justify-between">
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<div>
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<h1 className="text-2xl font-bold text-white flex items-center gap-2">
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<span className="text-2xl">✨</span>
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Magic Help - Handschrifterkennung
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</h1>
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<p className="text-gray-400 mt-1">
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KI-gestützte Klausurkorrektur mit TrOCR und Privacy-by-Design
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</p>
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</div>
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{getStatusBadge()}
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</div>
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{/* Tabs */}
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<div className="flex gap-2 border-b border-gray-700 pb-2">
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{tabs.map((tab) => (
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<button
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key={tab.id}
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onClick={() => setActiveTab(tab.id)}
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className={`px-4 py-2 rounded-t-lg text-sm font-medium transition-colors ${
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activeTab === tab.id
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? 'bg-gray-800 text-white border-b-2 border-blue-500'
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: 'text-gray-400 hover:text-white hover:bg-gray-800/50'
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}`}
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>
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<span className="mr-2">{tab.icon}</span>
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{tab.label}
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</button>
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))}
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</div>
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{/* Tab Content */}
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{activeTab === 'overview' && (
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<div className="space-y-6">
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{/* Status Card */}
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<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
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<div className="flex items-center justify-between mb-4">
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<h2 className="text-lg font-semibold text-white">Systemstatus</h2>
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<button
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onClick={fetchStatus}
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className="px-3 py-1 bg-blue-600 hover:bg-blue-700 rounded text-sm transition-colors"
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>
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Aktualisieren
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</button>
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</div>
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{loading ? (
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<div className="text-gray-400">Lade Status...</div>
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) : status?.status === 'available' ? (
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<div className="grid grid-cols-2 md:grid-cols-4 gap-4">
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<div className="bg-gray-900/50 rounded-lg p-4">
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<div className="text-2xl font-bold text-white">{status.model_name || 'trocr-base'}</div>
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<div className="text-xs text-gray-400">Modell</div>
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</div>
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<div className="bg-gray-900/50 rounded-lg p-4">
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<div className="text-2xl font-bold text-white">{status.device || 'CPU'}</div>
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<div className="text-xs text-gray-400">Gerät</div>
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</div>
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<div className="bg-gray-900/50 rounded-lg p-4">
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<div className="text-2xl font-bold text-white">{status.training_examples_count || 0}</div>
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<div className="text-xs text-gray-400">Trainingsbeispiele</div>
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</div>
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<div className="bg-gray-900/50 rounded-lg p-4">
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<div className="text-2xl font-bold text-white">{status.has_lora_adapter ? 'Aktiv' : 'Keiner'}</div>
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<div className="text-xs text-gray-400">LoRA Adapter</div>
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</div>
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</div>
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) : status?.status === 'not_installed' ? (
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<div className="text-gray-400">
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<p className="mb-2">TrOCR ist nicht installiert. Führe aus:</p>
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<code className="bg-gray-900 px-3 py-2 rounded text-sm block">{status.install_command}</code>
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</div>
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) : (
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<div className="text-red-400">{status?.error || 'Unbekannter Fehler'}</div>
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)}
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</div>
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{/* Quick Overview Cards */}
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<div className="grid grid-cols-1 md:grid-cols-3 gap-6">
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<div className="bg-gradient-to-br from-purple-900/30 to-purple-800/20 border border-purple-700/50 rounded-xl p-6">
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<div className="text-3xl mb-2">🎯</div>
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<h3 className="text-lg font-semibold text-white mb-2">Handschrifterkennung</h3>
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<p className="text-sm text-gray-300">
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TrOCR erkennt automatisch handgeschriebenen Text in Klausuren.
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Das Modell wurde speziell für deutsche Handschriften optimiert.
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</p>
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</div>
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<div className="bg-gradient-to-br from-green-900/30 to-green-800/20 border border-green-700/50 rounded-xl p-6">
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<div className="text-3xl mb-2">🔒</div>
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<h3 className="text-lg font-semibold text-white mb-2">Privacy by Design</h3>
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<p className="text-sm text-gray-300">
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Alle Daten werden lokal verarbeitet. Schülernamen werden durch
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QR-Codes pseudonymisiert - DSGVO-konform.
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</p>
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</div>
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<div className="bg-gradient-to-br from-blue-900/30 to-blue-800/20 border border-blue-700/50 rounded-xl p-6">
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<div className="text-3xl mb-2">📈</div>
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<h3 className="text-lg font-semibold text-white mb-2">Kontinuierliches Lernen</h3>
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<p className="text-sm text-gray-300">
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Mit LoRA Fine-Tuning passt sich das Modell an individuelle
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Handschriften an - ohne das Basismodell zu verändern.
