Restructure: Move 52 files into 7 domain packages

korrektur/ zeugnis/ admin/ compliance/ worksheet/ training/ metrics/
52 shims, relative imports, RAG untouched.

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

klausur-service/backend/training_models.py

@@ -1,118 +1,4 @@
-"""
-Training API — enums, request/response models, and in-memory state.
-"""
-
-import uuid
-from datetime import datetime
-from typing import Optional, List, Dict, Any
-from enum import Enum
-from dataclasses import dataclass, field
-from pydantic import BaseModel, Field
-
-
-# ============================================================================
-# ENUMS
-# ============================================================================
-
-class TrainingStatus(str, Enum):
-    QUEUED = "queued"
-    PREPARING = "preparing"
-    TRAINING = "training"
-    VALIDATING = "validating"
-    COMPLETED = "completed"
-    FAILED = "failed"
-    PAUSED = "paused"
-    CANCELLED = "cancelled"
-
-
-class ModelType(str, Enum):
-    ZEUGNIS = "zeugnis"
-    KLAUSUR = "klausur"
-    GENERAL = "general"
-
-
-# ============================================================================
-# REQUEST/RESPONSE MODELS
-# ============================================================================
-
-class TrainingConfig(BaseModel):
-    """Configuration for a training job."""
-    name: str = Field(..., description="Name for the training job")
-    model_type: ModelType = Field(ModelType.ZEUGNIS, description="Type of model to train")
-    bundeslaender: List[str] = Field(..., description="List of Bundesland codes to include")
-    batch_size: int = Field(16, ge=1, le=128)
-    learning_rate: float = Field(0.00005, ge=0.000001, le=0.1)
-    epochs: int = Field(10, ge=1, le=100)
-    warmup_steps: int = Field(500, ge=0, le=10000)
-    weight_decay: float = Field(0.01, ge=0, le=1)
-    gradient_accumulation: int = Field(4, ge=1, le=32)
-    mixed_precision: bool = Field(True, description="Use FP16 mixed precision training")
-
-
-class TrainingMetrics(BaseModel):
-    """Metrics from a training job."""
-    precision: float = 0.0
-    recall: float = 0.0
-    f1_score: float = 0.0
-    accuracy: float = 0.0
-    loss_history: List[float] = []
-    val_loss_history: List[float] = []
-
-
-class TrainingJob(BaseModel):
-    """A training job with full details."""
-    id: str
-    name: str
-    model_type: ModelType
-    status: TrainingStatus
-    progress: float
-    current_epoch: int
-    total_epochs: int
-    loss: float
-    val_loss: float
-    learning_rate: float
-    documents_processed: int
-    total_documents: int
-    started_at: Optional[datetime]
-    estimated_completion: Optional[datetime]
-    completed_at: Optional[datetime]
-    error_message: Optional[str]
-    metrics: TrainingMetrics
-    config: TrainingConfig
-
-
-class ModelVersion(BaseModel):
-    """A trained model version."""
-    id: str
-    job_id: str
-    version: str
-    model_type: ModelType
-    created_at: datetime
-    metrics: TrainingMetrics
-    is_active: bool
-    size_mb: float
-    bundeslaender: List[str]
-
-
-class DatasetStats(BaseModel):
-    """Statistics about the training dataset."""
-    total_documents: int
-    total_chunks: int
-    training_allowed: int
-    by_bundesland: Dict[str, int]
-    by_doc_type: Dict[str, int]
-
-
-# ============================================================================
-# IN-MEMORY STATE (Replace with database in production)
-# ============================================================================
-
-@dataclass
-class TrainingState:
-    """Global training state."""
-    jobs: Dict[str, dict] = field(default_factory=dict)
-    model_versions: Dict[str, dict] = field(default_factory=dict)
-    active_job_id: Optional[str] = None
-
-
-_state = TrainingState()
+# Backward-compat shim -- module moved to training/models.py
+import importlib as _importlib
+import sys as _sys
+_sys.modules[__name__] = _importlib.import_module("training.models")