feat(ocr-pipeline): add LLM-based OCR correction step (Step 6)
Replace the placeholder "Koordinaten" step with an LLM review step that sends vocab entries to qwen3:30b-a3b via Ollama for OCR error correction (e.g. "8en" → "Ben"). Teachers can review, accept/reject individual corrections in a diff table before applying them. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
@@ -8,7 +8,7 @@ import { StepDewarp } from '@/components/ocr-pipeline/StepDewarp'
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import { StepColumnDetection } from '@/components/ocr-pipeline/StepColumnDetection'
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import { StepRowDetection } from '@/components/ocr-pipeline/StepRowDetection'
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import { StepWordRecognition } from '@/components/ocr-pipeline/StepWordRecognition'
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import { StepCoordinates } from '@/components/ocr-pipeline/StepCoordinates'
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import { StepLlmReview } from '@/components/ocr-pipeline/StepLlmReview'
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import { StepReconstruction } from '@/components/ocr-pipeline/StepReconstruction'
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import { StepGroundTruth } from '@/components/ocr-pipeline/StepGroundTruth'
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import { PIPELINE_STEPS, type PipelineStep, type SessionListItem } from './types'
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@@ -155,7 +155,7 @@ export default function OcrPipelinePage() {
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3: 'Spalten',
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4: 'Zeilen',
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5: 'Woerter',
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6: 'Koordinaten',
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6: 'LLM-Korrektur',
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7: 'Rekonstruktion',
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8: 'Validierung',
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}
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@@ -173,7 +173,7 @@ export default function OcrPipelinePage() {
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case 4:
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return <StepWordRecognition sessionId={sessionId} onNext={handleNext} goToStep={goToStep} />
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case 5:
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return <StepCoordinates />
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return <StepLlmReview sessionId={sessionId} onNext={handleNext} />
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case 6:
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return <StepReconstruction />
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case 7:
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@@ -208,7 +208,7 @@ export const PIPELINE_STEPS: PipelineStep[] = [
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{ id: 'columns', name: 'Spalten', icon: '📊', status: 'pending' },
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{ id: 'rows', name: 'Zeilen', icon: '📏', status: 'pending' },
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{ id: 'words', name: 'Woerter', icon: '🔤', status: 'pending' },
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{ id: 'coordinates', name: 'Koordinaten', icon: '📍', status: 'pending' },
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{ id: 'llm-review', name: 'LLM-Korrektur', icon: '🤖', status: 'pending' },
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{ id: 'reconstruction', name: 'Rekonstruktion', icon: '🏗️', status: 'pending' },
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{ id: 'ground-truth', name: 'Validierung', icon: '✅', status: 'pending' },
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]
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345
admin-lehrer/components/ocr-pipeline/StepLlmReview.tsx
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345
admin-lehrer/components/ocr-pipeline/StepLlmReview.tsx
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@@ -0,0 +1,345 @@
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'use client'
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import { useCallback, useState } from 'react'
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const KLAUSUR_API = '/klausur-api'
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interface LlmChange {
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row_index: number
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field: 'english' | 'german' | 'example'
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old: string
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new: string
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}
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interface LlmReviewResult {
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changes: LlmChange[]
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model_used: string
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duration_ms: number
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total_entries: number
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corrections_found: number
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}
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interface StepLlmReviewProps {
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sessionId: string | null
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onNext: () => void
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}
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const FIELD_LABELS: Record<string, string> = {
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english: 'EN',
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german: 'DE',
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example: 'Beispiel',
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}
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export function StepLlmReview({ sessionId, onNext }: StepLlmReviewProps) {
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const [status, setStatus] = useState<'idle' | 'running' | 'done' | 'error' | 'applied'>('idle')
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const [result, setResult] = useState<LlmReviewResult | null>(null)
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const [error, setError] = useState<string>('')
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const [accepted, setAccepted] = useState<Set<number>>(new Set())
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const [applying, setApplying] = useState(false)
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const runReview = useCallback(async () => {
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if (!sessionId) return
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setStatus('running')
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setError('')
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setResult(null)
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try {
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const res = await fetch(`${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}/llm-review`, {
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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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})
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if (!res.ok) {
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const data = await res.json().catch(() => ({}))
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throw new Error(data.detail || `HTTP ${res.status}`)
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}
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const data: LlmReviewResult = await res.json()
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setResult(data)
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// Accept all changes by default
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setAccepted(new Set(data.changes.map((_, i) => i)))
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setStatus('done')
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} catch (e: unknown) {
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const msg = e instanceof Error ? e.message : String(e)
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setError(msg)
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setStatus('error')
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}
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}, [sessionId])
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const toggleChange = (index: number) => {
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setAccepted((prev) => {
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const next = new Set(prev)
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if (next.has(index)) next.delete(index)
