[split-required] Split 500-1000 LOC files across all services
backend-lehrer (5 files): - alerts_agent/db/repository.py (992 → 5), abitur_docs_api.py (956 → 3) - teacher_dashboard_api.py (951 → 3), services/pdf_service.py (916 → 3) - mail/mail_db.py (987 → 6) klausur-service (5 files): - legal_templates_ingestion.py (942 → 3), ocr_pipeline_postprocess.py (929 → 4) - ocr_pipeline_words.py (876 → 3), ocr_pipeline_ocr_merge.py (616 → 2) - KorrekturPage.tsx (956 → 6) website (5 pages): - mail (985 → 9), edu-search (958 → 8), mac-mini (950 → 7) - ocr-labeling (946 → 7), audit-workspace (871 → 4) studio-v2 (5 files + 1 deleted): - page.tsx (946 → 5), MessagesContext.tsx (925 → 4) - korrektur (914 → 6), worksheet-cleanup (899 → 6) - useVocabWorksheet.ts (888 → 3) - Deleted dead page-original.tsx (934 LOC) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
303
klausur-service/backend/ocr_pipeline_words_stream.py
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303
klausur-service/backend/ocr_pipeline_words_stream.py
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"""
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OCR Pipeline Words Stream — SSE streaming generators for word detection.
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Extracted from ocr_pipeline_words.py.
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Lizenz: Apache 2.0
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DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
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"""
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import json
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import logging
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import time
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from typing import Any, Dict, List
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import numpy as np
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from fastapi import Request
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from cv_vocab_pipeline import (
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PageRegion,
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RowGeometry,
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_cells_to_vocab_entries,
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_fix_character_confusion,
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_fix_phonetic_brackets,
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fix_cell_phonetics,
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build_cell_grid_v2,
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build_cell_grid_v2_streaming,
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create_ocr_image,
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)
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from ocr_pipeline_session_store import update_session_db
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from ocr_pipeline_common import _cache
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logger = logging.getLogger(__name__)
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async def _word_batch_stream_generator(
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session_id: str,
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cached: Dict[str, Any],
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col_regions: List[PageRegion],
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row_geoms: List[RowGeometry],
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dewarped_bgr: np.ndarray,
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engine: str,
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pronunciation: str,
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request: Request,
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skip_heal_gaps: bool = False,
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):
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"""SSE generator that runs batch OCR (parallel) then streams results.
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Uses build_cell_grid_v2 with ThreadPoolExecutor for parallel OCR,
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then emits all cells as SSE events.
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"""
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import asyncio
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t0 = time.time()
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ocr_img = create_ocr_image(dewarped_bgr)
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img_h, img_w = dewarped_bgr.shape[:2]
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_skip_types = {'column_ignore', 'header', 'footer', 'margin_top', 'margin_bottom', 'margin_left', 'margin_right'}
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n_content_rows = len([r for r in row_geoms if r.row_type == 'content'])
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n_cols = len([c for c in col_regions if c.type not in _skip_types])
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col_types = {c.type for c in col_regions if c.type not in _skip_types}
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is_vocab = bool(col_types & {'column_en', 'column_de'})
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total_cells = n_content_rows * n_cols
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# 1. Send meta event immediately
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meta_event = {
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"type": "meta",
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"grid_shape": {"rows": n_content_rows, "cols": n_cols, "total_cells": total_cells},
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"layout": "vocab" if is_vocab else "generic",
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}
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yield f"data: {json.dumps(meta_event)}\n\n"
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# 2. Send preparing event (keepalive for proxy)
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yield f"data: {json.dumps({'type': 'preparing', 'message': 'Cell-First OCR laeuft parallel...'})}\n\n"
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# 3. Run batch OCR in thread pool with periodic keepalive events.
