chore: establish project baseline
This commit is contained in:
OpenCode
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"""GTI Fiscal LOCAL COMPLETION V1 runner: snapshot -> dry-run -> guarded apply -> verify -> second pass -> reports/questions.
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python scripts/local_completion.py <stage> [--db NAME ...]
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stages: snapshot | dryrun | apply | verify | second | report | questions | all
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Databases are ONLY those in docs/LOCAL_WRITE_ALLOWLIST_V1.json (exact canonical paths). Writes go through
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scripts/firebird_write_executor.execute_guarded_product_update (path allowlist, schema/type/length guard, compare-before-write,
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one UPDATE per product, rowcount, independent re-read). Nothing is written by any stage except `apply` (and `second`, which must write 0).
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State lives in docs/runs/local_completion/ (git-ignored bulk files).
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"""
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import argparse
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import gzip
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import hashlib
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import importlib.util
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import json
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import os
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import pickle
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import subprocess
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import sys
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import tempfile
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from collections import Counter, defaultdict
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from datetime import datetime, timezone
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(ROOT)); sys.path.insert(0, str(ROOT / "scripts")); sys.path.insert(0, str(ROOT / "agent" / "src"))
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import fdb # noqa: E402
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import run_fiscal_decision_v1 as base # noqa: E402
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from central.app.field_contract import ALREADY, CHANGE, CONTRACT, CONTRACT_VERSION, CORRECTED, MANUAL, NA, WRITE_FIELDS, Context, resolve_product # noqa: E402
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from gti_fiscal_agent import company_context as cc # noqa: E402
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from gti_fiscal_agent.coverage import build_refs # noqa: E402
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from gti_fiscal_agent.firebird_reader import read_evidence # noqa: E402
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STATE = ROOT / "docs" / "runs" / "local_completion"
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HUB = "http://localhost:8000"
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# Per-database company configuration (operator decisions recorded in docs/AGENT_COMPANY_CONTEXT_RULES_V1.md): the three databases with a CRT conflict use the
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# emission authority EMISSOR; the company is always the C000004 row 000001 (the one whose CNPJ equals the EMISSOR CNPJ).
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COMPANY_CFG = {"CONSTRUCAO": {"company_code": "000001"}, "MMMODAS": {"company_code": "000001", "crt_source": "EMISSOR"}, "RINCAO": {"company_code": "000001", "crt_source": "EMISSOR"},
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"UNIAO": {"company_code": "000001", "crt_source": "EMISSOR"}, "TESTESOFICIAIS": {}}
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BASE_COLS = ["CODIGO", "PRODUTO", "CODGRUPO", "TIPO", "PROD_COZINHA", "PRODUTO_SERVICO"]
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s_ = lambda v: None if v is None else str(v).strip() # noqa: E731
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def load_executor():
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spec = importlib.util.spec_from_file_location("firebird_write_executor", ROOT / "scripts" / "firebird_write_executor.py")
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mod = importlib.util.module_from_spec(spec); sys.modules[spec.name] = mod; spec.loader.exec_module(mod)
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return mod
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def allowlist():
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doc = json.loads((ROOT / "docs" / "LOCAL_WRITE_ALLOWLIST_V1.json").read_text(encoding="utf-8"))
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return {d["name"]: d["path"] for d in doc["databases"]}
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def connect(path, rw=False):
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kw = dict(database=path, user=os.environ["FB_USER"], password=os.environ["FB_PASSWORD"], fb_library_name=os.environ.get("FIREBIRD_CLIENT_LIBRARY"))
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if not rw:
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kw["isolation_level"] = fdb.ISOLATION_LEVEL_READ_COMMITED_RO
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return fdb.connect(**kw)
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def env_for(path):
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return {"FIREBIRD_DATABASE_PATH": path, "FB_USER": os.environ["FB_USER"], "FB_PASSWORD": os.environ["FB_PASSWORD"], "FIREBIRD_CLIENT_LIBRARY": os.environ.get("FIREBIRD_CLIENT_LIBRARY")}
