b4613e26f3
backend-lehrer (10 files): - game/database.py (785 → 5), correction_api.py (683 → 4) - classroom_engine/antizipation.py (676 → 5) - llm_gateway schools/edu_search already done in prior batch klausur-service (12 files): - orientation_crop_api.py (694 → 5), pdf_export.py (677 → 4) - zeugnis_crawler.py (676 → 5), grid_editor_api.py (671 → 5) - eh_templates.py (658 → 5), mail/api.py (651 → 5) - qdrant_service.py (638 → 5), training_api.py (625 → 4) website (6 pages): - middleware (696 → 8), mail (733 → 6), consent (628 → 8) - compliance/risks (622 → 5), export (502 → 5), brandbook (629 → 7) studio-v2 (3 components): - B2BMigrationWizard (848 → 3), CleanupPanel (765 → 2) - dashboard-experimental (739 → 2) admin-lehrer (4 files): - uebersetzungen (769 → 4), manager (670 → 2) - ChunkBrowserQA (675 → 6), dsfa/page (674 → 5) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
137 lines
4.2 KiB
Python
137 lines
4.2 KiB
Python
"""
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Antizipation Engine - SuggestionGenerator.
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Main class that collects signals, evaluates rules, and generates
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prioritized suggestions for teachers.
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"""
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from typing import List, Dict, Any
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from .antizipation_models import Signals, ActiveContext, ContextType
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from .antizipation_collector import SignalCollector
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from .antizipation_rules import RuleEngine
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class SuggestionGenerator:
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"""
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Hauptklasse die Signale sammelt, Regeln evaluiert und
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Vorschlaege generiert.
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"""
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def __init__(self, db_session=None):
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self.collector = SignalCollector(db_session)
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self.rule_engine = RuleEngine()
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def generate(self, teacher_id: str, limit: int = 5) -> Dict[str, Any]:
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"""
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Generiert Vorschlaege fuer einen Lehrer.
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Returns:
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{
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"active_contexts": [...],
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"suggestions": [...],
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"signals_summary": {...}
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}
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"""
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# 1. Signale sammeln
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signals = self.collector.collect(teacher_id)
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# 2. Regeln evaluieren
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all_suggestions = self.rule_engine.evaluate(signals)
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# 3. Aktive Kontexte bestimmen
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active_contexts = self._determine_active_contexts(signals)
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# 4. Top N Vorschlaege
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top_suggestions = all_suggestions[:limit]
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return {
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"active_contexts": [
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{
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"id": ctx.id,
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"type": ctx.context_type.value,
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"label": ctx.label,
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}
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for ctx in active_contexts
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],
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"suggestions": [
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{
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"id": s.id,
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"title": s.title,
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"description": s.description,
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"tone": s.tone.value,
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"badge": s.badge,
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"priority": s.priority,
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"icon": s.icon,
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"action_url": s.action_url,
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}
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for s in top_suggestions
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],
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"signals_summary": {
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"macro_phase": signals.macro_phase,
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"current_week": signals.current_week,
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"has_classes": signals.has_classes,
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"exams_soon": len(signals.exams_in_7_days),
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"routines_today": len(signals.routines_today),
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},
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"total_suggestions": len(all_suggestions),
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}
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def _determine_active_contexts(self, signals: Signals) -> List[ActiveContext]:
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"""Bestimmt die aktiven Kontexte basierend auf Signalen."""
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contexts = []
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# Event-Kontexte
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if signals.exams_in_7_days:
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contexts.append(ActiveContext(
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id="EXAM_IN_7_DAYS",
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context_type=ContextType.EVENT_WINDOW,
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label="Klausur in 7 Tagen",
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))
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if signals.trips_in_30_days:
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contexts.append(ActiveContext(
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id="TRIP_UPCOMING",
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context_type=ContextType.EVENT_WINDOW,
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label="Klassenfahrt geplant",
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))
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# Routine-Kontexte
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if signals.has_conference_today:
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contexts.append(ActiveContext(
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id="CONFERENCE_TODAY",
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context_type=ContextType.ROUTINE,
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label="Konferenz heute",
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))
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# Zeit-Kontexte
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if signals.is_weekend:
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contexts.append(ActiveContext(
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id="WEEKEND",
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context_type=ContextType.TIME,
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label="Wochenende",
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))
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if signals.is_before_holidays:
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contexts.append(ActiveContext(
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id="BEFORE_HOLIDAYS",
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context_type=ContextType.TIME,
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label="Vor den Ferien",
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))
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# Phase-Kontexte
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if signals.macro_phase == "onboarding":
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contexts.append(ActiveContext(
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id="ONBOARDING",
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context_type=ContextType.PHASE,
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label="Einrichtung",
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))
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elif signals.macro_phase in ("halbjahresabschluss", "jahresabschluss"):
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contexts.append(ActiveContext(
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id="GRADE_PERIOD",
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context_type=ContextType.PHASE,
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label="Notenphase",
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))
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return contexts
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