21a844cb8a
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>
278 lines
8.5 KiB
Python
278 lines
8.5 KiB
Python
"""
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Multiple Choice Generator - Erstellt MC-Fragen aus Quelltexten.
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Verwendet LLM (Claude/Ollama) zur Generierung von:
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- Multiple-Choice-Fragen mit 4 Antwortmöglichkeiten
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- Unterschiedliche Schwierigkeitsgrade
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- Erklärungen für falsche Antworten
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"""
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import logging
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import json
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from typing import List, Dict, Any, Optional
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from dataclasses import dataclass
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from enum import Enum
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logger = logging.getLogger(__name__)
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class Difficulty(str, Enum):
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"""Schwierigkeitsgrade."""
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EASY = "easy"
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MEDIUM = "medium"
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HARD = "hard"
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@dataclass
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class MCOption:
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"""Eine Antwortmöglichkeit."""
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text: str
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is_correct: bool
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explanation: Optional[str] = None
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@dataclass
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class MCQuestion:
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"""Eine Multiple-Choice-Frage."""
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question: str
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options: List[MCOption]
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difficulty: Difficulty
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topic: Optional[str] = None
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hint: Optional[str] = None
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class MultipleChoiceGenerator:
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"""
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Generiert Multiple-Choice-Fragen aus Quelltexten.
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Verwendet ein LLM zur intelligenten Fragengenerierung.
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"""
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def __init__(self, llm_client=None):
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"""
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Initialisiert den Generator.
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Args:
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llm_client: Optional - LLM-Client für Generierung.
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Falls nicht angegeben, wird ein Mock verwendet.
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"""
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self.llm_client = llm_client
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logger.info("MultipleChoiceGenerator initialized")
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def generate(
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self,
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source_text: str,
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num_questions: int = 5,
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difficulty: Difficulty = Difficulty.MEDIUM,
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subject: Optional[str] = None,
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grade_level: Optional[str] = None
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) -> List[MCQuestion]:
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"""
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Generiert Multiple-Choice-Fragen aus einem Quelltext.
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Args:
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source_text: Der Text, aus dem Fragen generiert werden
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num_questions: Anzahl der zu generierenden Fragen
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difficulty: Schwierigkeitsgrad
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subject: Fach (z.B. "Biologie", "Geschichte")
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grade_level: Klassenstufe (z.B. "7")
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Returns:
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Liste von MCQuestion-Objekten
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"""
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logger.info(f"Generating {num_questions} MC questions (difficulty: {difficulty})")
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if not source_text or len(source_text.strip()) < 50:
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logger.warning("Source text too short for meaningful questions")
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return []
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if self.llm_client:
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return self._generate_with_llm(
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source_text, num_questions, difficulty, subject, grade_level
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)
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else:
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return self._generate_mock(
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source_text, num_questions, difficulty
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)
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def _generate_with_llm(
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self,
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source_text: str,
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num_questions: int,
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difficulty: Difficulty,
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subject: Optional[str],
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grade_level: Optional[str]
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) -> List[MCQuestion]:
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"""Generiert Fragen mit einem LLM."""
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difficulty_desc = {
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Difficulty.EASY: "einfach (Faktenwissen)",
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Difficulty.MEDIUM: "mittel (Verständnis)",
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Difficulty.HARD: "schwer (Anwendung und Analyse)"
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}
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prompt = f"""
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Erstelle {num_questions} Multiple-Choice-Fragen auf Deutsch basierend auf folgendem Text.