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else next.add(index)
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return next
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})
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}
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const toggleAll = () => {
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if (!result) return
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if (accepted.size === result.changes.length) {
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setAccepted(new Set())
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} else {
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setAccepted(new Set(result.changes.map((_, i) => i)))
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}
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}
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const applyChanges = useCallback(async () => {
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if (!sessionId || !result) return
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setApplying(true)
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try {
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const res = await fetch(`${KLAUSUR_API}/api/v1/ocr-pipeline/sessions/${sessionId}/llm-review/apply`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ accepted_indices: Array.from(accepted) }),
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})
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if (!res.ok) {
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const data = await res.json().catch(() => ({}))
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throw new Error(data.detail || `HTTP ${res.status}`)
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}
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setStatus('applied')
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} catch (e: unknown) {
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const msg = e instanceof Error ? e.message : String(e)
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setError(msg)
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} finally {
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setApplying(false)
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}
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}, [sessionId, result, accepted])
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if (!sessionId) {
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return (
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<div className="text-center py-12 text-gray-400">
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Bitte zuerst eine Session auswaehlen.
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</div>
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)
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}
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// --- Idle state ---
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if (status === 'idle') {
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return (
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<div className="flex flex-col items-center justify-center py-12 text-center">
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<div className="text-5xl mb-4">🤖</div>
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<h3 className="text-lg font-medium text-gray-700 dark:text-gray-300 mb-2">
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Schritt 6: LLM-Korrektur
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</h3>
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<p className="text-gray-500 dark:text-gray-400 max-w-lg mb-2">
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Ein lokales Sprachmodell prueft die OCR-Ergebnisse auf typische Erkennungsfehler
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(z.B. "8en" statt "Ben") und schlaegt Korrekturen vor.
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</p>
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<p className="text-xs text-gray-400 dark:text-gray-500 mb-6">
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Modell: <span className="font-mono">qwen3:30b-a3b</span> via Ollama (lokal)
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</p>
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<button
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onClick={runReview}
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className="px-6 py-2.5 bg-teal-600 text-white rounded-lg hover:bg-teal-700 transition-colors font-medium"
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>
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LLM-Korrektur starten
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</button>
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</div>
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)
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}
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// --- Running state ---
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if (status === 'running') {
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return (
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<div className="flex flex-col items-center justify-center py-16 text-center">
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<div className="animate-spin rounded-full h-10 w-10 border-b-2 border-teal-500 mb-4" />
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<h3 className="text-lg font-medium text-gray-700 dark:text-gray-300 mb-1">
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Korrektur laeuft...
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</h3>
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<p className="text-sm text-gray-400">
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<span className="font-mono">qwen3:30b-a3b</span> prueft die Vokabeleintraege
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</p>
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</div>
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)
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}
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// --- Error state ---
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if (status === 'error') {
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return (
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<div className="flex flex-col items-center justify-center py-12 text-center">
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<div className="text-5xl mb-4">⚠️</div>
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<h3 className="text-lg font-medium text-red-600 dark:text-red-400 mb-2">
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Fehler bei LLM-Korrektur
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</h3>
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<p className="text-sm text-gray-500 dark:text-gray-400 max-w-lg mb-4">
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{error}
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</p>
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<div className="flex gap-3">
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<button
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onClick={runReview}
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className="px-5 py-2 bg-teal-600 text-white rounded-lg hover:bg-teal-700 transition-colors text-sm"
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>
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Erneut versuchen
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</button>
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<button
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onClick={onNext}
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className="px-5 py-2 bg-gray-200 dark:bg-gray-700 text-gray-700 dark:text-gray-300 rounded-lg hover:bg-gray-300 dark:hover:bg-gray-600 transition-colors text-sm"
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>
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Ueberspringen →
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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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// --- Applied state ---
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if (status === 'applied') {
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return (
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<div className="flex flex-col items-center justify-center py-12 text-center">
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<div className="text-5xl mb-4">✅</div>
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<h3 className="text-lg font-medium text-gray-700 dark:text-gray-300 mb-2">
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Korrekturen uebernommen
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</h3>
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<p className="text-sm text-gray-500 dark:text-gray-400 mb-6">
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{accepted.size} von {result?.changes.length ?? 0} Korrekturen wurden angewendet.
