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>
This commit is contained in:
Benjamin Admin
536
backend/meeting_minutes_generator.py
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536
backend/meeting_minutes_generator.py
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@@ -0,0 +1,536 @@
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"""
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BreakPilot Meeting Minutes Generator
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Generiert KI-basierte Meeting-Protokolle aus Transkriptionen.
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Nutzt das LLM Gateway (Ollama/vLLM/Anthropic) fuer lokale Verarbeitung.
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Lizenz: MIT (kommerziell nutzbar)
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"""
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import os
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import json
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import logging
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import httpx
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from datetime import datetime
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from typing import Optional, List
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from dataclasses import dataclass, asdict
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from pydantic import BaseModel, Field
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logger = logging.getLogger(__name__)
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# ==========================================
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# CONFIGURATION
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# ==========================================
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LLM_GATEWAY_URL = os.getenv("LLM_GATEWAY_URL", "http://localhost:8002")
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LLM_MODEL = os.getenv("MEETING_MINUTES_MODEL", "breakpilot-teacher-8b")
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LLM_TIMEOUT = int(os.getenv("LLM_TIMEOUT", "120"))
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# ==========================================
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# PYDANTIC MODELS
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# ==========================================
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class ActionItem(BaseModel):
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"""Ein Aktionspunkt aus dem Meeting."""
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task: str = Field(..., description="Die zu erledigende Aufgabe")
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assignee: Optional[str] = Field(None, description="Verantwortliche Person (SPEAKER_XX oder Name)")
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deadline: Optional[str] = Field(None, description="Faelligkeit, falls erwaehnt")
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priority: str = Field(default="normal", description="Prioritaet: high, normal, low")
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class Decision(BaseModel):
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"""Eine getroffene Entscheidung."""
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topic: str = Field(..., description="Thema der Entscheidung")
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decision: str = Field(..., description="Die getroffene Entscheidung")
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rationale: Optional[str] = Field(None, description="Begruendung, falls erwaehnt")
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class TopicSummary(BaseModel):
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"""Zusammenfassung eines besprochenen Themas."""
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title: str = Field(..., description="Titel des Themas")
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summary: str = Field(..., description="Kurze Zusammenfassung")
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participants: List[str] = Field(default_factory=list, description="Beteiligte Sprecher")
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duration_estimate: Optional[str] = Field(None, description="Geschaetzte Dauer")
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class MeetingMinutes(BaseModel):
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"""Vollstaendiges Meeting-Protokoll."""
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id: str
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recording_id: str
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transcription_id: str
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# Metadaten
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title: str = Field(..., description="Titel des Meetings")
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date: str = Field(..., description="Datum des Meetings")
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duration_minutes: Optional[int] = Field(None, description="Dauer in Minuten")
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participant_count: int = Field(default=0, description="Anzahl Teilnehmer")
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language: str = Field(default="de", description="Sprache")
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# Inhalt
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summary: str = Field(..., description="Zusammenfassung in 3-5 Saetzen")
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topics: List[TopicSummary] = Field(default_factory=list, description="Besprochene Themen")
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decisions: List[Decision] = Field(default_factory=list, description="Getroffene Entscheidungen")
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action_items: List[ActionItem] = Field(default_factory=list, description="Aktionspunkte/TODOs")
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open_questions: List[str] = Field(default_factory=list, description="Offene Fragen")
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# KI-Metadaten
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model_used: str = Field(..., description="Verwendetes LLM")
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generated_at: datetime = Field(default_factory=datetime.utcnow)
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generation_time_seconds: Optional[float] = Field(None, description="Generierungszeit")
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# Status
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status: str = Field(default="completed", description="Status: pending, processing, completed, failed")
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error_message: Optional[str] = Field(None, description="Fehlermeldung bei Status=failed")
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class MinutesGenerationRequest(BaseModel):
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"""Anfrage zur Protokoll-Generierung."""
