Restructure: Move ocr_pipeline + labeling + crop into ocr/ package

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

klausur-service/backend/ocr_pipeline_auto_helpers.py

@@ -1,84 +1,4 @@
-"""
-OCR Pipeline Auto-Mode Helpers.
-
-VLM shear detection, SSE event formatting, and request models.
-
-Lizenz: Apache 2.0
-DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
-"""
-
-import json
-import logging
-import os
-import re
-from typing import Any, Dict
-
-from pydantic import BaseModel
-
-logger = logging.getLogger(__name__)
-
-
-class RunAutoRequest(BaseModel):
-    from_step: int = 1          # 1=deskew, 2=dewarp, 3=columns, 4=rows, 5=words, 6=llm-review
-    ocr_engine: str = "auto"    # "auto" | "rapid" | "tesseract"
-    pronunciation: str = "british"
-    skip_llm_review: bool = False
-    dewarp_method: str = "ensemble"  # "ensemble" | "vlm" | "cv"
-
-
-async def auto_sse_event(step: str, status: str, data: Dict[str, Any]) -> str:
-    """Format a single SSE event line."""
-    payload = {"step": step, "status": status, **data}
-    return f"data: {json.dumps(payload)}\n\n"
-
-
-async def detect_shear_with_vlm(image_bytes: bytes) -> Dict[str, Any]:
-    """Ask qwen2.5vl:32b to estimate the vertical shear angle of a scanned page.
-
-    The VLM is shown the image and asked: are the column/table borders tilted?
-    If yes, by how many degrees? Returns a dict with shear_degrees and confidence.
-    Confidence is 0.0 if Ollama is unavailable or parsing fails.
-    """
-    import httpx
-    import base64
-
-    ollama_base = os.getenv("OLLAMA_BASE_URL", "http://host.docker.internal:11434")
-    model = os.getenv("OLLAMA_HTR_MODEL", "qwen2.5vl:32b")
-
-    prompt = (
-        "This is a scanned vocabulary worksheet. Look at the vertical borders of the table columns. "
-        "Are they perfectly vertical, or do they tilt slightly? "
-        "If they tilt, estimate the tilt angle in degrees (positive = top tilts right, negative = top tilts left). "
-        "Reply with ONLY a JSON object like: {\"shear_degrees\": 1.2, \"confidence\": 0.8} "
-        "Use confidence 0.0-1.0 based on how clearly you can see the tilt. "
-        "If the columns look straight, return {\"shear_degrees\": 0.0, \"confidence\": 0.9}"
-    )
-
-    img_b64 = base64.b64encode(image_bytes).decode("utf-8")
-    payload = {
-        "model": model,
-        "prompt": prompt,
-        "images": [img_b64],
-        "stream": False,
-    }
-
-    try:
-        async with httpx.AsyncClient(timeout=60.0) as client:
-            resp = await client.post(f"{ollama_base}/api/generate", json=payload)
-            resp.raise_for_status()
-            text = resp.json().get("response", "")
-
-        # Parse JSON from response (may have surrounding text)
-        match = re.search(r'\{[^}]+\}', text)
-        if match:
-            data = json.loads(match.group(0))
-            shear = float(data.get("shear_degrees", 0.0))
-            conf = float(data.get("confidence", 0.0))
-            # Clamp to reasonable range
-            shear = max(-3.0, min(3.0, shear))
-            conf = max(0.0, min(1.0, conf))
-            return {"method": "vlm_qwen2.5vl", "shear_degrees": round(shear, 3), "confidence": round(conf, 2)}
-    except Exception as e:
-        logger.warning(f"VLM dewarp failed: {e}")
-
-    return {"method": "vlm_qwen2.5vl", "shear_degrees": 0.0, "confidence": 0.0}
+# Backward-compat shim -- module moved to ocr/pipeline/auto_helpers.py
+import importlib as _importlib
+import sys as _sys
+_sys.modules[__name__] = _importlib.import_module("ocr.pipeline.auto_helpers")