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</p>
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</div>
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</div>
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{/* Workflow Overview */}
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<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
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<h2 className="text-lg font-semibold text-white mb-4">Magic Onboarding Workflow</h2>
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<div className="flex flex-wrap items-center gap-4 text-sm">
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<div className="flex items-center gap-2 bg-gray-900/50 rounded-lg px-4 py-3">
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<span className="text-2xl">📄</span>
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<div>
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<div className="font-medium text-white">1. Upload</div>
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<div className="text-gray-400">25 Klausuren hochladen</div>
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</div>
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</div>
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<div className="text-gray-600">→</div>
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<div className="flex items-center gap-2 bg-gray-900/50 rounded-lg px-4 py-3">
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<span className="text-2xl">🔍</span>
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<div>
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<div className="font-medium text-white">2. Analyse</div>
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<div className="text-gray-400">Lokale OCR in 5-10 Sek</div>
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</div>
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</div>
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<div className="text-gray-600">→</div>
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<div className="flex items-center gap-2 bg-gray-900/50 rounded-lg px-4 py-3">
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<span className="text-2xl">✅</span>
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<div>
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<div className="font-medium text-white">3. Bestätigung</div>
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<div className="text-gray-400">Klasse, Schüler, Fach</div>
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</div>
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</div>
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<div className="text-gray-600">→</div>
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<div className="flex items-center gap-2 bg-gray-900/50 rounded-lg px-4 py-3">
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<span className="text-2xl">🤖</span>
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<div>
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<div className="font-medium text-white">4. KI-Korrektur</div>
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<div className="text-gray-400">Cloud mit Pseudonymisierung</div>
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</div>
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</div>
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<div className="text-gray-600">→</div>
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<div className="flex items-center gap-2 bg-gray-900/50 rounded-lg px-4 py-3">
|
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<span className="text-2xl">📊</span>
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<div>
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<div className="font-medium text-white">5. Integration</div>
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<div className="text-gray-400">Notenbuch, Zeugnisse</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
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)}
|
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|
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{activeTab === 'test' && (
|
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<div className="space-y-6">
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{/* OCR Test */}
|
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<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
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<h2 className="text-lg font-semibold text-white mb-4">OCR Test</h2>
|
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<p className="text-sm text-gray-400 mb-4">
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Teste die Handschrifterkennung mit einem eigenen Bild. Das Ergebnis zeigt
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den erkannten Text, Konfidenz und Verarbeitungszeit.
|
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</p>
|
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|
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<div
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className="border-2 border-dashed border-gray-600 rounded-lg p-8 text-center cursor-pointer hover:border-blue-500 transition-colors"
|
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onClick={() => document.getElementById('ocr-file-input')?.click()}
|
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onDragOver={(e) => { e.preventDefault(); e.currentTarget.classList.add('border-blue-500') }}
|
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onDragLeave={(e) => { e.currentTarget.classList.remove('border-blue-500') }}
|
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onDrop={(e) => {
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e.preventDefault()
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e.currentTarget.classList.remove('border-blue-500')
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const file = e.dataTransfer.files[0]
|
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if (file?.type.startsWith('image/')) handleFileUpload(file)
|
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}}
|
||
>
|
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<div className="text-4xl mb-2">📄</div>
|
||
<div className="text-gray-300">Bild hierher ziehen oder klicken zum Hochladen</div>
|
||
<div className="text-xs text-gray-500 mt-1">PNG, JPG - Handgeschriebener Text</div>
|
||
</div>
|
||
<input
|
||
type="file"
|
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id="ocr-file-input"
|
||
accept="image/*"
|
||
className="hidden"
|
||
onChange={(e) => {
|
||
const file = e.target.files?.[0]
|
||
if (file) handleFileUpload(file)
|
||
}}
|
||
/>
|
||
|
||
{ocrLoading && (
|
||
<div className="mt-4 flex items-center gap-2 text-gray-400">
|
||
<div className="animate-spin rounded-full h-4 w-4 border-b-2 border-white"></div>
|
||
Analysiere Bild...
