Faktenkompass: UI-Rebrand, Admin-Assistent und Produktions-Setup.
Modernisiert die öffentliche Oberfläche mit Inter, Dark Mode und Live-Suche, ergänzt KI-gestützte Entwürfe im Admin mit Nachweis-Uploads und dokumentiert Daphne/Nginx für den Serverbetrieb. Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Pirmin Hinderling (fedora)
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import base64
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import json
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import mimetypes
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import re
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from django.conf import settings
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from claims.models import Category, Claim
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VALID_CLAIM_STATUSES = {choice[0] for choice in Claim.Status.choices}
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VALID_EVIDENCE_LEVELS = {choice[0] for choice in Claim.EvidenceLevel.choices}
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SYSTEM_PROMPT = """Du bist ein sachlicher Fakten-Assistent für die Plattform „Faktenkompass“.
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Deine Aufgabe: Aus Artikeln, Bildern und Nutzereingaben einen überprüfbaren Fakteneintrag erstellen.
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Regeln:
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- Schreibe auf Deutsch, neutral und wissenschaftlich.
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- Keine parteipolitische Sprache, keine Übertreibungen.
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- Die Behauptung (title) formuliert eine häufig geäußerte Aussage, die überprüft wird.
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- Die Kurzantwort ist in ~20 Sekunden lesbar.
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- Quellen müssen echte, nachvollziehbare Referenzen sein (URL aus Eingabe bevorzugen).
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- Wähle eine passende bestehende Kategorie (use_existing_slug) ODER schlage eine neue vor.
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- Icons für neue Kategorien: Bootstrap Icons Klassen wie bi-thermometer-half, bi-virus, bi-lightning-charge.
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Antworte ausschließlich als gültiges JSON mit dieser Struktur:
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{
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"category": {
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"use_existing_slug": "slug-oder-null",
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"new_category": {
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"name": "Name",
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"description": "Kurzbeschreibung",
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"icon": "bi-icon-name"
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}
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},
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"claim": {
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"title": "Die zu prüfende Behauptung",
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"status": "falsch|irrefuehrend|teilweise_richtig|unbelegt|wissenschaftlicher_konsens|offene_frage",
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"short_answer": "Kurze Einordnung",
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"detailed_explanation": "Ausführlichere Erklärung",
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"evidence_level": "sehr_stark|stark|mittel|schwach"
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},
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"tags": ["Schlagwort1", "Schlagwort2"],
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"sources": [
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{
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"title": "Titel",
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"organization": "Autor/Organisation",
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"url": "https://...",
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"description": "Kurzbeschreibung"
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}
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],
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"counter_arguments": [
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{
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"argument": "Häufiges Gegenargument",
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"response": "Sachliche Antwort"
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}
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],
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"assistant_summary": "Kurze Zusammenfassung für den Admin (1-2 Sätze)"
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}
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"""
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class AIAssistantError(Exception):
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pass
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def _categories_context():
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categories = Category.objects.all().values('name', 'slug', 'description')
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if not categories:
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return 'Keine Kategorien vorhanden – neue Kategorie vorschlagen.'
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lines = []
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for cat in categories:
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lines.append(f"- {cat['name']} (slug: {cat['slug']}): {cat['description'][:120]}")
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return '\n'.join(lines)
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def _build_user_prompt(*, article_url='', article_text='', user_notes='', refinement=''):
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parts = [
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'Bestehende Kategorien:',
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_categories_context(),
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'',
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f'Artikel-URL: {article_url or "(keine)"}',
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'',
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'Artikeltext:',
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article_text or '(nicht verfügbar)',
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'',
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'Zusätzliche Hinweise des Admins:',
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user_notes or '(keine)',
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]
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if refinement:
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parts.extend(['', 'Überarbeitungswunsch:', refinement])
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return '\n'.join(parts)
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def _svg_to_png_bytes(svg_data):
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try:
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import cairosvg
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except ImportError:
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return None
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try:
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return cairosvg.svg2png(bytestring=svg_data)
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except Exception:
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return None
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def _svg_text_fallback(svg_data):
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text = svg_data.decode('utf-8', errors='replace')
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text = re.sub(r'<script[^>]*>.*?</script>', '', text, flags=re.IGNORECASE | re.DOTALL)
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return text[:4000]
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def _image_data_url(data, mime_type):
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encoded = base64.b64encode(data).decode('ascii')
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return {
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'type': 'image_url',
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'image_url': {'url': f'data:{mime_type};base64,{encoded}'},
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}
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def _image_message_part(uploaded_file):
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uploaded_file.open('rb')
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try:
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data = uploaded_file.read()
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finally:
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uploaded_file.close()
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mime_type = mimetypes.guess_type(uploaded_file.name)[0]
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if mime_type == 'application/pdf' or uploaded_file.name.lower().endswith('.pdf'):
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return {
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'type': 'text',
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'text': '[PDF-Datei hochgeladen – nutze vor allem die Admin-Beschreibung und den Artikeltext.]',
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}
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if mime_type == 'image/svg+xml' or uploaded_file.name.lower().endswith('.svg'):
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png_data = _svg_to_png_bytes(data)
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if png_data:
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return _image_data_url(png_data, 'image/png')
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return {
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'type': 'text',
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'text': (
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'[SVG-Grafik hochgeladen – OpenAI unterstützt SVG nicht direkt. '
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'Nutze diesen XML-Auszug und die Admin-Beschreibung:]\n'
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+ _svg_text_fallback(data)
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),
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}
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mime_type = mime_type or 'image/jpeg'
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if mime_type not in {'image/png', 'image/jpeg', 'image/gif', 'image/webp'}:
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mime_type = 'image/jpeg'
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return _image_data_url(data, mime_type)
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def _call_openai(messages, *, has_image=False):
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api_key = getattr(settings, 'OPENAI_API_KEY', '')
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if not api_key:
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raise AIAssistantError(
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'OPENAI_API_KEY ist nicht gesetzt. Bitte in der Umgebung oder .env konfigurieren.'