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title: Optional[str] = Field(None, description="Meeting-Titel (optional, wird generiert)")
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model: str = Field(default=LLM_MODEL, description="LLM Modell")
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include_action_items: bool = Field(default=True, description="Action Items extrahieren")
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include_decisions: bool = Field(default=True, description="Entscheidungen extrahieren")
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max_topics: int = Field(default=10, description="Maximale Anzahl Themen")
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# ==========================================
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# PROMPTS (German, Education Context)
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# ==========================================
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SYSTEM_PROMPT = """Du bist ein Assistent für die Erstellung von Meeting-Protokollen in deutschen Bildungseinrichtungen (Schulen, Universitäten).
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Deine Aufgabe ist es, aus einer Transkription ein strukturiertes Protokoll zu erstellen.
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WICHTIG:
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- Schreibe professionell und sachlich auf Deutsch
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- Verwende die formelle Anrede (Sie)
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- Halte dich an die Fakten der Transkription
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- Erfinde KEINE Informationen, die nicht in der Transkription stehen
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- Sprecher werden als SPEAKER_00, SPEAKER_01 etc. bezeichnet - behalte diese Bezeichnungen bei
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- Wenn du dir bei etwas unsicher bist, schreibe "Unklar:" davor
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Format für die Ausgabe (JSON):
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{
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"summary": "3-5 Sätze Zusammenfassung",
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"topics": [
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{"title": "Thema", "summary": "Kurzbeschreibung", "participants": ["SPEAKER_00"]}
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],
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"decisions": [
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{"topic": "Thema", "decision": "Was wurde entschieden", "rationale": "Begründung oder null"}
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],
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"action_items": [
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{"task": "Aufgabe", "assignee": "SPEAKER_XX oder null", "deadline": "Datum oder null", "priority": "high/normal/low"}
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],
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"open_questions": ["Frage 1", "Frage 2"]
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}"""
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EXTRACTION_PROMPT = """Analysiere folgende Meeting-Transkription und erstelle ein strukturiertes Protokoll.
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Meeting-Titel: {title}
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Datum: {date}
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Dauer: {duration} Minuten
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Teilnehmer: {participant_count}
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--- TRANSKRIPTION ---
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{transcript}
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--- ENDE TRANSKRIPTION ---
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Erstelle ein JSON-Protokoll mit:
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1. summary: Zusammenfassung in 3-5 Sätzen
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2. topics: Liste der besprochenen Themen (maximal {max_topics})
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3. decisions: Alle getroffenen Entscheidungen
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4. action_items: Alle Aufgaben/TODOs mit Verantwortlichen (falls genannt)
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5. open_questions: Offene Fragen, die nicht beantwortet wurden
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Antworte NUR mit dem JSON-Objekt, ohne zusätzlichen Text."""
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# ==========================================
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# MEETING MINUTES GENERATOR
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# ==========================================
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class MeetingMinutesGenerator:
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"""Generator fuer Meeting-Protokolle aus Transkriptionen."""
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def __init__(self, llm_gateway_url: str = LLM_GATEWAY_URL):
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self.llm_gateway_url = llm_gateway_url
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self._client: Optional[httpx.AsyncClient] = None
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async def get_client(self) -> httpx.AsyncClient:
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"""Lazy initialization des HTTP Clients."""
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if self._client is None:
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self._client = httpx.AsyncClient(timeout=LLM_TIMEOUT)
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return self._client
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async def close(self):
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"""Schliesst den HTTP Client."""
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if self._client:
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await self._client.aclose()
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self._client = None
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async def _call_llm(
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self,
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messages: List[dict],
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model: str = LLM_MODEL,
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temperature: float = 0.3,
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max_tokens: int = 4096
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) -> str:
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"""Ruft das LLM Gateway auf."""
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client = await self.get_client()
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payload = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": False
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}
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try:
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response = await client.post(
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f"{self.llm_gateway_url}/v1/chat/completions",
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json=payload,
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timeout=LLM_TIMEOUT
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)
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response.raise_for_status()
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data = response.json()
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content = data.get("choices", [{}])[0].get("message", {}).get("content", "")
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return content
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except httpx.TimeoutException:
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logger.error("LLM Gateway timeout")
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raise RuntimeError("LLM Gateway antwortet nicht (Timeout)")
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except httpx.HTTPStatusError as e:
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logger.error(f"LLM Gateway error: {e.response.status_code}")
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raise RuntimeError(f"LLM Gateway Fehler: {e.response.status_code}")
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except Exception as e:
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logger.error(f"LLM call failed: {e}")
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raise RuntimeError(f"LLM Aufruf fehlgeschlagen: {str(e)}")
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def _parse_llm_response(self, response: str) -> dict:
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"""Parst die LLM-Antwort als JSON."""