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Schwierigkeitsgrad: {difficulty_desc[difficulty]}
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{f'Fach: {subject}' if subject else ''}
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{f'Klassenstufe: {grade_level}' if grade_level else ''}
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Text:
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{source_text}
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Erstelle für jede Frage:
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- Eine klare Frage
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- 4 Antwortmöglichkeiten (genau eine richtig)
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- Eine kurze Erklärung, warum die richtigen Antwort richtig ist
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- Einen optionalen Hinweis
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Antworte im folgenden JSON-Format:
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{{
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"questions": [
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{{
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"question": "Die Frage...",
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"options": [
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{{"text": "Antwort A", "is_correct": false, "explanation": "Warum falsch"}},
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{{"text": "Antwort B", "is_correct": true, "explanation": "Warum richtig"}},
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{{"text": "Antwort C", "is_correct": false, "explanation": "Warum falsch"}},
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{{"text": "Antwort D", "is_correct": false, "explanation": "Warum falsch"}}
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],
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"topic": "Thema der Frage",
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"hint": "Optionaler Hinweis"
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}}
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]
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}}
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"""
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try:
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response = self.llm_client.generate(prompt)
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data = json.loads(response)
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return self._parse_llm_response(data, difficulty)
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except Exception as e:
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logger.error(f"Error generating with LLM: {e}")
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return self._generate_mock(source_text, num_questions, difficulty)
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def _parse_llm_response(
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self,
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data: Dict[str, Any],
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difficulty: Difficulty
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) -> List[MCQuestion]:
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"""Parst die LLM-Antwort zu MCQuestion-Objekten."""
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questions = []
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for q_data in data.get("questions", []):
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options = [
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MCOption(
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text=opt["text"],
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is_correct=opt.get("is_correct", False),
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explanation=opt.get("explanation")
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)
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for opt in q_data.get("options", [])
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]
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question = MCQuestion(
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question=q_data.get("question", ""),
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options=options,
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difficulty=difficulty,
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topic=q_data.get("topic"),
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hint=q_data.get("hint")
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)
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questions.append(question)
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return questions
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def _generate_mock(
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self,
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source_text: str,
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num_questions: int,
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difficulty: Difficulty
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) -> List[MCQuestion]:
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"""Generiert Mock-Fragen für Tests/Demo."""
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logger.info("Using mock generator (no LLM client)")
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# Extrahiere einige Schlüsselwörter aus dem Text
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words = source_text.split()
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keywords = [w for w in words if len(w) > 5][:10]
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questions = []
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for i in range(min(num_questions, 5)):
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keyword = keywords[i] if i < len(keywords) else f"Begriff {i+1}"
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question = MCQuestion(
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question=f"Was beschreibt '{keyword}' im Kontext des Textes am besten?",
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options=[
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MCOption(text=f"Definition A von {keyword}", is_correct=True,
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explanation="Dies ist die korrekte Definition."),
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MCOption(text=f"Falsche Definition B", is_correct=False,
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explanation="Diese Definition passt nicht."),
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MCOption(text=f"Falsche Definition C", is_correct=False,
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explanation="Diese Definition ist unvollständig."),
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MCOption(text=f"Falsche Definition D", is_correct=False,
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explanation="Diese Definition ist irreführend."),
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],
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difficulty=difficulty,
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topic="Allgemein",
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hint=f"Denke an die Bedeutung von '{keyword}'."
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)
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questions.append(question)
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return questions
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def to_h5p_format(self, questions: List[MCQuestion]) -> Dict[str, Any]:
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"""
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Konvertiert Fragen ins H5P-Format für Multiple Choice.
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Args:
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questions: Liste von MCQuestion-Objekten
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Returns:
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H5P-kompatibles Dict
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"""
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h5p_questions = []
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for q in questions:
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answers = []
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for opt in q.options:
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answers.append({
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"text": opt.text,
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"correct": opt.is_correct,
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"tpiMessage": opt.explanation or ""
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})
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h5p_questions.append({
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"question": q.question,
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"answers": answers,
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"tip": q.hint or ""
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})
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return {
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"library": "H5P.MultiChoice",
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"params": {
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"questions": h5p_questions,
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"behaviour": {
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"enableRetry": True,
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"enableSolutionsButton": True,
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"singleAnswer": True
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}
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}
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}
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def to_dict(self, questions: List[MCQuestion]) -> List[Dict[str, Any]]:
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"""Konvertiert Fragen zu Dictionary-Format."""
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return [
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{
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"question": q.question,
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"options": [
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{
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"text": opt.text,
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"is_correct": opt.is_correct,
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"explanation": opt.explanation
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}
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for opt in q.options
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],
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"difficulty": q.difficulty.value,
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"topic": q.topic,
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"hint": q.hint
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}
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for q in questions
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]
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