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</p>
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<button
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onClick={onNext}
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className="px-6 py-2.5 bg-teal-600 text-white rounded-lg hover:bg-teal-700 transition-colors font-medium"
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>
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Weiter →
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</button>
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</div>
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)
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}
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// --- Done state: show diff table ---
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const changes = result?.changes ?? []
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if (changes.length === 0) {
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return (
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<div className="flex flex-col items-center justify-center py-12 text-center">
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<div className="text-5xl mb-4">👍</div>
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<h3 className="text-lg font-medium text-gray-700 dark:text-gray-300 mb-2">
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Keine Korrekturen noetig
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</h3>
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<p className="text-sm text-gray-500 dark:text-gray-400 mb-1">
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Das LLM hat keine OCR-Fehler gefunden.
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</p>
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<p className="text-xs text-gray-400 dark:text-gray-500 mb-6">
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{result?.total_entries} Eintraege geprueft in {result?.duration_ms}ms
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({result?.model_used})
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</p>
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<button
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onClick={onNext}
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className="px-6 py-2.5 bg-teal-600 text-white rounded-lg hover:bg-teal-700 transition-colors font-medium"
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>
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Weiter →
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</button>
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</div>
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)
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}
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return (
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<div className="space-y-4">
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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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<h3 className="text-base font-medium text-gray-700 dark:text-gray-300">
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LLM-Korrekturvorschlaege
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</h3>
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<p className="text-xs text-gray-400 mt-0.5">
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{changes.length} Korrektur{changes.length !== 1 ? 'en' : ''} gefunden
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· {result?.duration_ms}ms · {result?.model_used}
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</p>
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</div>
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<div className="flex items-center gap-2">
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<button
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onClick={toggleAll}
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className="text-xs px-3 py-1.5 border border-gray-300 dark:border-gray-600 rounded-lg hover:bg-gray-50 dark:hover:bg-gray-700 transition-colors text-gray-600 dark:text-gray-400"
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>
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{accepted.size === changes.length ? 'Keine' : 'Alle'} auswaehlen
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</button>
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</div>
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</div>
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{/* Diff table */}
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<div className="border border-gray-200 dark:border-gray-700 rounded-lg overflow-hidden">
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<table className="w-full text-sm">
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<thead>
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<tr className="bg-gray-50 dark:bg-gray-800 border-b border-gray-200 dark:border-gray-700">
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<th className="w-10 px-3 py-2 text-center">
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<input
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type="checkbox"
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checked={accepted.size === changes.length}
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onChange={toggleAll}
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className="rounded border-gray-300 dark:border-gray-600"
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/>
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</th>
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<th className="px-3 py-2 text-left text-gray-500 dark:text-gray-400 font-medium">Zeile</th>
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<th className="px-3 py-2 text-left text-gray-500 dark:text-gray-400 font-medium">Feld</th>
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<th className="px-3 py-2 text-left text-gray-500 dark:text-gray-400 font-medium">Vorher</th>
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<th className="px-3 py-2 text-left text-gray-500 dark:text-gray-400 font-medium">Nachher</th>
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</tr>
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</thead>
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<tbody>
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{changes.map((change, idx) => (
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<tr
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key={idx}
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className={`border-b border-gray-100 dark:border-gray-700/50 ${
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accepted.has(idx)
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? 'bg-teal-50/50 dark:bg-teal-900/10'
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: 'bg-white dark:bg-gray-800/50'
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}`}
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>
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<td className="px-3 py-2 text-center">
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<input
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type="checkbox"
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checked={accepted.has(idx)}
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onChange={() => toggleChange(idx)}
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className="rounded border-gray-300 dark:border-gray-600"