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# Versuche JSON aus der Antwort zu extrahieren
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response = response.strip()
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# Entferne eventuelle Markdown Code-Bloecke
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if response.startswith("```json"):
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response = response[7:]
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if response.startswith("```"):
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response = response[3:]
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if response.endswith("```"):
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response = response[:-3]
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response = response.strip()
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try:
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return json.loads(response)
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except json.JSONDecodeError as e:
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logger.warning(f"JSON parse error: {e}. Response: {response[:200]}...")
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# Fallback: Leeres Protokoll
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return {
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"summary": "Protokoll konnte nicht automatisch erstellt werden.",
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"topics": [],
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"decisions": [],
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"action_items": [],
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"open_questions": []
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}
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async def generate(
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self,
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transcript: str,
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recording_id: str,
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transcription_id: str,
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title: Optional[str] = None,
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date: Optional[str] = None,
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duration_minutes: Optional[int] = None,
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participant_count: int = 0,
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model: str = LLM_MODEL,
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max_topics: int = 10,
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include_action_items: bool = True,
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include_decisions: bool = True
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) -> MeetingMinutes:
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"""
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Generiert Meeting Minutes aus einer Transkription.
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Args:
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transcript: Die vollstaendige Transkription
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recording_id: ID der Aufzeichnung
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transcription_id: ID der Transkription
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title: Meeting-Titel (wird generiert falls nicht angegeben)
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date: Datum des Meetings
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duration_minutes: Dauer in Minuten
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participant_count: Anzahl Teilnehmer
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model: LLM Modell
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max_topics: Maximale Anzahl Themen
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include_action_items: Action Items extrahieren
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include_decisions: Entscheidungen extrahieren
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Returns:
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MeetingMinutes: Das generierte Protokoll
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"""
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import uuid
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import time
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start_time = time.time()
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minutes_id = str(uuid.uuid4())
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# Defaults
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if not title:
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title = f"Meeting vom {date or datetime.utcnow().strftime('%d.%m.%Y')}"
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if not date:
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date = datetime.utcnow().strftime("%d.%m.%Y")
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# Transkription kuerzen falls zu lang (max ~8000 Tokens ~ 32000 chars)
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max_chars = 32000
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if len(transcript) > max_chars:
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logger.warning(f"Transcript too long ({len(transcript)} chars), truncating...")
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transcript = transcript[:max_chars] + "\n\n[... Transkription gekürzt ...]"
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# Prompt erstellen
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user_prompt = EXTRACTION_PROMPT.format(
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title=title,
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date=date,
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duration=duration_minutes or "unbekannt",
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participant_count=participant_count,
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transcript=transcript,
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max_topics=max_topics
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)
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_prompt}
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]
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try:
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# LLM aufrufen
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logger.info(f"Generating minutes for recording {recording_id} using {model}")
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response = await self._call_llm(messages, model=model)
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# Antwort parsen
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parsed = self._parse_llm_response(response)
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generation_time = time.time() - start_time
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# MeetingMinutes erstellen
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minutes = MeetingMinutes(
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id=minutes_id,
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recording_id=recording_id,
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transcription_id=transcription_id,
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title=title,
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date=date,
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duration_minutes=duration_minutes,
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participant_count=participant_count,
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language="de",
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summary=parsed.get("summary", "Zusammenfassung nicht verfügbar."),
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topics=[
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TopicSummary(**t) for t in parsed.get("topics", [])
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] if parsed.get("topics") else [],
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decisions=[
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Decision(**d) for d in parsed.get("decisions", [])
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] if include_decisions and parsed.get("decisions") else [],
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action_items=[
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ActionItem(**a) for a in parsed.get("action_items", [])
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] if include_action_items and parsed.get("action_items") else [],
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open_questions=parsed.get("open_questions", []),
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model_used=model,
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generated_at=datetime.utcnow(),
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generation_time_seconds=round(generation_time, 2),
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status="completed"
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)
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logger.info(f"Minutes generated in {generation_time:.2f}s: {len(minutes.topics)} topics, {len(minutes.action_items)} action items")
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return minutes
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except Exception as e:
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logger.error(f"Minutes generation failed: {e}")
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return MeetingMinutes(
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id=minutes_id,
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recording_id=recording_id,
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transcription_id=transcription_id,
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title=title,
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date=date,
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duration_minutes=duration_minutes,
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participant_count=participant_count,
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language="de",
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summary="",
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model_used=model,
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status="failed",
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error_message=str(e)
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)
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# ==========================================
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# EXPORT FUNCTIONS
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# ==========================================
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def minutes_to_markdown(minutes: MeetingMinutes) -> str:
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"""Exportiert Meeting Minutes als Markdown."""
