[split-required] Split 500-850 LOC files (batch 2)

backend-lehrer (10 files):
- game/database.py (785 → 5), correction_api.py (683 → 4)
- classroom_engine/antizipation.py (676 → 5)
- llm_gateway schools/edu_search already done in prior batch

klausur-service (12 files):
- orientation_crop_api.py (694 → 5), pdf_export.py (677 → 4)
- zeugnis_crawler.py (676 → 5), grid_editor_api.py (671 → 5)
- eh_templates.py (658 → 5), mail/api.py (651 → 5)
- qdrant_service.py (638 → 5), training_api.py (625 → 4)

website (6 pages):
- middleware (696 → 8), mail (733 → 6), consent (628 → 8)
- compliance/risks (622 → 5), export (502 → 5), brandbook (629 → 7)

studio-v2 (3 components):
- B2BMigrationWizard (848 → 3), CleanupPanel (765 → 2)
- dashboard-experimental (739 → 2)

admin-lehrer (4 files):
- uebersetzungen (769 → 4), manager (670 → 2)
- ChunkBrowserQA (675 → 6), dsfa/page (674 → 5)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend-lehrer/correction_helpers.py

@@ -0,0 +1,134 @@
+"""
+Correction API - Helper functions for grading, feedback, and OCR processing.
+"""
+
+import logging
+from typing import List, Dict
+
+from correction_models import AnswerEvaluation, CorrectionStatus, Correction
+
+logger = logging.getLogger(__name__)
+
+# FileProcessor requires OpenCV with libGL - make optional for CI
+try:
+    from services.file_processor import FileProcessor, ProcessingResult
+    _ocr_available = True
+except (ImportError, OSError):
+    FileProcessor = None  # type: ignore
+    ProcessingResult = None  # type: ignore
+    _ocr_available = False
+
+# PDF service requires WeasyPrint with system libraries - make optional for CI
+try:
+    from services.pdf_service import PDFService, CorrectionData, StudentInfo
+    _pdf_available = True
+except (ImportError, OSError):
+    PDFService = None  # type: ignore
+    CorrectionData = None  # type: ignore
+    StudentInfo = None  # type: ignore
+    _pdf_available = False
+
+
+# ============================================================================
+# In-Memory Storage (spaeter durch DB ersetzen)
+# ============================================================================
+
+corrections_store: Dict[str, Correction] = {}
+
+
+# ============================================================================
+# Helper Functions
+# ============================================================================
+
+def calculate_grade(percentage: float) -> str:
+    """Berechnet Note aus Prozent (deutsches System)."""
+    if percentage >= 92:
+        return "1"
+    elif percentage >= 81:
+        return "2"
+    elif percentage >= 67:
+        return "3"
+    elif percentage >= 50:
+        return "4"
+    elif percentage >= 30:
+        return "5"
+    else:
+        return "6"
+
+
+def generate_ai_feedback(
+    evaluations: List[AnswerEvaluation],
+    total_points: float,
+    max_points: float,
+    subject: str
+) -> str:
+    """Generiert KI-Feedback basierend auf Bewertung."""
+    # Ohne LLM: Einfaches Template-basiertes Feedback
+    percentage = (total_points / max_points * 100) if max_points > 0 else 0
+    correct_count = sum(1 for e in evaluations if e.is_correct)
+    total_count = len(evaluations)
+
+    if percentage >= 90:
+        intro = "Hervorragende Leistung!"
+    elif percentage >= 75:
+        intro = "Gute Arbeit!"
+    elif percentage >= 60:
+        intro = "Insgesamt eine solide Leistung."
+    elif percentage >= 50:
+        intro = "Die Arbeit zeigt Grundkenntnisse, aber es gibt Verbesserungsbedarf."
+    else:
+        intro = "Es sind deutliche Wissensluecken erkennbar."
+
+    # Finde Verbesserungsbereiche
+    weak_areas = [e for e in evaluations if not e.is_correct]
+    strengths = [e for e in evaluations if e.is_correct and e.confidence > 0.8]
+
+    feedback_parts = [intro]
+
+    if strengths:
+        feedback_parts.append(
+            f"Besonders gut geloest: Aufgabe(n) {', '.join(str(s.question_number) for s in strengths[:3])}."
+        )
+
+    if weak_areas:
+        feedback_parts.append(
+            f"Uebungsbedarf bei: Aufgabe(n) {', '.join(str(w.question_number) for w in weak_areas[:3])}."
+        )
+
+    feedback_parts.append(
+        f"Ergebnis: {correct_count} von {total_count} Aufgaben korrekt ({percentage:.1f}%)."
+    )
+
+    return " ".join(feedback_parts)
+
+
+async def process_ocr(correction_id: str, file_path: str):
+    """Background Task fuer OCR-Verarbeitung."""
+    from datetime import datetime
+
+    correction = corrections_store.get(correction_id)
+    if not correction:
+        return
+
+    try:
+        correction.status = CorrectionStatus.PROCESSING
+        corrections_store[correction_id] = correction
+
+        # OCR durchfuehren
+        processor = FileProcessor()
+        result = processor.process_file(file_path)
+
+        if result.success and result.text:
+            correction.extracted_text = result.text
+            correction.status = CorrectionStatus.OCR_COMPLETE
+        else:
+            correction.status = CorrectionStatus.ERROR
+
+        correction.updated_at = datetime.utcnow()
+        corrections_store[correction_id] = correction
+
+    except Exception as e:
+        logger.error(f"OCR error for {correction_id}: {e}")
+        correction.status = CorrectionStatus.ERROR
+        correction.updated_at = datetime.utcnow()
+        corrections_store[correction_id] = correction