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
312
backend/ai_processor/generators/cloze.py
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312
backend/ai_processor/generators/cloze.py
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"""
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AI Processor - Cloze Text Generator
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Generate cloze (fill-in-the-blank) texts from worksheet analysis.
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"""
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from pathlib import Path
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import json
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import logging
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import os
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import requests
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from ..config import VISION_API, BEREINIGT_DIR, get_openai_api_key
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logger = logging.getLogger(__name__)
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# Language codes to names
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LANGUAGE_NAMES = {
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"tr": "Tuerkisch",
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"ar": "Arabisch",
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"ru": "Russisch",
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"en": "Englisch",
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"fr": "Franzoesisch",
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"es": "Spanisch",
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"pl": "Polnisch",
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"uk": "Ukrainisch",
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}
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def _generate_cloze_with_openai(analysis_data: dict, target_language: str = "tr") -> dict:
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"""
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Generate cloze texts based on worksheet analysis.
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Important didactic requirements:
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- Multiple meaningful gaps per sentence (not just one!)
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- Difficulty level matches the original
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- Translation with the same gaps
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Args:
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analysis_data: The analysis JSON of the worksheet
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target_language: Target language for translation (default: "tr" for Turkish)
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Returns:
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Dict with cloze_items and metadata
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"""
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api_key = get_openai_api_key()
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title = analysis_data.get("title") or "Arbeitsblatt"
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subject = analysis_data.get("subject") or "Allgemein"
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grade_level = analysis_data.get("grade_level") or "unbekannt"
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canonical_text = analysis_data.get("canonical_text") or ""
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printed_blocks = analysis_data.get("printed_blocks") or []
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content_parts = []
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if canonical_text:
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content_parts.append(canonical_text)
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for block in printed_blocks:
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text = block.get("text", "").strip()
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if text and text not in content_parts:
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content_parts.append(text)
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worksheet_content = "\n\n".join(content_parts)
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if not worksheet_content.strip():
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logger.warning("Kein Textinhalt fuer Lueckentext-Generierung gefunden")
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return {"cloze_items": [], "metadata": {"error": "Kein Textinhalt gefunden"}}
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target_lang_name = LANGUAGE_NAMES.get(target_language, "Tuerkisch")
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url = "https://api.openai.com/v1/chat/completions"
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headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
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system_prompt = f"""Du bist ein erfahrener Paedagoge, der Lueckentexte fuer Schueler erstellt.
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WICHTIGE REGELN FUER LUECKENTEXTE:
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1. MEHRERE LUECKEN PRO SATZ:
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- Erstelle IMMER mehrere sinnvolle Luecken pro Satz
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- Beispiel: "Ich habe gestern meine Hausaufgaben gemacht."
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→ Luecken: "habe" UND "gemacht" (nicht nur eine!)
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2. SCHWIERIGKEITSGRAD:
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- Niveau muss exakt "{grade_level}" entsprechen
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3. SINNVOLLE LUECKENWOERTER:
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- Verben (konjugiert)
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- Wichtige Nomen
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- Adjektive
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- KEINE Artikel oder Praepositionen allein
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4. UEBERSETZUNG:
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- Uebersetze den VOLLSTAENDIGEN Satz auf {target_lang_name}
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- Die GLEICHEN Woerter muessen als Luecken markiert sein
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5. AUSGABE: Nur gueltiges JSON, kein Markdown."""
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user_prompt = f"""Erstelle Lueckentexte aus diesem Arbeitsblatt:
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TITEL: {title}
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FACH: {subject}
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KLASSENSTUFE: {grade_level}
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TEXT:
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{worksheet_content}
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Erstelle 5-8 Saetze mit Luecken. Gib das Ergebnis als JSON zurueck:
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{{
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"cloze_items": [
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{{
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"id": "c1",
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"original_sentence": "Der vollstaendige Originalsatz ohne Luecken",
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"sentence_with_gaps": "Der Satz mit ___ fuer jede Luecke",
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"gaps": [
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{{
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"id": "g1",
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"word": "das fehlende Wort",
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"position": 0,
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"hint": "optionaler Hinweis"
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}}
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],
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"translation": {{
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"language": "{target_language}",
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"language_name": "{target_lang_name}",
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"full_sentence": "Vollstaendige Uebersetzung",
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"sentence_with_gaps": "Uebersetzung mit ___ an gleichen Stellen"
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}}
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}}
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],
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"metadata": {{
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"subject": "{subject}",
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"grade_level": "{grade_level}",
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"source_title": "{title}",
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"target_language": "{target_language}",
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"total_gaps": 0
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}}
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}}
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WICHTIG:
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- Jeder Satz MUSS mindestens 2 Luecken haben!
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- Position ist der Index des Wortes im Satz (0-basiert)"""
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payload = {
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"model": "gpt-4o-mini",
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"response_format": {"type": "json_object"},
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"max_tokens": 3000,
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"temperature": 0.7,
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}
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response = requests.post(url, headers=headers, json=payload)
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response.raise_for_status()
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data = response.json()
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try:
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content = data["choices"][0]["message"]["content"]
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cloze_data = json.loads(content)
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except (KeyError, json.JSONDecodeError) as e:
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raise RuntimeError(f"Fehler bei Lueckentext-Generierung: {e}")
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# Calculate total number of gaps
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total_gaps = sum(len(item.get("gaps", [])) for item in cloze_data.get("cloze_items", []))
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if "metadata" in cloze_data:
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cloze_data["metadata"]["total_gaps"] = total_gaps
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return cloze_data
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def _generate_cloze_with_claude(analysis_data: dict, target_language: str = "tr") -> dict:
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"""Generate cloze texts with Claude API."""
