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[split-required] Split final 43 files (500-668 LOC) to complete refactoring

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klausur-service (11 files):
- cv_gutter_repair, ocr_pipeline_regression, upload_api
- ocr_pipeline_sessions, smart_spell, nru_worksheet_generator
- ocr_pipeline_overlays, mail/aggregator, zeugnis_api
- cv_syllable_detect, self_rag

backend-lehrer (17 files):
- classroom_engine/suggestions, generators/quiz_generator
- worksheets_api, llm_gateway/comparison, state_engine_api
- classroom/models (→ 4 submodules), services/file_processor
- alerts_agent/api/wizard+digests+routes, content_generators/pdf
- classroom/routes/sessions, llm_gateway/inference
- classroom_engine/analytics, auth/keycloak_auth
- alerts_agent/processing/rule_engine, ai_processor/print_versions

agent-core (5 files):
- brain/memory_store, brain/knowledge_graph, brain/context_manager
- orchestrator/supervisor, sessions/session_manager

admin-lehrer (5 components):
- GridOverlay, StepGridReview, DevOpsPipelineSidebar
- DataFlowDiagram, sbom/wizard/page

website (2 files):
- DependencyMap, lehrer/abitur-archiv

Other: nibis_ingestion, grid_detection_service, export-doclayout-onnx

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
2026-04-25 09:41:42 +02:00
parent 451365a312
commit bd4b956e3c
113 changed files with 13790 additions and 14148 deletions
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klausur-service/backend/cv_syllable_merge.py Normal file
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"""
Syllable Merge — word gap merging, syllabification, divider insertion.
Extracted from cv_syllable_detect.py for modularity.
Lizenz: Apache 2.0 (kommerziell nutzbar)
DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
"""
import logging
import re
from typing import Any, Dict, List, Optional
import numpy as np
from cv_syllable_core import (
_get_hyphenators,
_hyphenate_word,
_IPA_RE,
_STOP_WORDS,
)
logger = logging.getLogger(__name__)
def _try_merge_pipe_gaps(text: str, hyph_de) -> str:
"""Merge fragments separated by single spaces where OCR split at a pipe.
Example: "Kaf fee" -> "Kaffee" (pyphen recognizes the merged word).
Multi-step: "Ka bel jau" -> "Kabel jau" -> "Kabeljau".
Guards against false merges:
- The FIRST token must be pure alpha (word start -- no attached punctuation)
- The second token may have trailing punctuation (comma, period) which
stays attached to the merged word: "Ka" + "fer," -> "Kafer,"
- Common German function words (der, die, das, ...) are never merged
- At least one fragment must be very short (<=3 alpha chars)
"""
parts = text.split(' ')
if len(parts) < 2:
return text
result = [parts[0]]
i = 1
while i < len(parts):
prev = result[-1]
curr = parts[i]
# Extract alpha-only core for lookup
prev_alpha = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', prev)
curr_alpha = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', curr)
# Guard 1: first token must be pure alpha (word-start fragment)
# second token may have trailing punctuation
# Guard 2: neither alpha core can be a common German function word
# Guard 3: the shorter fragment must be <= 3 chars (pipe-gap signal)
# Guard 4: combined length must be >= 4
should_try = (
prev == prev_alpha # first token: pure alpha (word start)
and prev_alpha and curr_alpha
and prev_alpha.lower() not in _STOP_WORDS
and curr_alpha.lower() not in _STOP_WORDS
and min(len(prev_alpha), len(curr_alpha)) <= 3
and len(prev_alpha) + len(curr_alpha) >= 4
)
if should_try:
merged_alpha = prev_alpha + curr_alpha
hyph = hyph_de.inserted(merged_alpha, hyphen='-')
if '-' in hyph:
# pyphen recognizes merged word -- collapse the space
result[-1] = prev + curr
i += 1
continue
result.append(curr)
i += 1
return ' '.join(result)
def merge_word_gaps_in_zones(zones_data: List[Dict], session_id: str) -> int:
"""Merge OCR word-gap fragments in cell texts using pyphen validation.
OCR often splits words at syllable boundaries into separate word_boxes,
producing text like "zerknit tert" instead of "zerknittert". This
function tries to merge adjacent fragments in every content cell.
More permissive than ``_try_merge_pipe_gaps`` (threshold 5 instead of 3)
but still guarded by pyphen dictionary lookup and stop-word exclusion.
Returns the number of cells modified.
"""
hyph_de, _ = _get_hyphenators()
if hyph_de is None:
return 0
modified = 0
for z in zones_data:
for cell in z.get("cells", []):
ct = cell.get("col_type", "")
if not ct.startswith("column_"):
continue
text = cell.get("text", "")
if not text or " " not in text:
continue
# Skip IPA cells
text_no_brackets = re.sub(r'\[[^\]]*\]', '', text)
if _IPA_RE.search(text_no_brackets):
continue
new_text = _try_merge_word_gaps(text, hyph_de)
if new_text != text:
cell["text"] = new_text
modified += 1
if modified:
logger.info(
