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
364
ai-content-generator/app/services/claude_service.py
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364
ai-content-generator/app/services/claude_service.py
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"""
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Claude Service
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Integration mit Claude API für Content-Generierung
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"""
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import os
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from typing import List, Dict, Any, Optional
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from anthropic import Anthropic
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class ClaudeService:
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"""Claude API Service"""
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def __init__(self):
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self.api_key = os.getenv("ANTHROPIC_API_KEY")
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self.client = Anthropic(api_key=self.api_key) if self.api_key else None
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self.model = "claude-sonnet-4-5-20251101" # Latest model
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def is_configured(self) -> bool:
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"""Check if API key is configured"""
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return self.client is not None
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async def generate_content(
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self,
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prompt: str,
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system_prompt: Optional[str] = None,
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max_tokens: int = 4000,
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temperature: float = 1.0
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) -> str:
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"""
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Generate content with Claude
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Args:
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prompt: User prompt
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system_prompt: System prompt (optional)
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max_tokens: Maximum tokens to generate
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temperature: Sampling temperature
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Returns:
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Generated text
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"""
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if not self.client:
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raise ValueError("Claude API not configured. Set ANTHROPIC_API_KEY environment variable.")
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messages = [{"role": "user", "content": prompt}]
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kwargs = {
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"model": self.model,
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"max_tokens": max_tokens,
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"temperature": temperature,
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"messages": messages
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}
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if system_prompt:
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kwargs["system"] = system_prompt
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response = self.client.messages.create(**kwargs)
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return response.content[0].text
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async def generate_quiz_questions(
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self,
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topic: str,
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materials: List[Dict[str, Any]],
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target_grade: str,
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num_questions: int = 10
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) -> List[Dict[str, Any]]:
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"""Generate Quiz questions"""
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material_text = self._format_materials(materials)
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prompt = f"""Erstelle {num_questions} Multiple-Choice-Fragen zum Thema "{topic}" für Klassenstufe {target_grade}.
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Materialien:
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{material_text}
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Erstelle Fragen die:
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1. Das Verständnis testen
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2. Auf den Materialien basieren
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3. Altersgerecht sind
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4. 4 Antwortmöglichkeiten haben (1 richtig, 3 falsch)
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Formatiere die Ausgabe als JSON-Array:
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[
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{{
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"question": "Frage text?",
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"options": ["Option A", "Option B", "Option C", "Option D"],
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"correct_answer": 0,
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"explanation": "Erklärung warum die Antwort richtig ist"
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}}
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]
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Nur das JSON-Array zurückgeben, keine zusätzlichen Texte."""
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response = await self.generate_content(
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prompt=prompt,
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system_prompt="Du bist ein pädagogischer Experte der Quizfragen erstellt."
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)
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# Parse JSON
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import json
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try:
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questions = json.loads(response)
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return questions
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except json.JSONDecodeError:
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# Try to extract JSON from response
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import re
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json_match = re.search(r'\[.*\]', response, re.DOTALL)
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if json_match:
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questions = json.loads(json_match.group())
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return questions
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raise ValueError("Could not parse quiz questions from Claude response")
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async def generate_flashcards(
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self,
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topic: str,
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materials: List[Dict[str, Any]],
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target_grade: str,
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num_cards: int = 15
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) -> List[Dict[str, str]]:
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"""Generate Flashcards"""
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material_text = self._format_materials(materials)
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prompt = f"""Erstelle {num_cards} Lernkarten (Flashcards) zum Thema "{topic}" für Klassenstufe {target_grade}.
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Materialien:
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{material_text}
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Erstelle Karten die:
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1. Wichtige Begriffe und Konzepte abdecken
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2. Kurz und prägnant sind
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3. Zum Wiederholen geeignet sind
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Formatiere die Ausgabe als JSON-Array:
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[
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{{
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"front": "Begriff oder Frage",
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"back": "Definition oder Antwort"
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}}
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]
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Nur das JSON-Array zurückgeben."""
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response = await self.generate_content(
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prompt=prompt,
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system_prompt="Du bist ein Experte für Lernkarten-Design."
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)
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import json
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import re
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json_match = re.search(r'\[.*\]', response, re.DOTALL)
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if json_match:
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return json.loads(json_match.group())
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return json.loads(response)
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async def generate_fill_blanks_text(
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self,
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topic: str,
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materials: List[Dict[str, Any]],
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target_grade: str
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) -> Dict[str, Any]:
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"""Generate Fill-in-the-Blanks exercise"""
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material_text = self._format_materials(materials)
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prompt = f"""Erstelle einen Lückentext zum Thema "{topic}" für Klassenstufe {target_grade}.
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Materialien:
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{material_text}
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Erstelle einen Text mit 10-15 Lücken. Markiere Lücken mit *Wort*.
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Formatiere als JSON:
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{{
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"title": "Titel des Lückentexts",
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"text": "Der Text mit *Lücken* markiert...",
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"hints": "Hilfreiche Hinweise"
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}}
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Nur JSON zurückgeben."""
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response = await self.generate_content(
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prompt=prompt,
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system_prompt="Du bist ein Experte für Lückentexte."
