fix: Restore all files lost during destructive rebase
A previous `git pull --rebase origin main` dropped 177 local commits,
losing 3400+ files across admin-v2, backend, studio-v2, website,
klausur-service, and many other services. The partial restore attempt
(660295e2) only recovered some files.
This commit restores all missing files from pre-rebase ref 98933f5e
while preserving post-rebase additions (night-scheduler, night-mode UI,
NightModeWidget dashboard integration).
Restored features include:
- AI Module Sidebar (FAB), OCR Labeling, OCR Compare
- GPU Dashboard, RAG Pipeline, Magic Help
- Klausur-Korrektur (8 files), Abitur-Archiv (5+ files)
- Companion, Zeugnisse-Crawler, Screen Flow
- Full backend, studio-v2, website, klausur-service
- All compliance SDKs, agent-core, voice-service
- CI/CD configs, documentation, scripts
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
377
backend/klausur/db_models.py
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377
backend/klausur/db_models.py
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"""
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SQLAlchemy Database Models for Klausurkorrektur Module.
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Privacy-by-Design: No personal data (student names) is stored in these models.
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Only pseudonymized doc_tokens are used to reference exam documents.
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"""
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from datetime import datetime
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from sqlalchemy import (
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Column, String, Integer, DateTime, JSON,
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Boolean, Text, Enum as SQLEnum, ForeignKey, LargeBinary
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)
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from sqlalchemy.orm import relationship
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import enum
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import uuid
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from .database import Base
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class SessionStatus(str, enum.Enum):
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"""Status of an exam correction session."""
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CREATED = "created" # Session created, awaiting uploads
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UPLOADING = "uploading" # Documents being uploaded
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PROCESSING = "processing" # OCR and AI correction in progress
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COMPLETED = "completed" # All documents processed
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ARCHIVED = "archived" # Session archived (data retention)
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DELETED = "deleted" # Soft delete
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class OnboardingStatus(str, enum.Enum):
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"""Status of a magic onboarding session."""
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ANALYZING = "analyzing" # Local LLM extracting headers
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CONFIRMING = "confirming" # User confirming detected data
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PROCESSING = "processing" # Cloud LLM correcting exams
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LINKING = "linking" # Creating module links
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COMPLETE = "complete" # Onboarding finished
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class ModuleLinkType(str, enum.Enum):
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"""Type of cross-module link."""
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NOTENBUCH = "notenbuch" # Link to grade book
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ELTERNABEND = "elternabend" # Link to parent meetings
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ZEUGNIS = "zeugnis" # Link to certificates
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CALENDAR = "calendar" # Link to calendar events
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KLASSENBUCH = "klassenbuch" # Link to class book
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class DocumentStatus(str, enum.Enum):
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"""Status of a single pseudonymized document."""
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UPLOADED = "uploaded" # Document uploaded, awaiting OCR
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OCR_PROCESSING = "ocr_processing" # OCR in progress
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OCR_COMPLETED = "ocr_completed" # OCR done, awaiting AI correction
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AI_PROCESSING = "ai_processing" # AI correction in progress
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COMPLETED = "completed" # Fully processed
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FAILED = "failed" # Processing failed
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class ExamSession(Base):
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"""
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Exam Correction Session.
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Groups multiple pseudonymized documents for a single exam correction task.
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No personal data is stored - teacher_id is the only identifying info.
