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
309
backend/klausur/services/vision_ocr_service.py
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309
backend/klausur/services/vision_ocr_service.py
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"""
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Vision-OCR Service - Handschrifterkennung mit Llama 3.2 Vision.
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DATENSCHUTZ/PRIVACY BY DESIGN:
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- Alle Verarbeitung erfolgt lokal auf dem Mac Mini
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- Keine Daten verlassen das lokale Netzwerk
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- Keine Cloud-APIs beteiligt
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- Perfekt für DSGVO-konforme Schulumgebungen
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Verwendet llama3.2-vision:11b über Ollama für OCR/Handschrifterkennung.
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Dies ist eine Alternative zu TrOCR mit besserer Handschrifterkennung.
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"""
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import os
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import base64
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import httpx
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import logging
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import time
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from typing import Optional
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from dataclasses import dataclass
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from llm_gateway.config import get_config
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logger = logging.getLogger(__name__)
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@dataclass
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class VisionOCRResult:
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"""Result from Vision-LLM OCR extraction."""
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text: str
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confidence: float
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processing_time_ms: int
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model: str = "llama3.2-vision:11b"
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device: str = "local-ollama"
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# OCR System Prompt für optimale Handschrifterkennung
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HANDWRITING_OCR_PROMPT = """Du bist ein Experte für Handschrifterkennung (OCR).
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AUFGABE: Extrahiere den handschriftlichen Text aus dem Bild so genau wie möglich.
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WICHTIGE REGELN:
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1. Transkribiere NUR den sichtbaren Text - erfinde nichts dazu
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2. Behalte die Zeilenstruktur bei (jede Zeile auf einer neuen Zeile)
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3. Bei unleserlichen Stellen: [unleserlich] oder [?] verwenden
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4. Ignoriere Linien, Kästchen und andere Formatierungen
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5. Korrigiere KEINE Rechtschreibfehler - transkribiere exakt was da steht
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6. Bei Aufzählungen: Nummern/Punkte beibehalten (1., 2., a), b), etc.)
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AUSGABE: Nur der transkribierte Text, keine Erklärungen oder Kommentare."""
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# Alternative Prompt für gedruckten Text
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PRINTED_OCR_PROMPT = """Extrahiere den gesamten Text aus diesem Bild.
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Behalte die Struktur bei (Absätze, Listen, etc.).
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Gib nur den extrahierten Text zurück, ohne Kommentare."""
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class VisionOCRService:
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"""
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OCR Service mit Llama 3.2 Vision über Ollama.
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Läuft komplett lokal auf dem Mac Mini - keine Cloud-Verbindung nötig.
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Ideal für datenschutzkonforme Klausurkorrektur in Schulen.
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Usage:
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service = VisionOCRService()
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if await service.is_available():
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result = await service.extract_text(image_bytes)
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print(result.text)
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"""
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def __init__(self, ollama_url: Optional[str] = None, model: Optional[str] = None):
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"""
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Initialize Vision OCR Service.
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Args:
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ollama_url: Ollama API URL (default: from config)
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model: Vision model to use (default: llama3.2-vision:11b)
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"""
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config = get_config()
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self.ollama_url = ollama_url or (config.ollama.base_url if config.ollama else "http://localhost:11434")
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self.model = model or config.vision_model
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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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"""Get or create HTTP client."""
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if self._client is None or self._client.is_closed:
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self._client = httpx.AsyncClient(
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timeout=300.0 # 5 min timeout für große Bilder
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)
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return self._client
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async def close(self):
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"""Close the HTTP client."""
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if self._client and not self._client.is_closed:
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await self._client.aclose()
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async def is_available(self) -> bool:
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"""Check if Ollama with vision model is available."""
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try:
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client = await self._get_client()
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# Check Ollama health
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response = await client.get(
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f"{self.ollama_url}/api/tags",
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timeout=5.0
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)
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if response.status_code != 200:
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return False
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# Check if vision model is installed
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data = response.json()
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models = [m.get("name", "") for m in data.get("models", [])]
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# Check for any vision model
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has_vision = any(
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"vision" in m.lower() or "llava" in m.lower()
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for m in models
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)
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if not has_vision:
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logger.warning(f"No vision model found. Available: {models}")
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return False
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return True
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except Exception as e:
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logger.warning(f"Vision OCR service not available: {e}")
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return False
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async def get_status(self) -> dict:
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"""Get service status."""