|
||
</div>
|
||
)}
|
||
|
||
{ocrResult && (
|
||
<div className="mt-4 bg-gray-900/50 rounded-lg p-4">
|
||
<h3 className="text-sm font-medium text-gray-300 mb-2">Erkannter Text:</h3>
|
||
<pre className="bg-gray-950 p-3 rounded text-sm text-white whitespace-pre-wrap max-h-48 overflow-y-auto">
|
||
{ocrResult.text || '(Kein Text erkannt)'}
|
||
</pre>
|
||
<div className="mt-3 grid grid-cols-2 md:grid-cols-4 gap-4 text-sm">
|
||
<div className="bg-gray-800 rounded p-2">
|
||
<div className="text-gray-400 text-xs">Konfidenz</div>
|
||
<div className="text-white font-medium">{(ocrResult.confidence * 100).toFixed(1)}%</div>
|
||
</div>
|
||
<div className="bg-gray-800 rounded p-2">
|
||
<div className="text-gray-400 text-xs">Verarbeitungszeit</div>
|
||
<div className="text-white font-medium">{ocrResult.processing_time_ms}ms</div>
|
||
</div>
|
||
<div className="bg-gray-800 rounded p-2">
|
||
<div className="text-gray-400 text-xs">Modell</div>
|
||
<div className="text-white font-medium">{ocrResult.model || 'TrOCR'}</div>
|
||
</div>
|
||
<div className="bg-gray-800 rounded p-2">
|
||
<div className="text-gray-400 text-xs">LoRA Adapter</div>
|
||
<div className="text-white font-medium">{ocrResult.has_lora_adapter ? 'Ja' : 'Nein'}</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
)}
|
||
</div>
|
||
|
||
{/* Confidence Interpretation */}
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-4">Konfidenz-Interpretation</h2>
|
||
<div className="grid grid-cols-1 md:grid-cols-3 gap-4">
|
||
<div className="bg-green-900/20 border border-green-800 rounded-lg p-4">
|
||
<div className="text-green-400 font-medium">90-100%</div>
|
||
<div className="text-sm text-gray-300 mt-1">Sehr hohe Sicherheit - Text kann direkt übernommen werden</div>
|
||
</div>
|
||
<div className="bg-yellow-900/20 border border-yellow-800 rounded-lg p-4">
|
||
<div className="text-yellow-400 font-medium">70-90%</div>
|
||
<div className="text-sm text-gray-300 mt-1">Gute Sicherheit - manuelle Überprüfung empfohlen</div>
|
||
</div>
|
||
<div className="bg-red-900/20 border border-red-800 rounded-lg p-4">
|
||
<div className="text-red-400 font-medium">< 70%</div>
|
||
<div className="text-sm text-gray-300 mt-1">Niedrige Sicherheit - manuelle Eingabe erforderlich</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
)}
|
||
|
||
{activeTab === 'training' && (
|
||
<div className="space-y-6">
|
||
{/* Training Overview */}
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-4">Training mit LoRA</h2>
|
||
<p className="text-sm text-gray-400 mb-4">
|
||
LoRA (Low-Rank Adaptation) ermöglicht effizientes Fine-Tuning ohne das Basismodell zu verändern.
|
||
Das Training erfolgt lokal auf Ihrem System.
|
||
</p>
|
||
|
||
<div className="grid grid-cols-2 md:grid-cols-4 gap-4 mb-6">
|
||
<div className="bg-gray-900/50 rounded-lg p-4 text-center">
|
||
<div className="text-3xl font-bold text-white">{status?.training_examples_count || 0}</div>
|
||
<div className="text-xs text-gray-400">Trainingsbeispiele</div>
|
||
</div>
|
||
<div className="bg-gray-900/50 rounded-lg p-4 text-center">
|
||
<div className="text-3xl font-bold text-white">10</div>
|
||
<div className="text-xs text-gray-400">Minimum benötigt</div>
|
||
</div>
|
||
<div className="bg-gray-900/50 rounded-lg p-4 text-center">
|
||
<div className="text-3xl font-bold text-white">{settings.loraRank}</div>
|
||
<div className="text-xs text-gray-400">LoRA Rank</div>
|
||
</div>
|
||
<div className="bg-gray-900/50 rounded-lg p-4 text-center">
|
||
<div className="text-3xl font-bold text-white">{status?.has_lora_adapter ? '✓' : '✗'}</div>
|
||
<div className="text-xs text-gray-400">Adapter aktiv</div>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Progress Bar */}
|
||
<div className="mb-6">
|
||
<div className="flex justify-between text-sm mb-1">
|
||
<span className="text-gray-400">Fortschritt zum Fine-Tuning</span>
|
||
<span className="text-gray-400">{Math.min(100, ((status?.training_examples_count || 0) / 10) * 100).toFixed(0)}%</span>
|
||
</div>
|
||
<div className="h-2 bg-gray-700 rounded-full overflow-hidden">
|
||
<div
|
||
className="h-full bg-gradient-to-r from-purple-500 to-blue-500 transition-all duration-500"
|
||
style={{ width: `${Math.min(100, ((status?.training_examples_count || 0) / 10) * 100)}%` }}
|
||
/>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div className="grid grid-cols-1 md:grid-cols-2 gap-6">
|
||
{/* Add Training Example */}
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-4">Trainingsbeispiel hinzufügen</h2>
|
||
<p className="text-sm text-gray-400 mb-4">
|
||
Lade ein Bild mit handgeschriebenem Text hoch und gib die korrekte Transkription ein.
