Restructure: Move 43 files into 8 domain packages (backend-lehrer)

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

backend-lehrer/correction_helpers.py

@@ -1,134 +1,4 @@
-"""
-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
+# Backward-compat shim -- module moved to correction/helpers.py
+import importlib as _importlib
+import sys as _sys
+_sys.modules[__name__] = _importlib.import_module("correction.helpers")