33b38aacc7
* Phase 3: PP-Structure table extraction + personnel column mapper Adds the personnel-table stage of the pipeline. PaddleOCR's PP-Structure recognizes table regions and emits HTML, which we parse into a 2D cell grid. A separate column mapper detects the header row, classifies each column to a canonical PersonnelEntry field via a synonym dictionary, and walks the data rows. Variant handling: - Different satuan use different column orders and header phrasing. Supported synonyms for each canonical field are listed in pipeline/extract/personnel.py (Pangkat / NRP / Pangkat-NRP combo / Nama / Jabatan dalam Dinas / Jabatan dalam Sprint / Keterangan). - A merged 'PANGKAT NRP' or 'PANGKAT NRP NAMA' cell is split using the 8-digit NRP regex (with look-arounds so glued forms like 'BRIPKA98050505' work) and the master pangkat lookup. - Unknown ranks are kept verbatim so the validation layer can flag them as UNKNOWN_PANGKAT for HITL review. - Rows without nrp AND nama are dropped (separators / merged cells). New module pipeline/table.py: - DetectedTable dataclass (cells + html). - parse_table_html: tag/entity-tolerant HTML -> 2D grid. - extract_tables_from_pp_result: filter PP-Structure regions to type=table. - run_table_extraction: top-level entrypoint with lazy-init singleton for the heavy PP-Structure engine. Orchestrator now invokes table extraction (gated by TABLES_ENABLED) on every preprocessed page and merges the discovered personnel into the ExtractionResult. Failures are caught and logged so a flaky table recognizer never blocks header extraction. Tests: 38 new unit tests covering HTML parsing, region filtering, header classification, column mapping (split, combined, glued cells), and end-to-end personnel extraction. Total 108 tests, all green. PaddleOCR / PP-Structure remain optional - no test imports them. Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> * Phase 3: fix header misclassification for combined Pangkat/NRP/Nama columns Devin Review caught two related bugs in personnel column mapping: 1. _classify_header_cell iterated _HEADER_SYNONYMS in insertion order when falling back to substring matching. The dict listed shorter keywords first ('pangkat' before 'pangkat / nrp'), so a header like 'Pangkat / NRP / Nama' classified as plain 'pangkat'. map_row then tried to normalize the whole '"AKP 87010101 Budi Santoso"' cell as a rank, normalize_pangkat returned None, and the row failed the nrp-or-nama gate at the bottom of map_row -- silently dropping every personnel row in tables using this layout. 2. _split_pangkat_nrp_nama existed and was unit-tested but was never wired up in map_row, so even if classification had worked, the three-way split would not have run. The module docstring claimed the split was supported. Fix: - Iterate the synonym table sorted by keyword length descending in the substring-match fallback so the most specific synonym wins. - Add 'pangkat_nrp_nama' synonym entries for typical separators (' / ', '/', whitespace, comma). - Wire 'pangkat_nrp_nama' into map_row using the existing helper. - Update is_personnel_table so combined headers count as both an id signal and a name signal. Tests: 6 new asserts (parametrized), 1 regression test for triple- combined header end-to-end, 1 dedicated map_row test for the new column type. 114 tests total, all green. Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> * Phase 3: handle multi-word Polri ranks in _split_pangkat_nrp_nama Devin Review caught: token-by-token is_valid_pangkat() check could not recognize multi-word ranks ('KOMBES POL', 'BRIGJEN POL', 'IRJEN POL', 'KOMJEN POL', 'JENDERAL POL'). For 'KOMBES POL 88123456 John Doe' the old code returned pangkat=None, nama='KOMBES POL John Doe', and the validator's UNKNOWN_PANGKAT flag never fired because pangkat was falsy. New behavior: greedy longest-prefix match. After stripping the NRP we try the leading 3-token, 2-token, 1-token slice against normalize_pangkat() and take the longest that maps to a canonical rank. Tokens after the matched rank become the nama. Unknown ranks fall through to pangkat=None and the rank text stays in the nama field, where downstream validation already flags the row. Tests: 5 new asserts (4 multi-word ranks + 1 unknown-rank fallback), 119 total green. Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> * Phase 3: don't count pangkat_nrp as a name signal in is_personnel_table Devin Review caught: a table with header ['No', 'Pangkat / NRP', 'Jabatan'] (no name column) was wrongly classified as a personnel table because pangkat_nrp was lumped into has_name. Such a table would produce PersonnelEntry rows with nama=None passing the nrp-or- nama gate, polluting the personel[] output with id-only fragments. Split the combined-cell set into combined_id (counts toward has_id) and combined_name (counts toward has_name). Only pangkat_nrp_nama, which actually embeds a name, qualifies for has_name. pangkat_nrp remains an id-only signal. Tests: 3 new asserts (rejects id-only, accepts pangkat_nrp + separate nama, accepts pangkat_nrp_nama). 122 total green. Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com>
52 lines
1.5 KiB
Plaintext
52 lines
1.5 KiB
Plaintext
# ==== App ====
|
|
APP_ENV=local # local | dev | staging | prod
|
|
APP_HOST=0.0.0.0
|
|
APP_PORT=8000
|
|
APP_LOG_LEVEL=INFO
|
|
|
|
# ==== Storage (Phase 1: local filesystem) ====
|
|
STORAGE_LOCAL_DIR=./storage
|
|
|
|
# ==== OCR ====
|
|
OCR_LANG=latin # PaddleOCR lang code; "latin" works well for Bahasa Indonesia
|
|
OCR_USE_GPU=false # set true if running on a GPU host
|
|
OCR_DET_MODEL_DIR= # leave empty to use PaddleOCR defaults
|
|
OCR_REC_MODEL_DIR=
|
|
OCR_CLS_MODEL_DIR=
|
|
OCR_MAX_IMAGE_SIDE=2200 # downscale longest side before OCR
|
|
|
|
# ==== Preprocessing ====
|
|
PREPROCESS_TARGET_DPI=300
|
|
PREPROCESS_DENOISE=true
|
|
PREPROCESS_DESKEW=true
|
|
PREPROCESS_ADAPTIVE_THRESHOLD=false # turn on for low-quality phone photos
|
|
|
|
# ==== Document detection (Phase 2, IMAGE sources only) ====
|
|
PREPROCESS_DETECT_DOCUMENT=true
|
|
PREPROCESS_REMOVE_SHADOW=true
|
|
PREPROCESS_MIN_QUAD_AREA_FRACTION=0.20
|
|
|
|
# ==== Table extraction (Phase 3, PaddleOCR PP-Structure) ====
|
|
TABLES_ENABLED=true
|
|
|
|
# ==== Confidence / routing (Phase 5) ====
|
|
CONFIDENCE_AUTO_APPROVE=0.95
|
|
CONFIDENCE_NEEDS_REVIEW=0.85
|
|
|
|
# ==== LLM (Phase 5, optional) ====
|
|
LLM_ENABLED=false
|
|
LLM_PROVIDER=ollama
|
|
LLM_MODEL=qwen2.5:1.5b # CPU-friendly default
|
|
LLM_BASE_URL=http://localhost:11434
|
|
LLM_TIMEOUT_S=60
|
|
|
|
# ==== Async pipeline (Phase 4, optional) ====
|
|
QUEUE_ENABLED=false
|
|
REDIS_URL=redis://localhost:6379/0
|
|
DATABASE_URL=postgresql+psycopg://ocr:ocr@localhost:5432/ocr_sprint
|
|
MINIO_ENDPOINT=localhost:9000
|
|
MINIO_ACCESS_KEY=minioadmin
|
|
MINIO_SECRET_KEY=minioadmin
|
|
MINIO_BUCKET=ocr-sprint
|
|
MINIO_SECURE=false
|