|
||
</p>
|
||
|
||
<div className="space-y-4">
|
||
<div>
|
||
<label className="block text-sm text-gray-300 mb-1">Bild</label>
|
||
<input
|
||
type="file"
|
||
accept="image/*"
|
||
className="w-full bg-gray-900 border border-gray-700 rounded-lg px-3 py-2 text-sm"
|
||
onChange={(e) => setTrainingImage(e.target.files?.[0] || null)}
|
||
/>
|
||
</div>
|
||
<div>
|
||
<label className="block text-sm text-gray-300 mb-1">Korrekter Text (Ground Truth)</label>
|
||
<textarea
|
||
className="w-full bg-gray-900 border border-gray-700 rounded-lg px-3 py-2 text-sm text-white resize-none"
|
||
rows={3}
|
||
placeholder="Gib hier den korrekten Text ein..."
|
||
value={trainingText}
|
||
onChange={(e) => setTrainingText(e.target.value)}
|
||
/>
|
||
</div>
|
||
<button
|
||
onClick={handleAddTrainingExample}
|
||
className="w-full px-4 py-2 bg-purple-600 hover:bg-purple-700 rounded-lg text-sm font-medium transition-colors"
|
||
>
|
||
+ Trainingsbeispiel hinzufügen
|
||
</button>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Fine-Tuning */}
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-4">Fine-Tuning starten</h2>
|
||
<p className="text-sm text-gray-400 mb-4">
|
||
Trainiere das Modell mit den gesammelten Beispielen. Der Prozess dauert
|
||
je nach Anzahl der Beispiele einige Minuten.
|
||
</p>
|
||
|
||
<div className="bg-gray-900/50 rounded-lg p-4 mb-4">
|
||
<div className="grid grid-cols-2 gap-4 text-sm">
|
||
<div>
|
||
<span className="text-gray-400">Epochen:</span>
|
||
<span className="text-white ml-2">{settings.epochs}</span>
|
||
</div>
|
||
<div>
|
||
<span className="text-gray-400">Learning Rate:</span>
|
||
<span className="text-white ml-2">{settings.learningRate}</span>
|
||
</div>
|
||
<div>
|
||
<span className="text-gray-400">LoRA Rank:</span>
|
||
<span className="text-white ml-2">{settings.loraRank}</span>
|
||
</div>
|
||
<div>
|
||
<span className="text-gray-400">Batch Size:</span>
|
||
<span className="text-white ml-2">{settings.batchSize}</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<button
|
||
onClick={handleFineTune}
|
||
disabled={fineTuning || (status?.training_examples_count || 0) < 10}
|
||
className="w-full px-4 py-2 bg-green-600 hover:bg-green-700 disabled:bg-gray-700 disabled:cursor-not-allowed rounded-lg text-sm font-medium transition-colors"
|
||
>
|
||
{fineTuning ? (
|
||
<span className="flex items-center justify-center gap-2">
|
||
<div className="animate-spin rounded-full h-4 w-4 border-b-2 border-white"></div>
|
||
Fine-Tuning läuft...
