bfdaf63ba9
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>
212 lines
6.0 KiB
Python
212 lines
6.0 KiB
Python
"""
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AI Processing - HTML Generator.
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Baut saubere HTML-Arbeitsblätter aus Analyse-JSON.
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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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from .core import BEREINIGT_DIR
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logger = logging.getLogger(__name__)
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def build_clean_html_from_analysis(analysis_path: Path) -> Path:
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"""
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Nimmt eine *_analyse.json-Datei und baut daraus ein sauberes HTML-Arbeitsblatt.
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NEU:
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- Fokus auf gedruckten Text (canonical_text / printed_blocks)
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- Handschriftliche Eintragungen und durchgestrichene Wörter werden NICHT in den
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Haupttext übernommen
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- Verwendung eines Open-Source-Font-Stacks (z.B. Inter / Noto Sans)
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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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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"Analyse-Datei enthält kein gültiges JSON: {analysis_path}\n{e}") from e
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title = data.get("title") or "Arbeitsblatt"
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subject = data.get("subject") or ""
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grade_level = data.get("grade_level") or ""
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instructions = data.get("instructions") or ""
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tasks = data.get("tasks", []) or []
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canonical_text = data.get("canonical_text") or ""
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printed_blocks = data.get("printed_blocks") or []
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struck = data.get("struck_through_words") or []
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html_parts = []
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html_parts.append("<!DOCTYPE html>")
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html_parts.append("<html lang='de'>")
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html_parts.append("<head>")
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html_parts.append("<meta charset='UTF-8'>")
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html_parts.append(f"<title>{title}</title>")
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html_parts.append(
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"""
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<style>
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:root {
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--font-main: "Inter", "Noto Sans", system-ui, -apple-system, BlinkMacSystemFont, sans-serif;
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}
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* { box-sizing: border-box; }
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body {
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font-family: var(--font-main);
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margin: 32px;
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line-height: 1.5;
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font-size: 14px;
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color: #111827;
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}
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.page {
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max-width: 800px;
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margin: 0 auto;
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}
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h1 {
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font-size: 24px;
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margin-bottom: 4px;
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}
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h2 {
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font-size: 18px;
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margin-top: 24px;
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}
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.meta {
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font-size: 12px;
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color: #6b7280;
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margin-bottom: 16px;
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}
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.instructions {
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margin-bottom: 20px;
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padding: 8px 10px;
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border-radius: 8px;
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background: #eff6ff;
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border: 1px solid #bfdbfe;
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font-size: 13px;
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}
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.text-blocks {
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margin-bottom: 24px;
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}
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.text-block {
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margin-bottom: 8px;
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}
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.text-block-title {
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font-weight: 600;
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margin-bottom: 4px;
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}
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.task-list {
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margin-top: 8px;
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}
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.task {
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margin-bottom: 14px;
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padding-bottom: 8px;
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border-bottom: 1px dashed #e5e7eb;
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}
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.task-title {
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font-weight: 600;
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margin-bottom: 4px;
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}
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.gap-line {
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display: inline-block;
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border-bottom: 1px solid #000;
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min-width: 80px;
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margin: 0 4px;
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}
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.footnote {
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margin-top: 24px;
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font-size: 11px;
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color: #9ca3af;
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}
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</style>
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"""
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)
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html_parts.append("</head>")
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html_parts.append("<body>")
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html_parts.append("<div class='page'>")
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# Kopfbereich
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html_parts.append(f"<h1>{title}</h1>")
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meta_bits = []
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if subject:
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meta_bits.append(f"Fach: {subject}")
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if grade_level:
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meta_bits.append(f"Klassenstufe: {grade_level}")
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if meta_bits:
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html_parts.append(f"<div class='meta'>{' | '.join(meta_bits)}</div>")
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if instructions:
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html_parts.append(
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f"<div class='instructions'><strong>Arbeitsanweisung:</strong> {instructions}</div>"
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)
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# Haupttext / gedruckte Blöcke
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html_parts.append("<section class='text-blocks'>")
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if printed_blocks:
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for block in printed_blocks:
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role = (block.get("role") or "body").lower()
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text = (block.get("text") or "").strip()
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if not text:
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continue
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html_parts.append("<div class='text-block'>")
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if role == "title":
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html_parts.append(f"<div class='text-block-title'>{text}</div>")
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else:
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html_parts.append(f"<div>{text}</div>")
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html_parts.append("</div>")
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elif canonical_text:
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# Fallback: canonical_text in Absätze aufteilen
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paragraphs = [
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p.strip()
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for p in canonical_text.replace("\r\n", "\n").split("\n\n")
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if p.strip()
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]
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for p in paragraphs:
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html_parts.append(f"<div class='text-block'>{p}</div>")
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html_parts.append("</section>")
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# Aufgabenbereich
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if tasks:
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html_parts.append("<h2>Aufgaben</h2>")
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html_parts.append("<div class='task-list'>")
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for idx, task in enumerate(tasks, start=1):
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t_type = task.get("type") or "other"
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desc = task.get("description") or ""
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text_with_gaps = task.get("text_with_gaps")
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html_parts.append("<div class='task'>")
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html_parts.append(
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f"<div class='task-title'>Aufgabe {idx} ({t_type}): {desc}</div>"
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)
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if text_with_gaps:
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# Lücken „___" werden in Linien umgewandelt
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rendered = text_with_gaps.replace("___", "<span class='gap-line'> </span>")
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html_parts.append(f"<div>{rendered}</div>")
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html_parts.append("</div>")
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html_parts.append("</div>") # .task-list
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# kleine Fußnote mit Hinweis
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if struck:
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html_parts.append(
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"<div class='footnote'>Hinweis: Einige im Original durchgestrichene Wörter wurden "
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"von der KI erkannt und NICHT in dieses saubere Arbeitsblatt übernommen.</div>"
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)
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else:
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html_parts.append(
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"<div class='footnote'>Dieses Arbeitsblatt wurde automatisch aus einem Scan rekonstruiert "
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"und von handschriftlichen Eintragungen bereinigt.</div>"
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)
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html_parts.append("</div>") # .page
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html_parts.append("</body></html>")
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html_content = "\n".join(html_parts)
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out_name = analysis_path.stem.replace("_analyse", "") + "_clean.html"
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out_path = BEREINIGT_DIR / out_name
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out_path.write_text(html_content, encoding="utf-8")
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return out_path
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