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)
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import json
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import re
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json_match = re.search(r'\{.*\}', response, re.DOTALL)
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if json_match:
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return json.loads(json_match.group())
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return json.loads(response)
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async def generate_drag_drop_exercise(
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self,
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topic: str,
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materials: List[Dict[str, Any]],
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target_grade: str
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) -> Dict[str, Any]:
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"""Generate Drag-and-Drop exercise"""
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material_text = self._format_materials(materials)
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prompt = f"""Erstelle eine Drag-and-Drop Zuordnungsaufgabe zum Thema "{topic}" für Klassenstufe {target_grade}.
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Materialien:
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{material_text}
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Erstelle 3-4 Kategorien (Zonen) und 8-12 Elemente zum Zuordnen.
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Formatiere als JSON:
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{{
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"title": "Titel der Aufgabe",
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"question": "Aufgabenstellung",
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"zones": [
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{{ "id": 1, "name": "Kategorie 1" }},
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{{ "id": 2, "name": "Kategorie 2" }}
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],
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"draggables": [
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{{ "id": 1, "text": "Element 1", "correctZoneId": 1 }},
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{{ "id": 2, "text": "Element 2", "correctZoneId": 2 }}
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]
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}}
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Nur JSON zurückgeben."""
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response = await self.generate_content(
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prompt=prompt,
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system_prompt="Du bist ein Experte für interaktive Lernaufgaben."
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)
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import json
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import re
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json_match = re.search(r'\{.*\}', response, re.DOTALL)
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if json_match:
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return json.loads(json_match.group())
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return json.loads(response)
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async def generate_memory_pairs(
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self,
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topic: str,
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materials: List[Dict[str, Any]],
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target_grade: str,
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num_pairs: int = 8
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) -> List[Dict[str, str]]:
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"""Generate Memory Game pairs"""
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material_text = self._format_materials(materials)
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prompt = f"""Erstelle {num_pairs} Memory-Paare zum Thema "{topic}" für Klassenstufe {target_grade}.
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Materialien:
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{material_text}
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Jedes Paar besteht aus zwei zusammengehörigen Begriffen/Konzepten.
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Formatiere als JSON-Array:
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[
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{{ "card1": "Begriff 1", "card2": "Zugehöriger Begriff" }}
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]
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Nur JSON zurückgeben."""
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response = await self.generate_content(
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prompt=prompt,
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system_prompt="Du bist ein Experte für Memory-Spiele."
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)
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import json
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import re
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json_match = re.search(r'\[.*\]', response, re.DOTALL)
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if json_match:
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return json.loads(json_match.group())
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return json.loads(response)
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async def generate_timeline_events(
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self,
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topic: str,
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materials: List[Dict[str, Any]],
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target_grade: str
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) -> List[Dict[str, Any]]:
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"""Generate Timeline events"""
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material_text = self._format_materials(materials)
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prompt = f"""Erstelle eine Timeline mit 5-8 Ereignissen zum Thema "{topic}" für Klassenstufe {target_grade}.
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Materialien:
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{material_text}
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Formatiere als JSON-Array:
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[
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{{
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"year": "Jahr oder Zeitpunkt",
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"title": "Ereignis Titel",
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"description": "Kurze Beschreibung"
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}}
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]
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Nur JSON zurückgeben."""
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response = await self.generate_content(
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prompt=prompt,
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system_prompt="Du bist ein Experte für chronologische Darstellungen."
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)
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import json
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import re
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json_match = re.search(r'\[.*\]', response, re.DOTALL)
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if json_match:
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return json.loads(json_match.group())
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return json.loads(response)
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async def generate_presentation_slides(
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self,
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topic: str,
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materials: List[Dict[str, Any]],
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target_grade: str,
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num_slides: int = 5
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) -> List[Dict[str, str]]:
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"""Generate Presentation slides"""
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material_text = self._format_materials(materials)
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prompt = f"""Erstelle {num_slides} Präsentationsfolien zum Thema "{topic}" für Klassenstufe {target_grade}.
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Materialien:
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{material_text}
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Formatiere als JSON-Array:
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[
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{{
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"title": "Folien Titel",
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"content": "Folien Inhalt (2-4 Sätze)",
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"backgroundColor": "#ffffff"
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}}
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]
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Nur JSON zurückgeben."""
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response = await self.generate_content(
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prompt=prompt,
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system_prompt="Du bist ein Experte für Präsentationen."
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)
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import json
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import re
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json_match = re.search(r'\[.*\]', response, re.DOTALL)
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if json_match:
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return json.loads(json_match.group())
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return json.loads(response)
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def _format_materials(self, materials: List[Dict[str, Any]]) -> str:
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"""Format materials for prompt"""
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if not materials:
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return "Keine Materialien vorhanden."
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formatted = []
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for i, material in enumerate(materials, 1):
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formatted.append(f"Material {i} ({material.get('type', 'unknown')}):")
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formatted.append(material.get('content', '')[:2000]) # Limit content
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formatted.append("")
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return "\n".join(formatted)
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