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"""
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__tablename__ = 'klausur_sessions'
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# Primary Key
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id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
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# Teacher isolation (mandatory)
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teacher_id = Column(String(100), nullable=False, index=True)
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# Session metadata
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name = Column(String(200), nullable=False) # e.g., "Mathe 10a - Klausur 1"
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subject = Column(String(100), default="")
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class_name = Column(String(100), default="") # e.g., "10a"
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# Exam configuration
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total_points = Column(Integer, default=100)
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rubric = Column(Text, default="") # Bewertungskriterien
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questions = Column(JSON, default=list) # [{question, points, rubric}]
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# Status
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status = Column(
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SQLEnum(SessionStatus),
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default=SessionStatus.CREATED,
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nullable=False,
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index=True
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)
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# Statistics (anonymized)
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document_count = Column(Integer, default=0)
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processed_count = Column(Integer, default=0)
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# Encrypted identity map (only teacher can decrypt)
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# This is stored encrypted with teacher's password
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encrypted_identity_map = Column(LargeBinary, nullable=True)
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identity_map_iv = Column(String(64), nullable=True) # IV for AES decryption
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# Timestamps
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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completed_at = Column(DateTime, nullable=True)
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# Data retention: auto-delete after this date
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retention_until = Column(DateTime, nullable=True)
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# Magic Onboarding: Link to school class (optional)
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linked_school_class_id = Column(String(36), nullable=True)
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linked_subject_id = Column(String(36), nullable=True)
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# Relationship to documents
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documents = relationship(
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"PseudonymizedDocument",
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back_populates="session",
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cascade="all, delete-orphan"
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)
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def __repr__(self):
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return f"<ExamSession {self.id[:8]}: {self.name} ({self.status.value})>"
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class PseudonymizedDocument(Base):
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"""
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Pseudonymized Exam Document.
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PRIVACY DESIGN:
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- doc_token is a 128-bit random UUID, NOT derivable from student identity
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- No student name or personal info is stored here
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- Identity mapping is stored encrypted in ExamSession.encrypted_identity_map
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- The backend CANNOT de-pseudonymize documents
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Only the teacher (with their encryption key) can map doc_token -> student name.
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"""
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__tablename__ = 'klausur_documents'
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# Primary Key: The pseudonymization token
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doc_token = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
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# Session relationship
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session_id = Column(String(36), ForeignKey('klausur_sessions.id'), nullable=False, index=True)
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# Processing status
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status = Column(
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SQLEnum(DocumentStatus),
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default=DocumentStatus.UPLOADED,
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nullable=False,
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index=True
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)
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# Page info
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page_number = Column(Integer, default=1)
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total_pages = Column(Integer, default=1)
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# OCR result (redacted - no header/name visible)
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ocr_text = Column(Text, default="")
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ocr_confidence = Column(Integer, default=0) # 0-100
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# AI correction result (pseudonymized)
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ai_feedback = Column(Text, default="")
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ai_score = Column(Integer, nullable=True) # Points achieved
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ai_grade = Column(String(10), nullable=True) # e.g., "2+" or "B"
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ai_details = Column(JSON, default=dict) # Per-question scores
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# Processing metadata
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processing_started_at = Column(DateTime, nullable=True)
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processing_completed_at = Column(DateTime, nullable=True)
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processing_error = Column(Text, nullable=True)
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# Timestamps
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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# Relationship
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session = relationship("ExamSession", back_populates="documents")
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def __repr__(self):
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return f"<PseudonymizedDocument {self.doc_token[:8]} ({self.status.value})>"
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class QRBatchJob(Base):
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"""
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QR Code Generation Batch Job.
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Tracks generation of QR overlay sheets for printing.
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The generated PDF contains QR codes with doc_tokens.
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"""
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__tablename__ = 'klausur_qr_batches'
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id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
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# Session relationship
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session_id = Column(String(36), ForeignKey('klausur_sessions.id'), nullable=False, index=True)
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teacher_id = Column(String(100), nullable=False, index=True)
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# Batch info
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student_count = Column(Integer, nullable=False)
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generated_tokens = Column(JSON, default=list) # List of generated doc_tokens
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# Generated PDF (stored as path reference, not in DB)
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pdf_path = Column(String(500), nullable=True)
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# Timestamps
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created_at = Column(DateTime, default=datetime.utcnow)
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downloaded_at = Column(DateTime, nullable=True)
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def __repr__(self):
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return f"<QRBatchJob {self.id[:8]}: {self.student_count} students>"
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class OnboardingSession(Base):
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"""
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Magic Onboarding Session.
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Tracks the automatic class/student detection and setup process.
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Temporary data structure - merged into ExamSession after confirmation.