"build-grid session %s: merged word gaps in %d cells",
session_id, modified,
)
return modified
def _try_merge_word_gaps(text: str, hyph_de) -> str:
"""Merge OCR word fragments with relaxed threshold (max_short=5).
Similar to ``_try_merge_pipe_gaps`` but allows slightly longer fragments
(max_short=5 instead of 3). Still requires pyphen to recognize the
merged word.
"""
parts = text.split(' ')
if len(parts) < 2:
return text
result = [parts[0]]
i = 1
while i < len(parts):
prev = result[-1]
curr = parts[i]
prev_alpha = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', prev)
curr_alpha = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', curr)
should_try = (
prev == prev_alpha
and prev_alpha and curr_alpha
and prev_alpha.lower() not in _STOP_WORDS
and curr_alpha.lower() not in _STOP_WORDS
and min(len(prev_alpha), len(curr_alpha)) <= 5
and len(prev_alpha) + len(curr_alpha) >= 4
)
if should_try:
merged_alpha = prev_alpha + curr_alpha
hyph = hyph_de.inserted(merged_alpha, hyphen='-')
if '-' in hyph:
result[-1] = prev + curr
i += 1
continue
result.append(curr)
i += 1
return ' '.join(result)
def _syllabify_text(text: str, hyph_de, hyph_en) -> str:
"""Syllabify all significant words in a text string.
1. Strip existing | dividers
2. Merge pipe-gap spaces where possible
3. Apply pyphen to each word >= 3 alphabetic chars
4. Words pyphen doesn't recognize stay as-is (no bad guesses)
"""
if not text:
return text
# Skip cells that contain IPA transcription characters outside brackets.
text_no_brackets = re.sub(r'\[[^\]]*\]', '', text)
if _IPA_RE.search(text_no_brackets):
return text
# Phase 1: strip existing pipe dividers for clean normalization
clean = text.replace('|', '')
# Phase 2: merge pipe-gap spaces (OCR fragments from pipe splitting)
clean = _try_merge_pipe_gaps(clean, hyph_de)
# Phase 3: tokenize and syllabify each word
# Split on whitespace and comma/semicolon sequences, keeping separators
tokens = re.split(r'(\s+|[,;:]+\s*)', clean)
result = []
for tok in tokens:
if not tok or re.match(r'^[\s,;:]+$', tok):
result.append(tok)
continue
# Strip trailing/leading punctuation for pyphen lookup
m = re.match(r'^([^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]*)(.*?)([^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]*)$', tok)
if not m:
result.append(tok)
continue
lead, word, trail = m.group(1), m.group(2), m.group(3)
if len(word) < 3 or not re.search(r'[a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df]', word):
result.append(tok)
continue
hyph = _hyphenate_word(word, hyph_de, hyph_en)
if hyph:
result.append(lead + hyph + trail)
else:
result.append(tok)
return ''.join(result)
def insert_syllable_dividers(
zones_data: List[Dict],
img_bgr: np.ndarray,
session_id: str,
*,
force: bool = False,
col_filter: Optional[set] = None,
) -> int:
"""Insert pipe syllable dividers into dictionary cells.
For dictionary pages: process all content column cells, strip existing
pipes, merge pipe-gap spaces, and re-syllabify using pyphen.
Pre-check: at least 1% of content cells must already contain ``|`` from
OCR. This guards against pages with zero pipe characters.
Args:
force: If True, skip the pipe-ratio pre-check and syllabify all
content words regardless of whether the original has pipe dividers.
col_filter: If set, only process cells whose col_type is in this set.
None means process all content columns.
Returns the number of cells modified.
"""
hyph_de, hyph_en = _get_hyphenators()
if hyph_de is None:
logger.warning("pyphen not installed -- skipping syllable insertion")
return 0
# Pre-check: count cells that already have | from OCR.
if not force:
total_col_cells = 0
cells_with_pipes = 0
for z in zones_data:
for cell in z.get("cells", []):
if cell.get("col_type", "").startswith("column_"):
total_col_cells += 1
if "|" in cell.get("text", ""):
cells_with_pipes += 1
if total_col_cells > 0:
pipe_ratio = cells_with_pipes / total_col_cells
if pipe_ratio < 0.01:
logger.info(
"build-grid session %s: skipping syllable insertion -- "
"only %.1f%% of cells have existing pipes (need >=1%%)",
session_id, pipe_ratio * 100,
)
return 0
insertions = 0
for z in zones_data:
for cell in z.get("cells", []):
ct = cell.get("col_type", "")
if not ct.startswith("column_"):
continue
if col_filter is not None and ct not in col_filter:
continue
text = cell.get("text", "")
if not text:
continue
# In auto mode (force=False), only normalize cells that already
# have | from OCR (i.e. printed syllable dividers on the original
# scan). Don't add new syllable marks to other words.
if not force and "|" not in text:
continue
new_text = _syllabify_text(text, hyph_de, hyph_en)
if new_text != text:
cell["text"] = new_text
insertions += 1
if insertions:
logger.info(
"build-grid session %s: syllable dividers inserted/normalized "
"in %d cells (pyphen)",
session_id, insertions,
)
return insertions