|
||
</span>
|
||
) : (
|
||
'Fine-Tuning starten'
|
||
)}
|
||
</button>
|
||
|
||
{(status?.training_examples_count || 0) < 10 && (
|
||
<p className="text-xs text-yellow-400 mt-2 text-center">
|
||
Noch {10 - (status?.training_examples_count || 0)} Beispiele benötigt
|
||
</p>
|
||
)}
|
||
</div>
|
||
</div>
|
||
|
||
{/* Training Examples List */}
|
||
{examples.length > 0 && (
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-4">Trainingsbeispiele ({examples.length})</h2>
|
||
<div className="space-y-2 max-h-64 overflow-y-auto">
|
||
{examples.map((ex, i) => (
|
||
<div key={i} className="flex items-center gap-4 bg-gray-900/50 rounded-lg p-3">
|
||
<span className="text-gray-500 font-mono text-sm w-8">{i + 1}.</span>
|
||
<span className="text-white text-sm flex-1 truncate">{ex.ground_truth}</span>
|
||
<span className="text-gray-500 text-xs">{new Date(ex.created_at).toLocaleDateString('de-DE')}</span>
|
||
</div>
|
||
))}
|
||
</div>
|
||
</div>
|
||
)}
|
||
</div>
|
||
)}
|
||
|
||
{activeTab === 'architecture' && (
|
||
<div className="space-y-6">
|
||
{/* Architecture Diagram */}
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-6">Systemarchitektur</h2>
|
||
|
||
{/* ASCII Art Diagram */}
|
||
<div className="bg-gray-900 rounded-lg p-6 font-mono text-xs overflow-x-auto">
|
||
<pre className="text-gray-300">
|
||
{`┌─────────────────────────────────────────────────────────────────────────────┐
|
||
│ MAGIC HELP ARCHITEKTUR │
|
||
├─────────────────────────────────────────────────────────────────────────────┤
|
||
│ │
|
||
│ ┌───────────────┐ ┌──────────────────┐ ┌───────────────┐ │
|
||
│ │ FRONTEND │ │ BACKEND │ │ STORAGE │ │
|
||
│ │ (Next.js) │ │ (FastAPI) │ │ │ │
|
||
│ │ │ │ │ │ │ │
|
||
│ │ ┌─────────┐ │ REST │ ┌────────────┐ │ │ ┌─────────┐ │ │
|
||
│ │ │ Admin │──┼─────────┼──│ TrOCR │ │ │ │ Models │ │ │
|
||
│ │ │ Panel │ │ │ │ Service │──┼─────────┼──│ (ONNX) │ │ │
|
||
│ │ └─────────┘ │ │ └────────────┘ │ │ └─────────┘ │ │
|
||
│ │ │ │ │ │ │ │ │
|
||
│ │ ┌─────────┐ │ WebSocket│ ┌────────────┐ │ │ ┌─────────┐ │ │
|
||
│ │ │ Lehrer │──┼─────────┼──│ Klausur │ │ │ │ LoRA │ │ │
|
||
│ │ │ Portal │ │ │ │ Processor │──┼─────────┼──│ Adapter │ │ │
|
||
│ │ └─────────┘ │ │ └────────────┘ │ │ └─────────┘ │ │
|
||
│ │ │ │ │ │ │ │ │
|
||
│ └───────────────┘ │ ┌────────────┐ │ │ ┌─────────┐ │ │
|
||
│ │ │ Pseudo- │ │ │ │Training │ │ │
|
||
│ │ │ nymizer │──┼─────────┼──│ Data │ │ │
|
||
│ │ └────────────┘ │ │ └─────────┘ │ │
|
||
│ │ │ │ │ │
|
||
│ └──────────────────┘ └───────────────┘ │
|
||
│ │ │
|
||
│ │ (nur pseudonymisiert) │
|
||
│ ▼ │
|
||
│ ┌──────────────────┐ │
|
||
│ │ CLOUD LLM │ │
|
||
│ │ (SysEleven) │ │
|
||
│ │ Namespace- │ │
|
||
│ │ Isolation │ │
|
||
│ └──────────────────┘ │
|
||
│ │
|
||
└─────────────────────────────────────────────────────────────────────────────┘`}
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Components */}
|
||
<div className="grid grid-cols-1 md:grid-cols-2 gap-6">
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h3 className="text-lg font-semibold text-white mb-4 flex items-center gap-2">
|
||
<span>🔍</span> TrOCR Service
|
||
</h3>
|
||
<div className="space-y-3 text-sm">
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Modell</span>
|
||
<span className="text-white">microsoft/trocr-base-handwritten</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Größe</span>
|
||
<span className="text-white">~350 MB</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Lizenz</span>
|
||
<span className="text-white">MIT</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Framework</span>
|
||
<span className="text-white">PyTorch / Transformers</span>
|
||
</div>
|
||
</div>
|
||
<p className="text-gray-400 text-sm mt-4">
|
||
Das TrOCR-Modell von Microsoft ist speziell für Handschrifterkennung trainiert.
