[split-required] Split 700-870 LOC files across all services

backend-lehrer (11 files):
- llm_gateway/routes/schools.py (867 → 5), recording_api.py (848 → 6)
- messenger_api.py (840 → 5), print_generator.py (824 → 5)
- unit_analytics_api.py (751 → 5), classroom/routes/context.py (726 → 4)
- llm_gateway/routes/edu_search_seeds.py (710 → 4)

klausur-service (12 files):
- ocr_labeling_api.py (845 → 4), metrics_db.py (833 → 4)
- legal_corpus_api.py (790 → 4), page_crop.py (758 → 3)
- mail/ai_service.py (747 → 4), github_crawler.py (767 → 3)
- trocr_service.py (730 → 4), full_compliance_pipeline.py (723 → 4)
- dsfa_rag_api.py (715 → 4), ocr_pipeline_auto.py (705 → 4)

website (6 pages):
- audit-checklist (867 → 8), content (806 → 6)
- screen-flow (790 → 4), scraper (789 → 5)
- zeugnisse (776 → 5), modules (745 → 4)

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

klausur-service/backend/ocr_labeling_models.py

@@ -0,0 +1,86 @@
+"""
+OCR Labeling - Pydantic Models and Constants
+
+Extracted from ocr_labeling_api.py to keep files under 500 LOC.
+"""
+
+import os
+from pydantic import BaseModel
+from typing import Optional, Dict
+from datetime import datetime
+
+
+# Local storage path (fallback if MinIO not available)
+LOCAL_STORAGE_PATH = os.getenv("OCR_STORAGE_PATH", "/app/ocr-labeling")
+
+
+# =============================================================================
+# Pydantic Models
+# =============================================================================
+
+class SessionCreate(BaseModel):
+    name: str
+    source_type: str = "klausur"  # klausur, handwriting_sample, scan
+    description: Optional[str] = None
+    ocr_model: Optional[str] = "llama3.2-vision:11b"
+
+
+class SessionResponse(BaseModel):
+    id: str
+    name: str
+    source_type: str
+    description: Optional[str]
+    ocr_model: Optional[str]
+    total_items: int
+    labeled_items: int
+    confirmed_items: int
+    corrected_items: int
+    skipped_items: int
+    created_at: datetime
+
+
+class ItemResponse(BaseModel):
+    id: str
+    session_id: str
+    session_name: str
+    image_path: str
+    image_url: Optional[str]
+    ocr_text: Optional[str]
+    ocr_confidence: Optional[float]
+    ground_truth: Optional[str]
+    status: str
+    metadata: Optional[Dict]
+    created_at: datetime
+
+
+class ConfirmRequest(BaseModel):
+    item_id: str
+    label_time_seconds: Optional[int] = None
+
+
+class CorrectRequest(BaseModel):
+    item_id: str
+    ground_truth: str
+    label_time_seconds: Optional[int] = None
+
+
+class SkipRequest(BaseModel):
+    item_id: str
+
+
+class ExportRequest(BaseModel):
+    export_format: str = "generic"  # generic, trocr, llama_vision
+    session_id: Optional[str] = None
+    batch_id: Optional[str] = None
+
+
+class StatsResponse(BaseModel):
+    total_sessions: Optional[int] = None
+    total_items: int
+    labeled_items: int
+    confirmed_items: int
+    corrected_items: int
+    pending_items: int
+    exportable_items: Optional[int] = None
+    accuracy_rate: float
+    avg_label_time_seconds: Optional[float] = None