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/>
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</td>
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<td className="px-3 py-2 text-gray-500 dark:text-gray-400 font-mono text-xs">
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R{change.row_index}
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</td>
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<td className="px-3 py-2">
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<span className="text-xs px-1.5 py-0.5 rounded bg-gray-100 dark:bg-gray-700 text-gray-600 dark:text-gray-400">
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{FIELD_LABELS[change.field] || change.field}
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</span>
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</td>
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<td className="px-3 py-2">
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<span className="line-through text-red-500 dark:text-red-400">
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{change.old}
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</span>
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</td>
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<td className="px-3 py-2">
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<span className="text-green-600 dark:text-green-400 font-medium">
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{change.new}
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</span>
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</td>
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</tr>
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))}
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</tbody>
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</table>
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</div>
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{/* Actions */}
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<div className="flex items-center justify-between pt-2">
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<p className="text-xs text-gray-400">
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{accepted.size} von {changes.length} ausgewaehlt
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</p>
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<div className="flex gap-3">
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<button
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onClick={onNext}
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className="px-4 py-2 text-sm border border-gray-300 dark:border-gray-600 rounded-lg hover:bg-gray-50 dark:hover:bg-gray-700 transition-colors text-gray-600 dark:text-gray-400"
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>
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Alle ablehnen
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</button>
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<button
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onClick={applyChanges}
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disabled={applying || accepted.size === 0}
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className="px-5 py-2 text-sm bg-teal-600 text-white rounded-lg hover:bg-teal-700 disabled:opacity-50 disabled:cursor-not-allowed transition-colors font-medium"
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>
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{applying ? 'Wird uebernommen...' : `${accepted.size} Korrektur${accepted.size !== 1 ? 'en' : ''} uebernehmen`}
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</button>
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</div>
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</div>
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</div>
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)
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}
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@@ -4304,3 +4304,119 @@ async def run_cv_pipeline(
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result.duration_seconds = round(time.time() - total_start, 2)
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return result
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# ---------------------------------------------------------------------------
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# LLM-based OCR Correction (Step 6)
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# ---------------------------------------------------------------------------
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import httpx
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import os
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import json as _json
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import re as _re
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_OLLAMA_URL = os.getenv("OLLAMA_URL", os.getenv("OLLAMA_BASE_URL", "http://host.docker.internal:11434"))
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OLLAMA_REVIEW_MODEL = os.getenv("OLLAMA_REVIEW_MODEL", "qwen3:30b-a3b")
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async def llm_review_entries(
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entries: List[Dict],
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model: str = None,
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) -> Dict:
|
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"""Send vocab entries to a local LLM for OCR error correction."""
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model = model or OLLAMA_REVIEW_MODEL
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|
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# Build a compact table representation for the prompt
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table_lines = []
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for e in entries:
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table_lines.append({
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"row": e.get("row_index", 0),
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"en": e.get("english", ""),
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"de": e.get("german", ""),
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"ex": e.get("example", ""),
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})
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prompt = f"""Du bist ein Korrekturleser fuer OCR-erkannte Vokabeltabellen (Englisch-Deutsch).
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Die Tabelle wurde per OCR aus einem Schulbuch-Scan extrahiert. Korrigiere NUR offensichtliche OCR-Fehler.
|
||||
|
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Haeufige OCR-Fehler die du korrigieren sollst:
|
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- Ziffern statt Buchstaben: 8→B, 0→O, 1→l/I, 5→S, 6→G
|
||||
- Fehlende oder falsche Satzzeichen
|
||||
- Offensichtliche Tippfehler die durch OCR entstanden sind
|
||||
|
||||
WICHTIG:
|
||||
- Aendere NICHTS was korrekt aussieht
|
||||
- Erfinde KEINE neuen Woerter oder Uebersetzungen
|
||||
- Behalte Abkuerzungen wie sth., sb., etc. bei
|
||||
- Behalte die exakte Struktur (gleiche Anzahl Eintraege)
|
||||
|
||||
Antworte NUR mit dem korrigierten JSON-Array. Kein erklaerener Text.
|
||||
Fuer jeden Eintrag den du aenderst, setze "corrected": true.
|
||||
Fuer unveraenderte Eintraege setze "corrected": false.