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md = f"""# {minutes.title}
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**Datum:** {minutes.date}
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**Dauer:** {minutes.duration_minutes or 'unbekannt'} Minuten
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||||
**Teilnehmer:** {minutes.participant_count}
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---
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## Zusammenfassung
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{minutes.summary}
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||||
---
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||||
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## Besprochene Themen
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"""
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for i, topic in enumerate(minutes.topics, 1):
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md += f"### {i}. {topic.title}\n\n"
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md += f"{topic.summary}\n\n"
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if topic.participants:
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md += f"*Beteiligte: {', '.join(topic.participants)}*\n\n"
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||||
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if minutes.decisions:
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md += "---\n\n## Entscheidungen\n\n"
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for decision in minutes.decisions:
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md += f"- **{decision.topic}:** {decision.decision}"
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if decision.rationale:
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md += f" *(Begründung: {decision.rationale})*"
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md += "\n"
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md += "\n"
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||||
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if minutes.action_items:
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md += "---\n\n## Action Items\n\n"
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||||
md += "| Aufgabe | Verantwortlich | Fällig | Priorität |\n"
|
||||
md += "|---------|----------------|--------|----------|\n"
|
||||
for item in minutes.action_items:
|
||||
md += f"| {item.task} | {item.assignee or '-'} | {item.deadline or '-'} | {item.priority} |\n"
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||||
md += "\n"
|
||||
|
||||
if minutes.open_questions:
|
||||
md += "---\n\n## Offene Fragen\n\n"
|
||||
for q in minutes.open_questions:
|
||||
md += f"- {q}\n"
|
||||
md += "\n"
|
||||
|
||||
md += f"""---
|
||||
|
||||
*Generiert am {minutes.generated_at.strftime('%d.%m.%Y um %H:%M Uhr')} mit {minutes.model_used}*
|
||||
*Generierungszeit: {minutes.generation_time_seconds or 0:.1f} Sekunden*
|
||||
"""
|
||||
|
||||
return md
|
||||
|
||||
|
||||
def minutes_to_html(minutes: MeetingMinutes) -> str:
|
||||
"""Exportiert Meeting Minutes als HTML (fuer PDF-Konvertierung)."""
