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>

backend/test_cleaning.py

@@ -0,0 +1,109 @@
+"""
+Test script for worksheet cleaning pipeline
+"""
+
+from pathlib import Path
+import json
+import sys
+
+# Import functions from ai_processor
+from ai_processor import analyze_scan_structure_with_ai, remove_handwriting_from_scan
+
+def test_worksheet_cleaning(filename: str):
+    """Test the complete cleaning pipeline"""
+
+    # Paths
+    eingang_dir = Path.home() / "Arbeitsblaetter" / "Eingang"
+    bereinigt_dir = Path.home() / "Arbeitsblaetter" / "Bereinigt"
+
+    input_path = eingang_dir / filename
+
+    if not input_path.exists():
+        print(f"❌ Error: File not found: {input_path}")
+        return False
+
+    print(f"\n{'='*60}")
+    print(f"🧪 TESTING WORKSHEET CLEANING PIPELINE")
+    print(f"{'='*60}")
+    print(f"Input file: {filename}")
+    print(f"{'='*60}\n")
+
+    # Stage 1: AI Analysis
+    print("📊 Stage 1: AI Analysis (Enhanced)")
+    print("-" * 60)
+    try:
+        analysis_path = analyze_scan_structure_with_ai(input_path)
+        print(f"✅ Analysis completed: {analysis_path.name}")
+
+        # Load and display analysis
+        analysis_data = json.loads(analysis_path.read_text(encoding='utf-8'))
+
+        print(f"\n📋 Analysis Results:")
+        print(f"  - Title: {analysis_data.get('title')}")
+        print(f"  - Subject: {analysis_data.get('subject')}")
+        print(f"  - Grade Level: {analysis_data.get('grade_level')}")
+
+        # Layout info
+        layout = analysis_data.get('layout', {})
+        text_regions = layout.get('text_regions', [])
+        diagram_elements = layout.get('diagram_elements', [])
+        print(f"  - Text regions: {len(text_regions)}")
+        print(f"  - Diagram elements: {len(diagram_elements)}")
+
+        # Handwriting info
+        hw_regions = analysis_data.get('handwriting_regions', [])
+        print(f"  - Handwriting regions: {len(hw_regions)}")
+
+        if hw_regions:
+            print(f"\n  🖊️  Handwriting detected:")
+            for i, hw in enumerate(hw_regions[:3], 1):  # Show first 3
+                print(f"    {i}. Type: {hw.get('type')}, Color: {hw.get('color_hint')}")
+                print(f"       Text: '{hw.get('text', '')[:50]}...'")
+
+        print()
+
+    except Exception as e:
+        print(f"❌ Analysis failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+    # Stage 2: Image Cleaning
+    print("🧹 Stage 2: Image Cleaning (OpenCV + AI)")
+    print("-" * 60)
+    try:
+        cleaned_path = remove_handwriting_from_scan(input_path)
+        print(f"✅ Cleaning completed: {cleaned_path.name}")
+
+        # Check file size
+        original_size = input_path.stat().st_size / 1024
+        cleaned_size = cleaned_path.stat().st_size / 1024
+        print(f"  - Original size: {original_size:.1f} KB")
+        print(f"  - Cleaned size: {cleaned_size:.1f} KB")
+
+    except Exception as e:
+        print(f"❌ Cleaning failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+    # Summary
+    print(f"\n{'='*60}")
+    print("✅ TEST COMPLETED SUCCESSFULLY")
+    print(f"{'='*60}")
+    print(f"\n📂 Output files in: {bereinigt_dir}")
+    print(f"  - {input_path.stem}_analyse.json")
+    print(f"  - {input_path.stem}_clean.jpg")
+    print()
+
+    return True
+
+if __name__ == "__main__":
+    # Test with Handschrift.JPG
+    filename = "2025-12-10_Handschrift.JPG"
+
+    if len(sys.argv) > 1:
+        filename = sys.argv[1]
+
+    success = test_worksheet_cleaning(filename)
+    sys.exit(0 if success else 1)