|
||
Es verwendet eine Vision-Transformer (ViT) Architektur für Bildverarbeitung
|
||
und einen Text-Decoder für die Textgenerierung.
|
||
</p>
|
||
</div>
|
||
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h3 className="text-lg font-semibold text-white mb-4 flex items-center gap-2">
|
||
<span>🎯</span> LoRA Fine-Tuning
|
||
</h3>
|
||
<div className="space-y-3 text-sm">
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Methode</span>
|
||
<span className="text-white">Low-Rank Adaptation</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Adapter-Größe</span>
|
||
<span className="text-white">~10 MB</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Trainingszeit</span>
|
||
<span className="text-white">5-15 Min (CPU)</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Min. Beispiele</span>
|
||
<span className="text-white">10</span>
|
||
</div>
|
||
</div>
|
||
<p className="text-gray-400 text-sm mt-4">
|
||
LoRA fügt kleine, trainierbare Matrizen zu bestimmten Schichten hinzu,
|
||
ohne das Basismodell zu verändern. Dies ermöglicht effizientes Fine-Tuning
|
||
mit minimaler Speichernutzung.
|
||
</p>
|
||
</div>
|
||
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h3 className="text-lg font-semibold text-white mb-4 flex items-center gap-2">
|
||
<span>🔒</span> Pseudonymisierung
|
||
</h3>
|
||
<div className="space-y-3 text-sm">
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Methode</span>
|
||
<span className="text-white">QR-Code Tokens</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Token-Format</span>
|
||
<span className="text-white">UUID v4</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Mapping</span>
|
||
<span className="text-white">Lokal beim Lehrer</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Cloud-Daten</span>
|
||
<span className="text-white">Nur Tokens + Text</span>
|
||
</div>
|
||
</div>
|
||
<p className="text-gray-400 text-sm mt-4">
|
||
Schülernamen werden durch anonyme Tokens ersetzt, bevor Daten die lokale
|
||
Umgebung verlassen. Das Mapping wird ausschließlich lokal gespeichert.
|
||
</p>
|
||
</div>
|
||
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h3 className="text-lg font-semibold text-white mb-4 flex items-center gap-2">
|
||
<span>☁️</span> Cloud LLM
|
||
</h3>
|
||
<div className="space-y-3 text-sm">
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Provider</span>
|
||
<span className="text-white">SysEleven (DE)</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Standort</span>
|
||
<span className="text-white">Deutschland</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Isolation</span>
|
||
<span className="text-white">Namespace pro Schule</span>
|
||
</div>
|
||
<div className="flex justify-between">
|
||
<span className="text-gray-400">Datenverarbeitung</span>
|
||
<span className="text-white">Nur pseudonymisiert</span>
|
||
</div>
|
||
</div>
|
||
<p className="text-gray-400 text-sm mt-4">
|
||
Die KI-Korrektur erfolgt auf deutschen Servern mit strikter Mandantentrennung.
|
||
Es werden keine Klarnamen oder identifizierenden Informationen übertragen.
|
||
</p>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Data Flow */}
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-4">Datenfluss</h2>
|
||
<div className="space-y-4">
|
||
<div className="flex items-start gap-4 bg-gray-900/50 rounded-lg p-4">
|
||
<div className="w-8 h-8 rounded-full bg-blue-500/20 flex items-center justify-center text-blue-400 font-bold">1</div>
|
||
<div>
|
||
<div className="font-medium text-white">Lokale Header-Extraktion</div>
|
||
<div className="text-sm text-gray-400">TrOCR erkennt Schülernamen, Klasse und Fach direkt im Browser/PWA (offline-fähig)</div>
|
||
</div>
|
||
</div>
|
||
<div className="flex items-start gap-4 bg-gray-900/50 rounded-lg p-4">
|
||
<div className="w-8 h-8 rounded-full bg-purple-500/20 flex items-center justify-center text-purple-400 font-bold">2</div>