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loop = asyncio.get_event_loop()
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ocr_future = loop.run_in_executor(
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None,
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lambda: build_cell_grid_v2(
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ocr_img, col_regions, row_geoms, img_w, img_h,
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ocr_engine=engine, img_bgr=dewarped_bgr,
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skip_heal_gaps=skip_heal_gaps,
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),
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)
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# Send keepalive events every 5 seconds while OCR runs
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keepalive_count = 0
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while not ocr_future.done():
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try:
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cells, columns_meta = await asyncio.wait_for(
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asyncio.shield(ocr_future), timeout=5.0,
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)
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break # OCR finished
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except asyncio.TimeoutError:
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keepalive_count += 1
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elapsed = int(time.time() - t0)
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yield f"data: {json.dumps({'type': 'keepalive', 'elapsed': elapsed, 'message': f'OCR laeuft... ({elapsed}s)'})}\n\n"
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if await request.is_disconnected():
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logger.info(f"SSE batch: client disconnected during OCR for {session_id}")
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ocr_future.cancel()
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return
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else:
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cells, columns_meta = ocr_future.result()
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if await request.is_disconnected():
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logger.info(f"SSE batch: client disconnected after OCR for {session_id}")
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return
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# 4. Apply IPA phonetic fixes
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fix_cell_phonetics(cells, pronunciation=pronunciation)
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# 5. Send columns meta
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if columns_meta:
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yield f"data: {json.dumps({'type': 'columns', 'columns_used': columns_meta})}\n\n"
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# 6. Stream all cells
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for idx, cell in enumerate(cells):
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cell_event = {
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"type": "cell",
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"cell": cell,
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"progress": {"current": idx + 1, "total": len(cells)},
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}
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yield f"data: {json.dumps(cell_event)}\n\n"
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# 7. Build final result and persist
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duration = time.time() - t0
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used_engine = cells[0].get("ocr_engine", "tesseract") if cells else engine
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word_result = {
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"cells": cells,
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"grid_shape": {"rows": n_content_rows, "cols": n_cols, "total_cells": len(cells)},
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"columns_used": columns_meta,
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"layout": "vocab" if is_vocab else "generic",
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"image_width": img_w,
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"image_height": img_h,
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"duration_seconds": round(duration, 2),
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"ocr_engine": used_engine,
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"summary": {
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"total_cells": len(cells),
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"non_empty_cells": sum(1 for c in cells if c.get("text")),
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"low_confidence": sum(1 for c in cells if 0 < c.get("confidence", 0) < 50),
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},
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}
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vocab_entries = None
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has_text_col = 'column_text' in col_types
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if is_vocab or has_text_col:
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entries = _cells_to_vocab_entries(cells, columns_meta)
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entries = _fix_phonetic_brackets(entries, pronunciation=pronunciation)
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word_result["vocab_entries"] = entries
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word_result["entries"] = entries
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word_result["entry_count"] = len(entries)
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word_result["summary"]["total_entries"] = len(entries)
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word_result["summary"]["with_english"] = sum(1 for e in entries if e.get("english"))
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word_result["summary"]["with_german"] = sum(1 for e in entries if e.get("german"))
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vocab_entries = entries
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await update_session_db(session_id, word_result=word_result, current_step=8)
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cached["word_result"] = word_result
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logger.info(f"OCR Pipeline SSE batch: words session {session_id}: "
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f"layout={word_result['layout']}, {len(cells)} cells ({duration:.2f}s)")
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# 8. Send complete event
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complete_event = {
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"type": "complete",
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"summary": word_result["summary"],
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"duration_seconds": round(duration, 2),
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"ocr_engine": used_engine,
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}
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if vocab_entries is not None:
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complete_event["vocab_entries"] = vocab_entries
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yield f"data: {json.dumps(complete_event)}\n\n"
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async def _word_stream_generator(
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session_id: str,
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cached: Dict[str, Any],
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col_regions: List[PageRegion],
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row_geoms: List[RowGeometry],
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dewarped_bgr: np.ndarray,
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engine: str,
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pronunciation: str,
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request: Request,
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):
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"""SSE generator that yields cell-by-cell OCR progress."""
