Restructure: Move 52 files into 7 domain packages

korrektur/ zeugnis/ admin/ compliance/ worksheet/ training/ metrics/
52 shims, relative imports, RAG untouched.

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

klausur-service/backend/worksheet_editor_reconstruct.py

@@ -1,255 +1,4 @@
-"""
-Worksheet Editor Reconstruct — Document reconstruction from vocab sessions.
-"""
-
-import io
-import uuid
-import base64
-import logging
-from typing import List, Dict
-
-import numpy as np
-
-from worksheet_editor_models import (
-    ReconstructRequest,
-    ReconstructResponse,
-)
-
-logger = logging.getLogger(__name__)
-
-
-async def reconstruct_document_logic(request: ReconstructRequest) -> ReconstructResponse:
-    """
-    Reconstruct a document from a vocab session into Fabric.js canvas format.
-
-    This function:
-    1. Loads the original PDF from the vocab session
-    2. Runs OCR with position tracking
-    3. Creates Fabric.js canvas JSON with positioned elements
-    4. Maps extracted vocabulary to their positions
-
-    Returns ReconstructResponse ready to send to the client.
-    """
-    from fastapi import HTTPException
-    from vocab_worksheet_api import _sessions, convert_pdf_page_to_image
-
-    # Check if session exists
-    if request.session_id not in _sessions:
-        raise HTTPException(status_code=404, detail=f"Session {request.session_id} not found")
-
-    session = _sessions[request.session_id]
-
-    if not session.get("pdf_data"):
-        raise HTTPException(status_code=400, detail="Session has no PDF data")
-
-    pdf_data = session["pdf_data"]
-    page_count = session.get("pdf_page_count", 1)
-
-    if request.page_number < 1 or request.page_number > page_count:
-        raise HTTPException(
-            status_code=400,
-            detail=f"Page {request.page_number} not found. PDF has {page_count} pages."
-        )
-
-    vocabulary = session.get("vocabulary", [])
-    page_vocab = [v for v in vocabulary if v.get("source_page") == request.page_number]
-
-    logger.info(f"Reconstructing page {request.page_number} from session {request.session_id}")
-    logger.info(f"Found {len(page_vocab)} vocabulary items for this page")
-
-    image_bytes = await convert_pdf_page_to_image(pdf_data, request.page_number)
-    if not image_bytes:
-        raise HTTPException(status_code=500, detail="Failed to convert PDF page to image")
-
-    from PIL import Image
-    img = Image.open(io.BytesIO(image_bytes))
-    img_width, img_height = img.size
-
-    from hybrid_vocab_extractor import run_paddle_ocr
-    ocr_regions, raw_text = run_paddle_ocr(image_bytes)
-
-    logger.info(f"OCR found {len(ocr_regions)} text regions")
-
-    A4_WIDTH = 794
-    A4_HEIGHT = 1123
-    scale_x = A4_WIDTH / img_width
-    scale_y = A4_HEIGHT / img_height
-
-    fabric_objects = []
-
-    # 1. Add white background
-    fabric_objects.append({
-        "type": "rect", "left": 0, "top": 0,
-        "width": A4_WIDTH, "height": A4_HEIGHT,
-        "fill": "#ffffff", "selectable": False,
-        "evented": False, "isBackground": True
-    })
-
-    # 2. Group OCR regions by Y-coordinate to detect rows
-    sorted_regions = sorted(ocr_regions, key=lambda r: (r.y1, r.x1))
-
-    # 3. Detect headers (larger text at top)
-    headers = []
-    for region in sorted_regions:
-        height = region.y2 - region.y1
-        if region.y1 < img_height * 0.15 and height > 30:
-            headers.append(region)
-
-    # 4. Create text objects for each region
-    vocab_matched = 0
-
-    for region in sorted_regions:
-        left = int(region.x1 * scale_x)
-        top = int(region.y1 * scale_y)
-
-        is_header = region in headers
-
-        region_height = region.y2 - region.y1
-        base_font_size = max(10, min(32, int(region_height * scale_y * 0.8)))
-
-        if is_header:
-            base_font_size = max(base_font_size, 24)
-
-        is_vocab = False
-        vocab_match = None
-        for v in page_vocab:
-            if v.get("english", "").lower() in region.text.lower() or \
-               v.get("german", "").lower() in region.text.lower():
-                is_vocab = True
-                vocab_match = v
-                vocab_matched += 1
-                break
-
-        text_obj = {
-            "type": "i-text",
-            "id": f"text_{uuid.uuid4().hex[:8]}",
-            "left": left, "top": top,
-            "text": region.text,
-            "fontFamily": "Arial",
-            "fontSize": base_font_size,
-            "fontWeight": "bold" if is_header else "normal",
-            "fill": "#000000",
