File size: 6,748 Bytes
d423504 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | """MVP closed-loop runner: router → parser → quality scorer → JSONL.
This is the tiniest possible end-to-end harness for the pdfsys pipeline.
Given a directory of PDFs, it:
1. runs :class:`pdfsys_router.Router` to pick a backend per document;
2. for PDFs routed to ``Backend.MUPDF``, runs :func:`pdfsys_parser_mupdf.extract_doc`
to produce an :class:`pdfsys_core.ExtractedDoc`;
3. scores the resulting Markdown with :class:`pdfsys_bench.OcrQualityScorer`
(the ModernBERT-large regression head from FinePDFs);
4. writes one JSON line per PDF to an output file with routing decision,
extraction stats, and quality score.
PDFs routed to ``PIPELINE`` / ``VLM`` / ``DEFERRED`` are recorded with
their routing decision but skipped for extraction — those backends are
not implemented yet in this MVP.
"""
from __future__ import annotations
import json
import time
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Iterable
from pdfsys_core import Backend
from pdfsys_parser_mupdf import extract_doc
from pdfsys_router import Router
from .quality import OcrQualityScorer, QualityScore
@dataclass(slots=True)
class LoopResult:
"""Per-PDF result row, serialized to JSONL."""
pdf_path: str
sha256: str | None
backend: str
ocr_prob: float
num_pages: int
is_form: bool
garbled_text_ratio: float
router_error: str | None
extract_stats: dict[str, Any] = field(default_factory=dict)
extract_error: str | None = None
quality_score: float | None = None
quality_num_chars: int | None = None
quality_num_tokens: int | None = None
quality_model: str | None = None
markdown_chars: int = 0
wall_ms_router: float = 0.0
wall_ms_extract: float = 0.0
wall_ms_quality: float = 0.0
def to_json_line(self) -> str:
return json.dumps(asdict(self), ensure_ascii=False)
def _iter_pdfs(root: Path, limit: int | None) -> Iterable[Path]:
pdfs = sorted(p for p in root.rglob("*.pdf") if p.is_file())
if limit is not None:
pdfs = pdfs[:limit]
yield from pdfs
def run_loop(
pdf_dir: str | Path,
out_path: str | Path,
*,
limit: int | None = None,
score_quality: bool = True,
router_weights: str | Path | None = None,
quality_model: str = "HuggingFaceFW/finepdfs_ocr_quality_classifier_eng_Latn",
markdown_dir: str | Path | None = None,
ocr_threshold: float = 0.5,
) -> dict[str, Any]:
"""Drive the full MVP loop over a PDF directory.
Returns an aggregate summary dict. Individual result rows are written
to ``out_path`` as JSONL (one line per PDF, in input-order).
"""
pdf_dir = Path(pdf_dir)
out_path = Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
router = Router(model_path=router_weights, ocr_threshold=ocr_threshold)
scorer = OcrQualityScorer(model_name=quality_model) if score_quality else None
md_root = Path(markdown_dir) if markdown_dir else None
if md_root is not None:
md_root.mkdir(parents=True, exist_ok=True)
summary: dict[str, Any] = {
"pdf_dir": str(pdf_dir),
"out_path": str(out_path),
"num_pdfs": 0,
"by_backend": {},
"num_extracted": 0,
"num_scored": 0,
"num_errors": 0,
"sum_quality": 0.0,
"started_at": time.time(),
}
with out_path.open("w", encoding="utf-8") as out_f:
for pdf_path in _iter_pdfs(pdf_dir, limit):
row = _run_one(
pdf_path=pdf_path,
router=router,
scorer=scorer,
md_root=md_root,
)
out_f.write(row.to_json_line() + "\n")
out_f.flush()
summary["num_pdfs"] += 1
by_b = summary["by_backend"]
by_b[row.backend] = by_b.get(row.backend, 0) + 1
if row.extract_error is None and row.backend == Backend.MUPDF.value:
summary["num_extracted"] += 1
if row.quality_score is not None:
summary["num_scored"] += 1
summary["sum_quality"] += row.quality_score
if row.router_error or row.extract_error:
summary["num_errors"] += 1
summary["finished_at"] = time.time()
summary["wall_seconds"] = summary["finished_at"] - summary["started_at"]
summary["avg_quality"] = (
summary["sum_quality"] / summary["num_scored"] if summary["num_scored"] else None
)
summary_path = out_path.with_suffix(".summary.json")
summary_path.write_text(json.dumps(summary, indent=2, ensure_ascii=False))
summary["summary_path"] = str(summary_path)
return summary
def _run_one(
*,
pdf_path: Path,
router: Router,
scorer: OcrQualityScorer | None,
md_root: Path | None,
) -> LoopResult:
# -- Stage-A routing ------------------------------------------------------
t0 = time.perf_counter()
decision = router.classify(pdf_path)
t1 = time.perf_counter()
row = LoopResult(
pdf_path=str(pdf_path),
sha256=None,
backend=decision.backend.value,
ocr_prob=decision.ocr_prob,
num_pages=decision.num_pages,
is_form=decision.is_form,
garbled_text_ratio=decision.garbled_text_ratio,
router_error=decision.error,
wall_ms_router=(t1 - t0) * 1000.0,
)
# -- MVP only extracts the text-ok fast path ------------------------------
if decision.backend != Backend.MUPDF:
return row
try:
t2 = time.perf_counter()
extracted = extract_doc(pdf_path)
t3 = time.perf_counter()
row.sha256 = extracted.sha256
row.extract_stats = dict(extracted.stats)
row.markdown_chars = extracted.char_count
row.wall_ms_extract = (t3 - t2) * 1000.0
except Exception as e: # noqa: BLE001
row.extract_error = f"extract_failed: {e}"
return row
if md_root is not None and extracted.markdown:
md_path = md_root / f"{extracted.sha256}.md"
md_path.write_text(extracted.markdown, encoding="utf-8")
# -- Quality scoring ------------------------------------------------------
if scorer is not None and extracted.markdown:
try:
t4 = time.perf_counter()
q: QualityScore = scorer.score(extracted.markdown)
t5 = time.perf_counter()
row.quality_score = q.score
row.quality_num_chars = q.num_chars
row.quality_num_tokens = q.num_tokens
row.quality_model = q.model
row.wall_ms_quality = (t5 - t4) * 1000.0
except Exception as e: # noqa: BLE001
row.extract_error = f"quality_failed: {e}"
return row
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