Add interactive learning modules MVP (Phases 1-3.1)

New feature: After OCR vocabulary extraction, users can generate interactive
learning modules (flashcards, quiz, type trainer) with one click.

Frontend (studio-v2):
- Fortune Sheet spreadsheet editor tab in vocab-worksheet
- "Lernmodule generieren" button in ExportTab
- /learn page with unit overview and exercise type cards
- /learn/[unitId]/flashcards — Flip-card trainer with Leitner spaced repetition
- /learn/[unitId]/quiz — Multiple choice quiz with explanations
- /learn/[unitId]/type — Type-in trainer with Levenshtein distance feedback
- AudioButton component using Web Speech API for EN+DE TTS

Backend (klausur-service):
- vocab_learn_bridge.py: Converts VocabularyEntry[] to analysis_data format
- POST /sessions/{id}/generate-learning-unit endpoint

Backend (backend-lehrer):
- generate-qa, generate-mc, generate-cloze endpoints on learning units
- get-qa/mc/cloze data retrieval endpoints
- Leitner progress update + next review items endpoints

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend-lehrer/learning_units_api.py

@@ -1,5 +1,9 @@
 from typing import List, Dict, Any, Optional
 from datetime import datetime
+from pathlib import Path
+import json
+import os
+import logging
 
 from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
@@ -15,6 +19,8 @@ from learning_units import (
     delete_learning_unit,
 )
 
+logger = logging.getLogger(__name__)
+
 
 router = APIRouter(
     prefix="/learning-units",
@@ -49,6 +55,11 @@ class RemoveWorksheetPayload(BaseModel):
     worksheet_file: str
 
 
+class GenerateFromAnalysisPayload(BaseModel):
+    analysis_data: Dict[str, Any]
+    num_questions: int = 8
+
+
 # ---------- Hilfsfunktion: Backend-Modell -> Frontend-Objekt ----------
 
 
@@ -195,3 +206,145 @@ def api_delete_learning_unit(unit_id: str):
         raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")
     return {"status": "deleted", "id": unit_id}
 