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# ------------------------------------------------------------------------------------------------------------------ snapshot
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def snapshot(name, path):
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conn = connect(path); cur = conn.cursor()
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cur.execute("SELECT TRIM(rf.RDB$FIELD_NAME), f.RDB$FIELD_TYPE FROM RDB$RELATION_FIELDS rf JOIN RDB$FIELDS f ON f.RDB$FIELD_NAME = rf.RDB$FIELD_SOURCE WHERE rf.RDB$RELATION_NAME = 'C000025' ORDER BY rf.RDB$FIELD_POSITION")
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cols = [c for c, t in cur.fetchall() if t != 261] # blobs (images) excluded
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cur.execute(f"SELECT {', '.join(cols)} FROM C000025 ORDER BY CODIGO")
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rows = {s_(r[cols.index("CODIGO")]): tuple(r) for r in cur.fetchall()}
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side = {}
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for t in ("C000004", "EMISSOR"):
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try:
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cur.execute(f"SELECT * FROM {t}")
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names = [d[0] for d in cur.description]
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data = [tuple(v.read() if hasattr(v, "read") else v for v in r) for r in cur.fetchall()]
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side[t] = {"columns": names, "rows": data, "sha256": hashlib.sha256(repr(data).encode("utf-8", "replace")).hexdigest()}
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except Exception as e: # noqa: BLE001
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side[t] = {"error": type(e).__name__}
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company = cc.resolve(cc.collect_evidence(cur), cc.config_from_env({k.upper(): v for k, v in _cfg_env(name).items()}))
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conn.close()
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snap = {"name": name, "path": path, "taken": datetime.now(timezone.utc).isoformat(timespec="seconds"), "columns": cols, "rows": rows, "side_tables": side,
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"product_count": len(rows), "products_logical_sha256": hashlib.sha256(repr(sorted(rows.items())).encode("utf-8", "replace")).hexdigest(),
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"company": {k: company.get(k) for k in ("status", "classification", "company_code", "company_cnpj", "company_uf", "company_crt", "source_of_truth", "confidence", "warnings", "reasons")}}
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STATE.mkdir(parents=True, exist_ok=True)
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with gzip.open(STATE / f"{name}_before.pkl.gz", "wb") as f:
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pickle.dump(snap, f)
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return snap
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def _cfg_env(name):
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cfg = COMPANY_CFG.get(name, {})
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out = {}
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if cfg.get("company_code"): out["COMPANY_CODE"] = cfg["company_code"]
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if cfg.get("crt_source"): out["COMPANY_CRT_SOURCE"] = cfg["crt_source"]
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return out
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def load_snapshot(name):
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with gzip.open(STATE / f"{name}_before.pkl.gz", "rb") as f:
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return pickle.load(f)
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# ------------------------------------------------------------------------------------------------------------------ analysis
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def classify_pairs(pairs):
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pairs = sorted(set(pairs))
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with tempfile.TemporaryDirectory() as tmp:
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fin, fout = Path(tmp) / "in.json", Path(tmp) / "out.json"
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fin.write_text(json.dumps(pairs, ensure_ascii=False), encoding="utf-8")
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subprocess.run(["docker", "cp", str(ROOT / "scripts" / "bulk_classify_in_container.py"), "projeto-fiscal-hub-1:/tmp/bulk.py"], check=True)
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subprocess.run(["docker", "cp", str(fin), "projeto-fiscal-hub-1:/tmp/in.json"], check=True)
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subprocess.run(["docker", "exec", "projeto-fiscal-hub-1", "sh", "-c", "cd /app && python /tmp/bulk.py /tmp/in.json /tmp/out.json"], check=True, capture_output=True)
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subprocess.run(["docker", "cp", "projeto-fiscal-hub-1:/tmp/out.json", str(fout)], check=True)
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return dict(zip(pairs, json.loads(fout.read_text(encoding="utf-8"))))
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def hub_get(path, token):
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return base.http(HUB + path, token)
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def analyze(name, path, token, snap=None, extra_context=None):
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"""Read the database (read-only) and resolve every product/field. Returns the full analysis."""