|
||
<div>
|
||
<div className="font-medium text-white">Pseudonymisierung</div>
|
||
<div className="text-sm text-gray-400">Namen werden durch QR-Code Tokens ersetzt, Mapping bleibt lokal</div>
|
||
</div>
|
||
</div>
|
||
<div className="flex items-start gap-4 bg-gray-900/50 rounded-lg p-4">
|
||
<div className="w-8 h-8 rounded-full bg-green-500/20 flex items-center justify-center text-green-400 font-bold">3</div>
|
||
<div>
|
||
<div className="font-medium text-white">Cloud-Korrektur</div>
|
||
<div className="text-sm text-gray-400">Nur pseudonymisierte Dokument-Tokens werden an die KI gesendet</div>
|
||
</div>
|
||
</div>
|
||
<div className="flex items-start gap-4 bg-gray-900/50 rounded-lg p-4">
|
||
<div className="w-8 h-8 rounded-full bg-yellow-500/20 flex items-center justify-center text-yellow-400 font-bold">4</div>
|
||
<div>
|
||
<div className="font-medium text-white">Re-Identifikation</div>
|
||
<div className="text-sm text-gray-400">Ergebnisse werden lokal mit dem Mapping wieder den echten Namen zugeordnet</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
)}
|
||
|
||
{activeTab === 'settings' && (
|
||
<div className="space-y-6">
|
||
{/* OCR Settings */}
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-4">OCR Einstellungen</h2>
|
||
|
||
<div className="grid grid-cols-1 md:grid-cols-2 gap-6">
|
||
<div>
|
||
<label className="flex items-center gap-3 cursor-pointer">
|
||
<input
|
||
type="checkbox"
|
||
checked={settings.autoDetectLines}
|
||
onChange={(e) => setSettings({ ...settings, autoDetectLines: e.target.checked })}
|
||
className="w-5 h-5 rounded bg-gray-900 border-gray-700"
|
||
/>
|
||
<div>
|
||
<div className="text-white font-medium">Automatische Zeilenerkennung</div>
|
||
<div className="text-sm text-gray-400">Erkennt und verarbeitet einzelne Zeilen separat</div>
|
||
</div>
|
||
</label>
|
||
</div>
|
||
|
||
<div>
|
||
<label className="block text-sm text-gray-300 mb-2">Konfidenz-Schwellwert</label>
|
||
<input
|
||
type="range"
|
||
min="0"
|
||
max="1"
|
||
step="0.1"
|
||
value={settings.confidenceThreshold}
|
||
onChange={(e) => setSettings({ ...settings, confidenceThreshold: parseFloat(e.target.value) })}
|
||
className="w-full"
|
||
/>
|
||
<div className="flex justify-between text-xs text-gray-500 mt-1">
|
||
<span>0%</span>
|
||
<span className="text-white">{(settings.confidenceThreshold * 100).toFixed(0)}%</span>
|
||
<span>100%</span>
|
||
</div>
|
||
</div>
|
||
|
||
<div>
|
||
<label className="block text-sm text-gray-300 mb-2">Max. Bildgröße (px)</label>
|
||
<input
|
||
type="number"
|
||
value={settings.maxImageSize}
|
||
onChange={(e) => setSettings({ ...settings, maxImageSize: parseInt(e.target.value) })}
|
||
className="w-full bg-gray-900 border border-gray-700 rounded-lg px-3 py-2 text-white"
|
||
/>
|
||
<div className="text-xs text-gray-500 mt-1">Größere Bilder werden skaliert</div>
|
||
</div>
|
||
|
||
<div>
|
||
<label className="flex items-center gap-3 cursor-pointer">
|
||
<input
|
||
type="checkbox"
|
||
checked={settings.enableCache}
|
||
onChange={(e) => setSettings({ ...settings, enableCache: e.target.checked })}
|
||
className="w-5 h-5 rounded bg-gray-900 border-gray-700"
|
||
/>
|
||
<div>
|
||
<div className="text-white font-medium">Ergebnis-Cache aktivieren</div>
|
||
<div className="text-sm text-gray-400">Speichert OCR-Ergebnisse für identische Bilder</div>
|
||
</div>
|
||
</label>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Training Settings */}
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-4">Training Einstellungen</h2>
|
||
|
||
<div className="grid grid-cols-1 md:grid-cols-2 gap-6">
|
||
<div>
|
||
<label className="block text-sm text-gray-300 mb-2">LoRA Rank</label>
|
||
<select
|
||
value={settings.loraRank}
|
||
onChange={(e) => setSettings({ ...settings, loraRank: parseInt(e.target.value) })}
