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