+
+# ---------- Generator-Endpunkte ----------
+
+LERNEINHEITEN_DIR = os.path.expanduser("~/Arbeitsblaetter/Lerneinheiten")
+
+
+def _save_analysis_and_get_path(unit_id: str, analysis_data: Dict[str, Any]) -> Path:
+    """Save analysis_data to disk and return the path."""
+    os.makedirs(LERNEINHEITEN_DIR, exist_ok=True)
+    path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_analyse.json"
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(analysis_data, f, ensure_ascii=False, indent=2)
+    return path
+
+
+@router.post("/{unit_id}/generate-qa")
+def api_generate_qa(unit_id: str, payload: GenerateFromAnalysisPayload):
+    """Generate Q&A items with Leitner fields from analysis data."""
+    lu = get_learning_unit(unit_id)
+    if not lu:
+        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")
+
+    analysis_path = _save_analysis_and_get_path(unit_id, payload.analysis_data)
+
+    try:
+        from ai_processing.qa_generator import generate_qa_from_analysis
+        qa_path = generate_qa_from_analysis(analysis_path, num_questions=payload.num_questions)
+        with open(qa_path, "r", encoding="utf-8") as f:
+            qa_data = json.load(f)
+
+        # Update unit status
+        update_learning_unit(unit_id, LearningUnitUpdate(status="qa_generated"))
+        logger.info(f"Generated QA for unit {unit_id}: {len(qa_data.get('qa_items', []))} items")
+        return qa_data
+    except Exception as e:
+        logger.error(f"QA generation failed for {unit_id}: {e}")
+        raise HTTPException(status_code=500, detail=f"QA-Generierung fehlgeschlagen: {e}")
+
+
+@router.post("/{unit_id}/generate-mc")
+def api_generate_mc(unit_id: str, payload: GenerateFromAnalysisPayload):
+    """Generate multiple choice questions from analysis data."""
+    lu = get_learning_unit(unit_id)
+    if not lu:
+        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")
+
+    analysis_path = _save_analysis_and_get_path(unit_id, payload.analysis_data)
+
+    try:
+        from ai_processing.mc_generator import generate_mc_from_analysis
+        mc_path = generate_mc_from_analysis(analysis_path, num_questions=payload.num_questions)
+        with open(mc_path, "r", encoding="utf-8") as f:
+            mc_data = json.load(f)
+
+        update_learning_unit(unit_id, LearningUnitUpdate(status="mc_generated"))
+        logger.info(f"Generated MC for unit {unit_id}: {len(mc_data.get('questions', []))} questions")
+        return mc_data
+    except Exception as e:
+        logger.error(f"MC generation failed for {unit_id}: {e}")
+        raise HTTPException(status_code=500, detail=f"MC-Generierung fehlgeschlagen: {e}")
+
+
+@router.post("/{unit_id}/generate-cloze")
+def api_generate_cloze(unit_id: str, payload: GenerateFromAnalysisPayload):
+    """Generate cloze (fill-in-the-blank) items from analysis data."""
+    lu = get_learning_unit(unit_id)
+    if not lu:
+        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")
+
+    analysis_path = _save_analysis_and_get_path(unit_id, payload.analysis_data)
+
+    try:
+        from ai_processing.cloze_generator import generate_cloze_from_analysis
+        cloze_path = generate_cloze_from_analysis(analysis_path)
+        with open(cloze_path, "r", encoding="utf-8") as f:
+            cloze_data = json.load(f)
+
+        update_learning_unit(unit_id, LearningUnitUpdate(status="cloze_generated"))
+        logger.info(f"Generated Cloze for unit {unit_id}: {len(cloze_data.get('cloze_items', []))} items")
+        return cloze_data
+    except Exception as e:
+        logger.error(f"Cloze generation failed for {unit_id}: {e}")
+        raise HTTPException(status_code=500, detail=f"Cloze-Generierung fehlgeschlagen: {e}")
+
+
+@router.get("/{unit_id}/qa")
+def api_get_qa(unit_id: str):
+    """Get generated QA items for a unit."""
+    qa_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_qa.json"
+    if not qa_path.exists():
+        raise HTTPException(status_code=404, detail="Keine QA-Daten gefunden.")
+    with open(qa_path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+@router.get("/{unit_id}/mc")
+def api_get_mc(unit_id: str):
+    """Get generated MC questions for a unit."""
+    mc_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_mc.json"
+    if not mc_path.exists():
+        raise HTTPException(status_code=404, detail="Keine MC-Daten gefunden.")
+    with open(mc_path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+@router.get("/{unit_id}/cloze")
+def api_get_cloze(unit_id: str):
+    """Get generated cloze items for a unit."""
+    cloze_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_cloze.json"
+    if not cloze_path.exists():
+        raise HTTPException(status_code=404, detail="Keine Cloze-Daten gefunden.")
+    with open(cloze_path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+@router.post("/{unit_id}/leitner/update")
+def api_update_leitner(unit_id: str, item_id: str, correct: bool):
+    """Update Leitner progress for a QA item."""
+    qa_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_qa.json"
+    if not qa_path.exists():
+        raise HTTPException(status_code=404, detail="Keine QA-Daten gefunden.")
+    try:
+        from ai_processing.qa_generator import update_leitner_progress
+        result = update_leitner_progress(qa_path, item_id, correct)
+        return result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/{unit_id}/leitner/next")
+def api_get_next_review(unit_id: str, limit: int = 5):
+    """Get next Leitner review items."""
+    qa_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_qa.json"
+    if not qa_path.exists():
+        raise HTTPException(status_code=404, detail="Keine QA-Daten gefunden.")
+    try:
+        from ai_processing.qa_generator import get_next_review_items
+        items = get_next_review_items(qa_path, limit=limit)
+        return {"items": items, "count": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+