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)
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try:
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from openai import OpenAI
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except ImportError as exc:
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raise AIAssistantError('OpenAI-Paket fehlt. Bitte pip install -r requirements.txt ausführen.') from exc
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model = settings.OPENAI_MODEL_VISION if has_image else settings.OPENAI_MODEL
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client = OpenAI(api_key=api_key, timeout=settings.OPENAI_TIMEOUT)
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response = client.chat.completions.create(
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model=model,
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messages=messages,
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response_format={'type': 'json_object'},
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temperature=0.3,
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)
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content = response.choices[0].message.content
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if not content:
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raise AIAssistantError('Leere Antwort von der KI erhalten.')
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return json.loads(content)
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def _validate_payload(payload):
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claim = payload.get('claim') or {}
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status = claim.get('status', '')
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evidence = claim.get('evidence_level', '')
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if status and status not in VALID_CLAIM_STATUSES:
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raise AIAssistantError(f'Ungültiger Status von der KI: {status}')
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if evidence and evidence not in VALID_EVIDENCE_LEVELS:
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raise AIAssistantError(f'Ungültiges Evidenzlevel von der KI: {evidence}')
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return payload
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def generate_draft_payload(
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*,
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article_url='',
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article_text='',
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user_notes='',
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uploaded_file=None,
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refinement='',
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previous_payload=None,
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):
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user_prompt = _build_user_prompt(
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article_url=article_url,
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article_text=article_text,
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user_notes=user_notes,
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refinement=refinement,
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)
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if previous_payload:
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user_prompt += '\n\nBisheriger Entwurf (JSON):\n' + json.dumps(previous_payload, ensure_ascii=False)
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user_content = [{'type': 'text', 'text': user_prompt}]
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has_image = False
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extra_text = ''
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if uploaded_file and uploaded_file.name:
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part = _image_message_part(uploaded_file)
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if part.get('type') == 'image_url':
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user_content.append(part)
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has_image = True
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elif part.get('type') == 'text':
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extra_text = part['text']
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if extra_text:
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user_prompt = f'{user_prompt}\n\n{extra_text}'
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if has_image:
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user_content[0] = {'type': 'text', 'text': user_prompt}
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user_message = user_content
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else:
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user_message = user_prompt
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messages = [
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{'role': 'system', 'content': SYSTEM_PROMPT},
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{'role': 'user', 'content': user_message},
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]
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payload = _call_openai(messages, has_image=has_image)
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return _validate_payload(payload)
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def apply_payload_to_draft(draft, payload):
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category = payload.get('category') or {}
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claim = payload.get('claim') or {}
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draft.category_slug = category.get('use_existing_slug') or ''
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new_category = category.get('new_category') or {}
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draft.new_category_name = new_category.get('name', '')
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draft.new_category_description = new_category.get('description', '')
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draft.new_category_icon = new_category.get('icon', '')
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draft.title = claim.get('title', '')
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draft.claim_status = claim.get('status', Claim.Status.UNBELEGT)
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draft.short_answer = claim.get('short_answer', '')
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draft.detailed_explanation = claim.get('detailed_explanation', '')
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draft.evidence_level = claim.get('evidence_level', Claim.EvidenceLevel.MITTEL)
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draft.tags = payload.get('tags') or []
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draft.sources = payload.get('sources') or []
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draft.counter_arguments = payload.get('counter_arguments') or []
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draft.ai_raw_response = payload
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summary = payload.get('assistant_summary', 'Entwurf wurde erstellt.')
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draft.add_message('assistant', summary)
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