|
||||
|
||||
Eingabe:
|
||||
{_json.dumps(table_lines, ensure_ascii=False, indent=2)}"""
|
||||
|
||||
t0 = time.time()
|
||||
async with httpx.AsyncClient(timeout=120.0) as client:
|
||||
resp = await client.post(
|
||||
f"{_OLLAMA_URL}/api/chat",
|
||||
json={
|
||||
"model": model,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"stream": False,
|
||||
"options": {"temperature": 0.1, "num_predict": 8192},
|
||||
},
|
||||
)
|
||||
resp.raise_for_status()
|
||||
content = resp.json().get("message", {}).get("content", "")
|
||||
duration_ms = int((time.time() - t0) * 1000)
|
||||
|
||||
# Parse LLM response — extract JSON array
|
||||
corrected = _parse_llm_json_array(content)
|
||||
|
||||
# Build diff: compare original vs corrected
|
||||
changes = []
|
||||
entries_corrected = []
|
||||
for i, orig in enumerate(entries):
|
||||
if i < len(corrected):
|
||||
c = corrected[i]
|
||||
entry = dict(orig)
|
||||
for field_name, key in [("english", "en"), ("german", "de"), ("example", "ex")]:
|
||||
new_val = c.get(key, "").strip()
|
||||
old_val = (orig.get(field_name, "") or "").strip()
|
||||
if new_val and new_val != old_val:
|
||||
changes.append({
|
||||
"row_index": orig.get("row_index", i),
|
||||
"field": field_name,
|
||||
"old": old_val,
|
||||
"new": new_val,
|
||||
})
|
||||
entry[field_name] = new_val
|
||||
entry["llm_corrected"] = True
|
||||
entries_corrected.append(entry)
|
||||
else:
|
||||
entries_corrected.append(dict(orig))
|
||||
|
||||
return {
|
||||
"entries_original": entries,
|
||||
"entries_corrected": entries_corrected,
|
||||
"changes": changes,
|
||||
"model_used": model,
|
||||
"duration_ms": duration_ms,
|
||||
}
|
||||
|
||||
|
||||
def _parse_llm_json_array(text: str) -> List[Dict]:
|
||||
"""Extract JSON array from LLM response (may contain markdown fences)."""
|
||||
# Strip markdown code fences
|
||||
text = _re.sub(r'```json\s*', '', text)
|
||||
text = _re.sub(r'```\s*', '', text)
|
||||
# Find array
|
||||
match = _re.search(r'\[.*\]', text, _re.DOTALL)
|
||||
if match:
|
||||
try:
|
||||
return _json.loads(match.group())
|
||||
except (ValueError, _json.JSONDecodeError):
|
||||
pass
|
||||
return []
|
||||
|
||||
@@ -7,7 +7,7 @@ Zerlegt den OCR-Prozess in 8 einzelne Schritte:
|
||||
3. Spaltenerkennung - Unsichtbare Spalten finden
|
||||
4. Zeilenerkennung - Horizontale Zeilen + Kopf-/Fusszeilen
|
||||
5. Worterkennung - OCR mit Bounding Boxes
|
||||
6. Koordinatenzuweisung - Exakte Positionen
|
||||
6. LLM-Korrektur - OCR-Fehler per LLM korrigieren
|
||||
7. Seitenrekonstruktion - Seite nachbauen
|
||||
8. Ground Truth Validierung - Gesamtpruefung
|
||||
|
||||
@@ -30,6 +30,7 @@ from fastapi.responses import Response, StreamingResponse
|
||||
from pydantic import BaseModel
|
||||
|
||||
from cv_vocab_pipeline import (
|
||||
OLLAMA_REVIEW_MODEL,
|
||||
PageRegion,
|
||||
RowGeometry,
|
||||
_cells_to_vocab_entries,
|
||||
@@ -49,6 +50,7 @@ from cv_vocab_pipeline import (
|
||||
detect_row_geometry,
|
||||
dewarp_image,
|
||||
dewarp_image_manual,
|
||||
llm_review_entries,
|
||||
render_image_high_res,
|
||||
render_pdf_high_res,
|
||||
)
|
||||
@@ -1387,6 +1389,124 @@ async def get_word_ground_truth(session_id: str):
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# LLM Review Endpoints (Step 6)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@router.post("/sessions/{session_id}/llm-review")
|
||||
async def run_llm_review(session_id: str, request: Request):
|
||||
"""Run LLM-based correction on vocab entries from Step 5."""