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"""
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__tablename__ = 'klausur_onboarding_sessions'
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id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
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# Links
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klausur_session_id = Column(String(36), ForeignKey('klausur_sessions.id'), nullable=True)
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teacher_id = Column(String(100), nullable=False, index=True)
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# Detected metadata (from local LLM)
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detected_class = Column(String(100), nullable=True)
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detected_subject = Column(String(100), nullable=True)
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detected_date = Column(DateTime, nullable=True)
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detected_student_count = Column(Integer, default=0)
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detection_confidence = Column(Integer, default=0) # 0-100
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# Confirmed data (after user review)
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confirmed_class = Column(String(100), nullable=True)
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confirmed_subject = Column(String(100), nullable=True)
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# Linked school entities (after confirmation)
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linked_school_id = Column(String(36), nullable=True)
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linked_class_id = Column(String(36), nullable=True)
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# School context
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bundesland = Column(String(50), nullable=True)
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schulform = Column(String(50), nullable=True)
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school_name = Column(String(200), nullable=True)
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# Status
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status = Column(
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SQLEnum(OnboardingStatus),
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default=OnboardingStatus.ANALYZING,
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nullable=False,
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index=True
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)
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# Progress tracking
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analysis_completed_at = Column(DateTime, nullable=True)
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confirmation_completed_at = Column(DateTime, nullable=True)
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processing_started_at = Column(DateTime, nullable=True)
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processing_completed_at = Column(DateTime, nullable=True)
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# Timestamps
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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# Relationships
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detected_students = relationship(
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"DetectedStudent",
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back_populates="onboarding_session",
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cascade="all, delete-orphan"
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)
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def __repr__(self):
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return f"<OnboardingSession {self.id[:8]}: {self.detected_class} ({self.status.value})>"
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class DetectedStudent(Base):
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"""
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Student detected during Magic Onboarding.
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Temporary storage for detected student data before confirmation.
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After confirmation, students are created in the School Service.
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"""
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__tablename__ = 'klausur_detected_students'
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id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
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# Onboarding session
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onboarding_session_id = Column(
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String(36),
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ForeignKey('klausur_onboarding_sessions.id'),
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nullable=False,
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index=True
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)
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# Detected data (from exam header)
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detected_first_name = Column(String(100), nullable=True)
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detected_last_name_hint = Column(String(100), nullable=True) # Partial, e.g. "M."
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# Confirmed data (after roster matching)
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confirmed_first_name = Column(String(100), nullable=True)
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confirmed_last_name = Column(String(100), nullable=True)
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# Matched to School Service student
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matched_student_id = Column(String(36), nullable=True)
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# Parent contact (extracted from roster)
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parent_email = Column(String(200), nullable=True)
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parent_phone = Column(String(50), nullable=True)
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# Link to pseudonymized document
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doc_token = Column(String(36), nullable=True)
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# Confidence
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confidence = Column(Integer, default=0) # 0-100
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# Timestamps
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created_at = Column(DateTime, default=datetime.utcnow)
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# Relationship
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onboarding_session = relationship("OnboardingSession", back_populates="detected_students")
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def __repr__(self):
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name = self.confirmed_first_name or self.detected_first_name or "?"
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return f"<DetectedStudent {self.id[:8]}: {name}>"
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class ModuleLink(Base):
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"""
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Cross-module link from Klausur to other BreakPilot modules.
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Tracks connections to: Notenbuch, Elternabend, Zeugnis, Calendar
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"""
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__tablename__ = 'klausur_module_links'
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id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
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# Source
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klausur_session_id = Column(
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String(36),
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ForeignKey('klausur_sessions.id'),
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nullable=False,
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index=True
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)
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# Link type
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link_type = Column(
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SQLEnum(ModuleLinkType),
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nullable=False,
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index=True
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)
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# Target
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target_module = Column(String(50), nullable=False) # school, calendar, etc.
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target_entity_id = Column(String(36), nullable=True)
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target_url = Column(String(500), nullable=True)
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# Link metadata
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link_metadata = Column(JSON, default=dict)
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# Timestamps
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created_at = Column(DateTime, default=datetime.utcnow)
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def __repr__(self):
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return f"<ModuleLink {self.id[:8]}: {self.link_type.value} -> {self.target_module}>"
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# Export all models
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__all__ = [
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"SessionStatus",
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"DocumentStatus",
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"OnboardingStatus",
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"ModuleLinkType",
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"ExamSession",
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"PseudonymizedDocument",
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"QRBatchJob",
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"OnboardingSession",
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"DetectedStudent",
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"ModuleLink",
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]
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Block a user