-            "originX": "left", "originY": "top",
-        }
-
-        if is_vocab and vocab_match:
-            text_obj["isVocabulary"] = True
-            text_obj["vocabularyId"] = vocab_match.get("id")
-            text_obj["english"] = vocab_match.get("english")
-            text_obj["german"] = vocab_match.get("german")
-
-        fabric_objects.append(text_obj)
-
-    # 5. If include_images, detect and extract image regions
-    if request.include_images:
-        image_regions = await _detect_image_regions(image_bytes, ocr_regions, img_width, img_height)
-
-        for i, img_region in enumerate(image_regions):
-            img_x1 = int(img_region["x1"])
-            img_y1 = int(img_region["y1"])
-            img_x2 = int(img_region["x2"])
-            img_y2 = int(img_region["y2"])
-
-            cropped = img.crop((img_x1, img_y1, img_x2, img_y2))
-
-            buffer = io.BytesIO()
-            cropped.save(buffer, format='PNG')
-            buffer.seek(0)
-            img_base64 = f"data:image/png;base64,{base64.b64encode(buffer.getvalue()).decode('utf-8')}"
-
-            fabric_objects.append({
-                "type": "image",
-                "id": f"img_{uuid.uuid4().hex[:8]}",
-                "left": int(img_x1 * scale_x),
-                "top": int(img_y1 * scale_y),
-                "width": int((img_x2 - img_x1) * scale_x),
-                "height": int((img_y2 - img_y1) * scale_y),
-                "src": img_base64,
-                "scaleX": 1, "scaleY": 1,
-            })
-
-    import json
-    canvas_data = {
-        "version": "6.0.0",
-        "objects": fabric_objects,
-        "background": "#ffffff"
-    }
-
-    return ReconstructResponse(
-        canvas_json=json.dumps(canvas_data),
-        page_width=A4_WIDTH,
-        page_height=A4_HEIGHT,
-        elements_count=len(fabric_objects),
-        vocabulary_matched=vocab_matched,
-        message=f"Reconstructed page {request.page_number} with {len(fabric_objects)} elements, "
-                f"{vocab_matched} vocabulary items matched"
-    )
-
-
-async def _detect_image_regions(
-    image_bytes: bytes,
-    ocr_regions: list,
-    img_width: int,
-    img_height: int
-) -> List[Dict]:
-    """
-    Detect image/graphic regions in the document.
-
-    Uses a simple approach:
-    1. Find large gaps between text regions (potential image areas)
-    2. Use edge detection to find bounded regions
-    3. Filter out text areas
-    """
-    from PIL import Image
-    import cv2
-
-    try:
-        img = Image.open(io.BytesIO(image_bytes))
-        img_array = np.array(img.convert('L'))
-
-        text_mask = np.ones_like(img_array, dtype=bool)
-        for region in ocr_regions:
-            x1 = max(0, region.x1 - 5)
-            y1 = max(0, region.y1 - 5)
-            x2 = min(img_width, region.x2 + 5)
-            y2 = min(img_height, region.y2 + 5)
-            text_mask[y1:y2, x1:x2] = False
-
-        image_regions = []
-
-        edges = cv2.Canny(img_array, 50, 150)
-        edges[~text_mask] = 0
-
-        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-        for contour in contours:
-            x, y, w, h = cv2.boundingRect(contour)
-
-            if w > 50 and h > 50:
-                if w < img_width * 0.9 and h < img_height * 0.9:
-                    region_content = img_array[y:y+h, x:x+w]
-                    variance = np.var(region_content)
-
-                    if variance > 500:
-                        image_regions.append({
-                            "x1": x, "y1": y,
-                            "x2": x + w, "y2": y + h
-                        })
-
-        filtered_regions = []
-        for region in sorted(image_regions, key=lambda r: (r["x2"]-r["x1"])*(r["y2"]-r["y1"]), reverse=True):
-            overlaps = False
-            for existing in filtered_regions:
-                if not (region["x2"] < existing["x1"] or region["x1"] > existing["x2"] or
-                        region["y2"] < existing["y1"] or region["y1"] > existing["y2"]):
-                    overlaps = True
-                    break
-            if not overlaps:
-                filtered_regions.append(region)
-
-        logger.info(f"Detected {len(filtered_regions)} image regions")
-        return filtered_regions[:10]
-
-    except Exception as e:
-        logger.warning(f"Image region detection failed: {e}")
-        return []
+# Backward-compat shim -- module moved to worksheet/editor_reconstruct.py
+import importlib as _importlib
+import sys as _sys
+_sys.modules[__name__] = _importlib.import_module("worksheet.editor_reconstruct")