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All services: admin-v2, studio-v2, website, ai-compliance-sdk, consent-service, klausur-service, voice-service, and infrastructure. Large PDFs and compiled binaries excluded via .gitignore.
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
"""
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Klausurkorrektur Module - Privacy-by-Design Exam Correction.
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DSGVO-compliant exam correction with QR-code based pseudonymization.
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No personal data is sent to the LLM.
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Architecture:
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- Pseudonymization via doc_token (128-bit UUID)
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- Teacher namespace isolation
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- Self-hosted LLM at SysEleven
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- Zero-knowledge identity mapping (encrypted client-side)
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"""
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from .db_models import (
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ExamSession, PseudonymizedDocument, QRBatchJob,
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SessionStatus, DocumentStatus,
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# Magic Onboarding
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OnboardingSession, DetectedStudent, ModuleLink,
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OnboardingStatus, ModuleLinkType
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)
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from .repository import KlausurRepository
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from .database import get_db, init_db
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# Services
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from .services.roster_parser import RosterParser, get_roster_parser
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from .services.school_resolver import SchoolResolver, get_school_resolver
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from .services.module_linker import ModuleLinker, get_module_linker
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__all__ = [
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# Models
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"ExamSession",
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"PseudonymizedDocument",
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"QRBatchJob",
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"SessionStatus",
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"DocumentStatus",
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# Magic Onboarding Models
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"OnboardingSession",
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"DetectedStudent",
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"ModuleLink",
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"OnboardingStatus",
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"ModuleLinkType",
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# Repository
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"KlausurRepository",
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# Database
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"get_db",
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"init_db",
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# Services
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"RosterParser",
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"get_roster_parser",
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"SchoolResolver",
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"get_school_resolver",
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"ModuleLinker",
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"get_module_linker",
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]
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