|
||||
html = f"""<!DOCTYPE html>
|
||||
<html lang="de">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>{minutes.title}</title>
|
||||
<style>
|
||||
body {{ font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }}
|
||||
h1 {{ color: #1a365d; border-bottom: 2px solid #3182ce; padding-bottom: 10px; }}
|
||||
h2 {{ color: #2c5282; margin-top: 30px; }}
|
||||
h3 {{ color: #2d3748; }}
|
||||
.meta {{ background: #f7fafc; padding: 15px; border-radius: 8px; margin-bottom: 20px; }}
|
||||
.meta p {{ margin: 5px 0; }}
|
||||
.summary {{ background: #ebf8ff; padding: 15px; border-left: 4px solid #3182ce; margin: 20px 0; }}
|
||||
table {{ width: 100%; border-collapse: collapse; margin: 15px 0; }}
|
||||
th, td {{ border: 1px solid #e2e8f0; padding: 10px; text-align: left; }}
|
||||
th {{ background: #edf2f7; }}
|
||||
.priority-high {{ color: #c53030; font-weight: bold; }}
|
||||
.priority-normal {{ color: #2d3748; }}
|
||||
.priority-low {{ color: #718096; }}
|
||||
.decision {{ background: #f0fff4; padding: 10px; border-left: 4px solid #38a169; margin: 10px 0; }}
|
||||
.question {{ background: #fffaf0; padding: 10px; border-left: 4px solid #dd6b20; margin: 10px 0; }}
|
||||
.footer {{ margin-top: 40px; padding-top: 20px; border-top: 1px solid #e2e8f0; font-size: 0.9em; color: #718096; }}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>{minutes.title}</h1>
|
||||
|
||||
<div class="meta">
|
||||
<p><strong>Datum:</strong> {minutes.date}</p>
|
||||
<p><strong>Dauer:</strong> {minutes.duration_minutes or 'unbekannt'} Minuten</p>
|
||||
<p><strong>Teilnehmer:</strong> {minutes.participant_count}</p>
|
||||
</div>
|
||||
|
||||
<h2>Zusammenfassung</h2>
|
||||
<div class="summary">
|
||||
<p>{minutes.summary}</p>
|
||||
</div>
|
||||
|
||||
<h2>Besprochene Themen</h2>
|
||||
"""
|
||||
|
||||
for i, topic in enumerate(minutes.topics, 1):
|
||||
html += f""" <h3>{i}. {topic.title}</h3>
|
||||
<p>{topic.summary}</p>
|
||||
"""
|
||||
if topic.participants:
|
||||
html += f" <p><em>Beteiligte: {', '.join(topic.participants)}</em></p>\n"
|
||||
|
||||
if minutes.decisions:
|
||||
html += " <h2>Entscheidungen</h2>\n"
|
||||
for decision in minutes.decisions:
|
||||
html += f""" <div class="decision">
|
||||
<strong>{decision.topic}:</strong> {decision.decision}
|
||||
"""
|
||||
if decision.rationale:
|
||||
html += f" <br><em>Begründung: {decision.rationale}</em>\n"
|
||||
html += " </div>\n"
|
||||
|
||||
if minutes.action_items:
|
||||
html += """ <h2>Action Items</h2>
|
||||
<table>
|
||||
<thead>
|
||||
<tr><th>Aufgabe</th><th>Verantwortlich</th><th>Fällig</th><th>Priorität</th></tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
"""
|
||||
for item in minutes.action_items:
|
||||
priority_class = f"priority-{item.priority}"
|
||||
html += f""" <tr>
|
||||
<td>{item.task}</td>
|
||||
<td>{item.assignee or '-'}</td>
|
||||
<td>{item.deadline or '-'}</td>
|
||||
<td class="{priority_class}">{item.priority}</td>
|
||||
</tr>
|
||||
"""
|
||||
html += """ </tbody>
|
||||
</table>
|
||||
"""
|
||||
|
||||
if minutes.open_questions:
|
||||
html += " <h2>Offene Fragen</h2>\n"
|
||||
for q in minutes.open_questions:
|
||||
html += f' <div class="question">{q}</div>\n'
|
||||
|
||||
html += f"""
|
||||
<div class="footer">
|
||||
<p>Generiert am {minutes.generated_at.strftime('%d.%m.%Y um %H:%M Uhr')} mit {minutes.model_used}</p>
|
||||
<p>Generierungszeit: {minutes.generation_time_seconds or 0:.1f} Sekunden</p>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
return html
|
||||
|
||||
|
||||
# ==========================================
|
||||
# SINGLETON
|
||||
# ==========================================
|
||||
|
||||
_generator: Optional[MeetingMinutesGenerator] = None
|
||||
|
||||
|
||||
def get_minutes_generator() -> MeetingMinutesGenerator:
|
||||
"""Gibt den Meeting Minutes Generator Singleton zurueck."""
|
||||
global _generator
|
||||
if _generator is None:
|
||||
_generator = MeetingMinutesGenerator()
|
||||
return _generator
|
||||
Reference in New Issue
Block a user