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try:
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client = await self._get_client()
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response = await client.get(f"{self.ollama_url}/api/tags")
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if response.status_code == 200:
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data = response.json()
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models = data.get("models", [])
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vision_models = [
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m for m in models
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if "vision" in m.get("name", "").lower() or "llava" in m.get("name", "").lower()
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]
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return {
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"status": "available",
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"ollama_url": self.ollama_url,
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"configured_model": self.model,
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"vision_models": [m.get("name") for m in vision_models],
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"total_models": len(models)
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}
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else:
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return {
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"status": "unavailable",
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"error": f"HTTP {response.status_code}"
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}
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except Exception as e:
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return {
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"status": "unavailable",
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"error": str(e)
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}
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async def extract_text(
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self,
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image_data: bytes,
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filename: str = "image.png",
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is_handwriting: bool = True
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) -> VisionOCRResult:
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"""
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Extract text from an image using Vision LLM.
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Args:
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image_data: Raw image bytes (PNG, JPG, etc.)
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filename: Original filename (for logging)
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is_handwriting: True for handwriting, False for printed text
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Returns:
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VisionOCRResult with extracted text
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"""
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start_time = time.time()
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try:
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client = await self._get_client()
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# Encode image as base64
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image_base64 = base64.b64encode(image_data).decode("utf-8")
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# Select appropriate prompt
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prompt = HANDWRITING_OCR_PROMPT if is_handwriting else PRINTED_OCR_PROMPT
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# Ollama Vision API request
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payload = {
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"model": self.model,
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"messages": [
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{
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"role": "user",
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"content": prompt,
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"images": [image_base64]
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}
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],
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"stream": False,
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"options": {
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"temperature": 0.1, # Low temperature for consistent OCR
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"num_predict": 2048, # Max tokens for extracted text
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}
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}
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logger.info(f"Sending image to Vision OCR: {filename} ({len(image_data)} bytes)")
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response = await client.post(
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f"{self.ollama_url}/api/chat",
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json=payload,
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timeout=180.0 # 3 min timeout
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)
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response.raise_for_status()
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data = response.json()
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extracted_text = data.get("message", {}).get("content", "")
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processing_time_ms = int((time.time() - start_time) * 1000)
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# Estimate confidence based on response quality
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confidence = self._estimate_confidence(extracted_text)
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logger.info(
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f"Vision OCR completed for {filename}: "
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f"{len(extracted_text)} chars in {processing_time_ms}ms"
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)
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return VisionOCRResult(
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text=extracted_text.strip(),
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confidence=confidence,
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processing_time_ms=processing_time_ms,
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model=self.model,
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device="local-ollama"
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)
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except httpx.TimeoutException:
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logger.error(f"Vision OCR timed out for {filename}")
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raise
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except Exception as e:
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logger.error(f"Vision OCR failed for {filename}: {e}")
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raise
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def _estimate_confidence(self, text: str) -> float:
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"""
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Estimate OCR confidence based on text quality.
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This is a heuristic - real confidence would need model output.
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"""
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if not text:
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return 0.0
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# Count uncertain markers
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uncertain_markers = text.count("[unleserlich]") + text.count("[?]")
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# Count reasonable text vs markers
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text_length = len(text.replace("[unleserlich]", "").replace("[?]", ""))
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if text_length == 0:
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return 0.1
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# Base confidence
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confidence = 0.85
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# Reduce for uncertain markers
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confidence -= min(uncertain_markers * 0.05, 0.3)
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# Very short text might be incomplete
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if text_length < 20:
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confidence -= 0.1
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return max(confidence, 0.1)
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# Singleton instance
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_vision_ocr_service: Optional[VisionOCRService] = None
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def get_vision_ocr_service() -> VisionOCRService:
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"""Get the Vision OCR service singleton."""
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global _vision_ocr_service
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if _vision_ocr_service is None:
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_vision_ocr_service = VisionOCRService()
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return _vision_ocr_service
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async def extract_handwriting(
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image_data: bytes,
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filename: str = "image.png"
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) -> VisionOCRResult:
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"""
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Convenience function to extract handwriting from an image.
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Uses Llama 3.2 Vision locally via Ollama.
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All processing happens on the local Mac Mini - DSGVO-konform.
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Args:
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image_data: Raw image bytes
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filename: Original filename
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Returns:
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VisionOCRResult with extracted text
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"""
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service = get_vision_ocr_service()
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return await service.extract_text(image_data, filename, is_handwriting=True)
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