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import anthropic
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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raise RuntimeError("ANTHROPIC_API_KEY ist nicht gesetzt.")
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client = anthropic.Anthropic(api_key=api_key)
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title = analysis_data.get("title") or "Arbeitsblatt"
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subject = analysis_data.get("subject") or "Allgemein"
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grade_level = analysis_data.get("grade_level") or "unbekannt"
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canonical_text = analysis_data.get("canonical_text") or ""
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printed_blocks = analysis_data.get("printed_blocks") or []
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content_parts = []
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if canonical_text:
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content_parts.append(canonical_text)
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for block in printed_blocks:
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text = block.get("text", "").strip()
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if text and text not in content_parts:
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content_parts.append(text)
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worksheet_content = "\n\n".join(content_parts)
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if not worksheet_content.strip():
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return {"cloze_items": [], "metadata": {"error": "Kein Textinhalt gefunden"}}
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target_lang_name = LANGUAGE_NAMES.get(target_language, "Tuerkisch")
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prompt = f"""Erstelle Lueckentexte aus diesem Arbeitsblatt.
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WICHTIGE REGELN:
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1. MEHRERE LUECKEN PRO SATZ (mindestens 2!)
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Beispiel: "Ich habe gestern Hausaufgaben gemacht" → Luecken: "habe" UND "gemacht"
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2. Schwierigkeitsgrad: exakt "{grade_level}"
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3. Uebersetzung auf {target_lang_name} mit gleichen Luecken
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TITEL: {title}
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FACH: {subject}
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KLASSENSTUFE: {grade_level}
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TEXT:
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{worksheet_content}
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Antworte NUR mit diesem JSON (5-8 Saetze):
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{{
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"cloze_items": [
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{{
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"id": "c1",
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"original_sentence": "Vollstaendiger Satz",
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"sentence_with_gaps": "Satz mit ___ fuer Luecken",
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"gaps": [
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{{"id": "g1", "word": "Lueckenwort", "position": 0, "hint": "Hinweis"}}
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],
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"translation": {{
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"language": "{target_language}",
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"language_name": "{target_lang_name}",
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"full_sentence": "Uebersetzung",
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"sentence_with_gaps": "Uebersetzung mit ___"
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}}
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}}
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],
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"metadata": {{
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"subject": "{subject}",
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"grade_level": "{grade_level}",
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"source_title": "{title}",
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"target_language": "{target_language}",
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"total_gaps": 0
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}}
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}}"""
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message = client.messages.create(
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model="claude-3-5-sonnet-20241022",
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max_tokens=3000,
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messages=[{"role": "user", "content": prompt}]
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)
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content = message.content[0].text
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try:
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if "```json" in content:
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content = content.split("```json")[1].split("```")[0]
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elif "```" in content:
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content = content.split("```")[1].split("```")[0]
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cloze_data = json.loads(content.strip())
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except json.JSONDecodeError as e:
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raise RuntimeError(f"Claude hat ungueltiges JSON geliefert: {e}")
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# Calculate total number of gaps
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total_gaps = sum(len(item.get("gaps", [])) for item in cloze_data.get("cloze_items", []))
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if "metadata" in cloze_data:
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cloze_data["metadata"]["total_gaps"] = total_gaps
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return cloze_data
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def generate_cloze_from_analysis(analysis_path: Path, target_language: str = "tr") -> Path:
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"""
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Generate cloze texts from an analysis JSON file.
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The cloze texts will:
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- Have multiple meaningful gaps per sentence
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- Match the difficulty level of the original
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- Include translation to target language
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Args:
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analysis_path: Path to *_analyse.json file
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target_language: Language code for translation (default: "tr" for Turkish)
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Returns:
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Path to generated *_cloze.json file
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"""
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if not analysis_path.exists():
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raise FileNotFoundError(f"Analysedatei nicht gefunden: {analysis_path}")
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try:
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analysis_data = json.loads(analysis_path.read_text(encoding="utf-8"))
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except json.JSONDecodeError as e:
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raise RuntimeError(f"Ungueltige Analyse-JSON: {e}")
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logger.info(f"Generiere Lueckentexte fuer: {analysis_path.name}")
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# Generate cloze texts (use configured API)
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if VISION_API == "claude":
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try:
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cloze_data = _generate_cloze_with_claude(analysis_data, target_language)
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except Exception as e:
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logger.warning(f"Claude Lueckentext-Generierung fehlgeschlagen, nutze OpenAI: {e}")
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cloze_data = _generate_cloze_with_openai(analysis_data, target_language)
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else:
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cloze_data = _generate_cloze_with_openai(analysis_data, target_language)
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# Save cloze data
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out_name = analysis_path.stem.replace("_analyse", "") + "_cloze.json"
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out_path = BEREINIGT_DIR / out_name
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out_path.write_text(json.dumps(cloze_data, ensure_ascii=False, indent=2), encoding="utf-8")
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logger.info(f"Lueckentexte gespeichert: {out_path.name}")
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return out_path
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