|
||
className="w-full bg-gray-900 border border-gray-700 rounded-lg px-3 py-2 text-white"
|
||
>
|
||
<option value="4">4 (Schnell, weniger Kapazität)</option>
|
||
<option value="8">8 (Ausgewogen)</option>
|
||
<option value="16">16 (Mehr Kapazität)</option>
|
||
<option value="32">32 (Maximum)</option>
|
||
</select>
|
||
</div>
|
||
|
||
<div>
|
||
<label className="block text-sm text-gray-300 mb-2">LoRA Alpha</label>
|
||
<input
|
||
type="number"
|
||
value={settings.loraAlpha}
|
||
onChange={(e) => setSettings({ ...settings, loraAlpha: parseInt(e.target.value) })}
|
||
className="w-full bg-gray-900 border border-gray-700 rounded-lg px-3 py-2 text-white"
|
||
/>
|
||
<div className="text-xs text-gray-500 mt-1">Empfohlen: 4 × LoRA Rank</div>
|
||
</div>
|
||
|
||
<div>
|
||
<label className="block text-sm text-gray-300 mb-2">Epochen</label>
|
||
<input
|
||
type="number"
|
||
min="1"
|
||
max="10"
|
||
value={settings.epochs}
|
||
onChange={(e) => setSettings({ ...settings, epochs: parseInt(e.target.value) })}
|
||
className="w-full bg-gray-900 border border-gray-700 rounded-lg px-3 py-2 text-white"
|
||
/>
|
||
</div>
|
||
|
||
<div>
|
||
<label className="block text-sm text-gray-300 mb-2">Batch Size</label>
|
||
<select
|
||
value={settings.batchSize}
|
||
onChange={(e) => setSettings({ ...settings, batchSize: parseInt(e.target.value) })}
|
||
className="w-full bg-gray-900 border border-gray-700 rounded-lg px-3 py-2 text-white"
|
||
>
|
||
<option value="1">1 (Wenig RAM)</option>
|
||
<option value="2">2</option>
|
||
<option value="4">4 (Standard)</option>
|
||
<option value="8">8 (Viel RAM)</option>
|
||
</select>
|
||
</div>
|
||
|
||
<div>
|
||
<label className="block text-sm text-gray-300 mb-2">Learning Rate</label>
|
||
<select
|
||
value={settings.learningRate}
|
||
onChange={(e) => setSettings({ ...settings, learningRate: parseFloat(e.target.value) })}
|
||
className="w-full bg-gray-900 border border-gray-700 rounded-lg px-3 py-2 text-white"
|
||
>
|
||
<option value="0.0001">0.0001 (Schnell)</option>
|
||
<option value="0.00005">0.00005 (Standard)</option>
|
||
<option value="0.00001">0.00001 (Konservativ)</option>
|
||
</select>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Save Button */}
|
||
<div className="flex justify-end gap-4">
|
||
<button
|
||
onClick={() => setSettings(DEFAULT_SETTINGS)}
|
||
className="px-6 py-2 bg-gray-700 hover:bg-gray-600 rounded-lg text-sm font-medium transition-colors"
|
||
>
|
||
Zurücksetzen
|
||
</button>
|
||
<button
|
||
onClick={saveSettings}
|
||
className="px-6 py-2 bg-blue-600 hover:bg-blue-700 rounded-lg text-sm font-medium transition-colors"
|
||
>
|
||
{settingsSaved ? '✓ Gespeichert!' : 'Einstellungen speichern'}
|
||
</button>
|
||
</div>
|
||
|
||
{/* Technical Info */}
|
||
<div className="bg-gray-800/50 border border-gray-700 rounded-xl p-6">
|
||
<h2 className="text-lg font-semibold text-white mb-4">Technische Informationen</h2>
|
||
<div className="grid grid-cols-2 md:grid-cols-4 gap-4 text-sm">
|
||
<div>
|
||
<span className="text-gray-400">API Endpoint:</span>
|
||
<code className="text-white ml-2 bg-gray-900 px-2 py-1 rounded text-xs">/api/klausur/trocr</code>
|
||
</div>
|
||
<div>
|
||
<span className="text-gray-400">Model Path:</span>
|
||
<code className="text-white ml-2 bg-gray-900 px-2 py-1 rounded text-xs">~/.cache/huggingface</code>
|
||
</div>
|
||
<div>
|
||
<span className="text-gray-400">LoRA Path:</span>
|
||
<code className="text-white ml-2 bg-gray-900 px-2 py-1 rounded text-xs">./models/lora</code>
|
||
</div>
|
||
<div>
|
||
<span className="text-gray-400">Training Data:</span>
|
||
<code className="text-white ml-2 bg-gray-900 px-2 py-1 rounded text-xs">./data/training</code>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
)}
|
||
</div>
|
||
</AdminLayout>
|
||
)
|
||
}
|