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t0 = time.time()
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ocr_img = create_ocr_image(dewarped_bgr)
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img_h, img_w = dewarped_bgr.shape[:2]
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n_content_rows = len([r for r in row_geoms if r.row_type == 'content'])
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_skip_types = {'column_ignore', 'header', 'footer', 'margin_top', 'margin_bottom', 'margin_left', 'margin_right'}
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n_cols = len([c for c in col_regions if c.type not in _skip_types])
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col_types = {c.type for c in col_regions if c.type not in _skip_types}
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is_vocab = bool(col_types & {'column_en', 'column_de'})
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columns_meta = None
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total_cells = n_content_rows * n_cols
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meta_event = {
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"type": "meta",
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"grid_shape": {"rows": n_content_rows, "cols": n_cols, "total_cells": total_cells},
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"layout": "vocab" if is_vocab else "generic",
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}
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yield f"data: {json.dumps(meta_event)}\n\n"
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yield f"data: {json.dumps({'type': 'preparing', 'message': 'Cell-First OCR wird initialisiert...'})}\n\n"
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all_cells: List[Dict[str, Any]] = []
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cell_idx = 0
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last_keepalive = time.time()
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for cell, cols_meta, total in build_cell_grid_v2_streaming(
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ocr_img, col_regions, row_geoms, img_w, img_h,
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ocr_engine=engine, img_bgr=dewarped_bgr,
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):
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if await request.is_disconnected():
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logger.info(f"SSE: client disconnected during streaming for {session_id}")
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return
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if columns_meta is None:
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columns_meta = cols_meta
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meta_update = {"type": "columns", "columns_used": cols_meta}
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yield f"data: {json.dumps(meta_update)}\n\n"
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all_cells.append(cell)
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cell_idx += 1
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cell_event = {
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"type": "cell",
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"cell": cell,
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"progress": {"current": cell_idx, "total": total},
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}
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yield f"data: {json.dumps(cell_event)}\n\n"
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# All cells done
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duration = time.time() - t0
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if columns_meta is None:
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columns_meta = []
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# Remove all-empty rows
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rows_with_text: set = set()
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for c in all_cells:
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if c.get("text", "").strip():
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rows_with_text.add(c["row_index"])
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before_filter = len(all_cells)
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all_cells = [c for c in all_cells if c["row_index"] in rows_with_text]
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empty_rows_removed = (before_filter - len(all_cells)) // max(n_cols, 1)
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if empty_rows_removed > 0:
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logger.info(f"SSE: removed {empty_rows_removed} all-empty rows after OCR")
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used_engine = all_cells[0].get("ocr_engine", "tesseract") if all_cells else engine
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fix_cell_phonetics(all_cells, pronunciation=pronunciation)
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word_result = {
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"cells": all_cells,
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"grid_shape": {"rows": n_content_rows, "cols": n_cols, "total_cells": len(all_cells)},
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"columns_used": columns_meta,
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"layout": "vocab" if is_vocab else "generic",
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"image_width": img_w,
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"image_height": img_h,
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"duration_seconds": round(duration, 2),
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"ocr_engine": used_engine,
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"summary": {
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"total_cells": len(all_cells),
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"non_empty_cells": sum(1 for c in all_cells if c.get("text")),
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"low_confidence": sum(1 for c in all_cells if 0 < c.get("confidence", 0) < 50),
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},
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}
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vocab_entries = None
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has_text_col = 'column_text' in col_types
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if is_vocab or has_text_col:
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entries = _cells_to_vocab_entries(all_cells, columns_meta)
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entries = _fix_character_confusion(entries)
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entries = _fix_phonetic_brackets(entries, pronunciation=pronunciation)
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word_result["vocab_entries"] = entries
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word_result["entries"] = entries
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word_result["entry_count"] = len(entries)
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word_result["summary"]["total_entries"] = len(entries)
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word_result["summary"]["with_english"] = sum(1 for e in entries if e.get("english"))
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word_result["summary"]["with_german"] = sum(1 for e in entries if e.get("german"))
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vocab_entries = entries
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await update_session_db(session_id, word_result=word_result, current_step=8)
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cached["word_result"] = word_result
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logger.info(f"OCR Pipeline SSE: words session {session_id}: "
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f"layout={word_result['layout']}, "
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f"{len(all_cells)} cells ({duration:.2f}s)")
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complete_event = {
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"type": "complete",
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"summary": word_result["summary"],
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"duration_seconds": round(duration, 2),
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"ocr_engine": used_engine,
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}
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if vocab_entries is not None:
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complete_event["vocab_entries"] = vocab_entries
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yield f"data: {json.dumps(complete_event)}\n\n"
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