File size: 2,989 Bytes
eef4eed | 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 | from __future__ import annotations
import hashlib
import json
from pathlib import Path
from typing import Any
import pandas as pd
from src.domain import CODE_VERSION, PRODUCT, RULESET_ID
def input_hash(payload: dict[str, Any]) -> str:
raw = json.dumps(payload, sort_keys=True, ensure_ascii=False, default=str)
return hashlib.sha256(raw.encode("utf-8")).hexdigest()[:16]
def public_report(result: dict[str, Any]) -> dict[str, Any]:
kpis = result["kpis"]
return {
"product": PRODUCT,
"code_version": CODE_VERSION,
"ruleset": RULESET_ID,
"run_id": result["run_id"],
"scenario_id": result.get("scenario_id"),
"input_hash": result.get("input_hash"),
"verdict": result["verdict"].value if hasattr(result["verdict"], "value") else result["verdict"],
"kpis": kpis.model_dump() if hasattr(kpis, "model_dump") else kpis,
"comparison": result.get("comparison") or [],
"finding_count": len(result.get("finding_rows") or []),
"engine_profile": result.get("engine_profile"),
"policy_action": result.get("policy_action"),
"limits": (
"Detector-scope redaction only. This report does not claim that every sensitive "
"value was removed. Scanned PDFs and production identity data are out of scope."
),
}
def write_downloads(result: dict[str, Any], folder: Path) -> dict[str, str]:
folder.mkdir(parents=True, exist_ok=True)
run_id = result["run_id"]
files = {
"documents": folder / f"{run_id}_documents.csv",
"findings": folder / f"{run_id}_findings.csv",
"spans": folder / f"{run_id}_spans.csv",
"comparison": folder / f"{run_id}_comparison.csv",
"report": folder / f"{run_id}_report.json",
"redacted": folder / f"{run_id}_redacted.txt",
}
pd.DataFrame(result.get("doc_rows") or []).to_csv(files["documents"], index=False)
pd.DataFrame(result.get("finding_rows") or []).to_csv(files["findings"], index=False)
pd.DataFrame(result.get("span_rows") or []).to_csv(files["spans"], index=False)
pd.DataFrame(result.get("comparison") or []).to_csv(files["comparison"], index=False)
files["report"].write_text(
json.dumps(public_report(result), indent=2, ensure_ascii=False, default=str),
encoding="utf-8",
newline="\n",
)
files["redacted"].write_text(result.get("redacted_text") or "", encoding="utf-8", newline="\n")
exports = result.get("exports") or {}
for doc_id, payload in exports.items():
if payload.get("pdf"):
pdf_path = folder / f"{run_id}_{doc_id}.pdf"
pdf_path.write_bytes(payload["pdf"])
files[f"pdf_{doc_id}"] = pdf_path
text_path = folder / f"{run_id}_{doc_id}.txt"
text_path.write_text(payload.get("text") or "", encoding="utf-8", newline="\n")
files[f"text_{doc_id}"] = text_path
return {key: str(path) for key, path in files.items()}
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