|
||||
session = await get_session_db(session_id)
|
||||
if not session:
|
||||
raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
|
||||
|
||||
word_result = session.get("word_result")
|
||||
if not word_result:
|
||||
raise HTTPException(status_code=400, detail="No word result found — run Step 5 first")
|
||||
|
||||
entries = word_result.get("vocab_entries") or word_result.get("entries") or []
|
||||
if not entries:
|
||||
raise HTTPException(status_code=400, detail="No vocab entries found — run Step 5 first")
|
||||
|
||||
# Optional model override from request body
|
||||
body = {}
|
||||
try:
|
||||
body = await request.json()
|
||||
except Exception:
|
||||
pass
|
||||
model = body.get("model") or OLLAMA_REVIEW_MODEL
|
||||
|
||||
try:
|
||||
result = await llm_review_entries(entries, model=model)
|
||||
except Exception as e:
|
||||
logger.error(f"LLM review failed for session {session_id}: {e}")
|
||||
raise HTTPException(status_code=502, detail=f"LLM review failed: {e}")
|
||||
|
||||
# Store result inside word_result as a sub-key
|
||||
word_result["llm_review"] = {
|
||||
"changes": result["changes"],
|
||||
"model_used": result["model_used"],
|
||||
"duration_ms": result["duration_ms"],
|
||||
"entries_corrected": result["entries_corrected"],
|
||||
}
|
||||
await update_session_db(session_id, word_result=word_result, current_step=6)
|
||||
|
||||
if session_id in _cache:
|
||||
_cache[session_id]["word_result"] = word_result
|
||||
|
||||
logger.info(f"LLM review session {session_id}: {len(result['changes'])} changes, "
|
||||
f"{result['duration_ms']}ms, model={result['model_used']}")
|
||||
|
||||
return {
|
||||
"session_id": session_id,
|
||||
"changes": result["changes"],
|
||||
"model_used": result["model_used"],
|
||||
"duration_ms": result["duration_ms"],
|
||||
"total_entries": len(entries),
|
||||
"corrections_found": len(result["changes"]),
|
||||
}
|
||||
|
||||
|
||||
@router.post("/sessions/{session_id}/llm-review/apply")
|
||||
async def apply_llm_corrections(session_id: str, request: Request):
|
||||
"""Apply selected LLM corrections to vocab entries."""
|
||||
session = await get_session_db(session_id)
|
||||
if not session:
|
||||
raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
|
||||
|
||||
word_result = session.get("word_result")
|
||||
if not word_result:
|
||||
raise HTTPException(status_code=400, detail="No word result found")
|
||||
|
||||
llm_review = word_result.get("llm_review")
|
||||
if not llm_review:
|
||||
raise HTTPException(status_code=400, detail="No LLM review found — run /llm-review first")
|
||||
|
||||
body = await request.json()
|
||||
accepted_indices = set(body.get("accepted_indices", [])) # indices into changes[]
|
||||
|
||||
changes = llm_review.get("changes", [])
|
||||
entries = word_result.get("vocab_entries") or word_result.get("entries") or []
|
||||
|
||||
# Build a lookup: (row_index, field) -> new_value for accepted changes
|
||||
corrections = {}
|
||||
applied_count = 0
|
||||
for idx, change in enumerate(changes):
|
||||
if idx in accepted_indices:
|
||||
key = (change["row_index"], change["field"])
|
||||
corrections[key] = change["new"]
|
||||
applied_count += 1
|
||||
|
||||
# Apply corrections to entries
|
||||
for entry in entries:
|
||||
row_idx = entry.get("row_index", -1)
|
||||
for field_name in ("english", "german", "example"):
|
||||
key = (row_idx, field_name)
|
||||
if key in corrections:
|
||||
entry[field_name] = corrections[key]
|
||||
entry["llm_corrected"] = True
|
||||
|
||||
# Update word_result
|
||||
word_result["vocab_entries"] = entries
|
||||
word_result["entries"] = entries
|
||||
word_result["llm_review"]["applied_count"] = applied_count
|
||||
word_result["llm_review"]["applied_at"] = datetime.utcnow().isoformat()
|
||||
|
||||
await update_session_db(session_id, word_result=word_result)
|
||||
|
||||
if session_id in _cache:
|
||||
_cache[session_id]["word_result"] = word_result
|
||||
|
||||
logger.info(f"Applied {applied_count}/{len(changes)} LLM corrections for session {session_id}")
|
||||
|
||||
return {
|
||||
"session_id": session_id,
|
||||
"applied_count": applied_count,
|
||||
"total_changes": len(changes),
|
||||
}
|
||||
|
||||
|
||||
async def _get_rows_overlay(session_id: str) -> Response:
|
||||
"""Generate dewarped image with row bands drawn on it."""
|
||||
session = await get_session_db(session_id)
|
||||
|
||||
Reference in New Issue
Block a user