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conn = connect(path); cur = conn.cursor()
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cur.execute("SELECT TRIM(RDB$FIELD_NAME) FROM RDB$RELATION_FIELDS WHERE RDB$RELATION_NAME = 'C000025'")
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have = {r[0] for r in cur.fetchall()}
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cols = [c for c in BASE_COLS + WRITE_FIELDS if c in have]
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cur.execute(f"SELECT {', '.join(cols)} FROM C000025 ORDER BY CODIGO")
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products = []
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for r in cur.fetchall():
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p = {c: None for c in BASE_COLS + WRITE_FIELDS}
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p.update(zip(cols, r))
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for k in p:
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if isinstance(p[k], str):
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p[k] = p[k].strip()
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products.append(p)
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company = cc.resolve(cc.collect_evidence(cur), cc.config_from_env(_cfg_env(name)))
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ev_rows = cc.collect_evidence(cur)
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sel = next((c for c in ev_rows["c000004"] if c["code"] == company["company_code"]), {})
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groups = {}
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if "C000017" in {r[0] for r in _tables(cur)}:
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cur.execute("SELECT TRIM(CODIGO), TRIM(GRUPO) FROM C000017")
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groups = dict(cur.fetchall())
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contrib = None
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if "CONTRIBUINTE_IPI" in _cols(cur, "C000004"):
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cur.execute("SELECT CODIGO, CONTRIBUINTE_IPI FROM C000004")
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contrib = {s_(a): s_(b) for a, b in cur.fetchall()}.get(company["company_code"])
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conn.close()
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if company["status"] != "PASS":
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raise RuntimeError(f"{name}: company context {company['classification']}: {company['reasons']}")
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comp = {"uf": company["company_uf"], "crt": company["company_crt"], "contribuinte_ipi": contrib, "company_code": company["company_code"], "company_key": company["company_cnpj_fingerprint"]}
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# ERP-emission evidence: the SGV NFC-e history shows whether the PIS/COFINS cadastral fields are consumed at emission (issued CST_PIS_COFINS non-blank share)
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try:
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_c = connect(path); _cur = _c.cursor()
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_cur.execute("SELECT COUNT(*), COUNT(NULLIF(TRIM(CST_PIS_COFINS), '')) FROM NFCE_ITENS")
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tot, nonblank = _cur.fetchone(); _c.close()
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comp["pis_cofins_emission_items"] = tot; comp["pis_cofins_emission_nonblank"] = nonblank
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comp["pis_cofins_unused_by_emission"] = bool(tot >= 1000 and nonblank / tot < 0.001)
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except Exception as exc: # noqa: BLE001
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comp["pis_cofins_unused_by_emission"] = False; comp["pis_cofins_evidence_error"] = str(exc)[:120]
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evidence = read_evidence(env_for(path), company["company_code"])
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refs = build_refs(lambda p: hub_get(p, token), products, comp["uf"])
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entries = list(extra_context or [])
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try:
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entries += hub_get(f"/v1/context?company_key={comp['company_key']}", token)["entries"]
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except Exception: # noqa: BLE001
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pass
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ctx = Context(entries)
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manual = {}
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for p in products:
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v, _ = ctx.get("ncm", s_(p["CODIGO"]))
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if v:
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manual[s_(p["CODIGO"])] = v
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pairs = [(s_(p["CLASSIFICACAO_FISCAL"]) or "", s_(p["PRODUTO"]) or "") for p in products] + [(v, s_(p["PRODUTO"]) or "") for p in products for v in [manual.get(s_(p["CODIGO"]))] if v]
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dec = classify_pairs(pairs)
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results = {}
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dup_codes = {c: n for c, n in Counter(s_(p["CODIGO"]) for p in products).items() if n > 1}
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seen = Counter()
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for p in products:
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code = s_(p["CODIGO"]); desc = s_(p["PRODUTO"]) or ""
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seen[code] += 1
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rk = code if seen[code] == 1 else f"{code}#{seen[code]}"
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p["_rk"] = rk
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m = manual.get(code)
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md = dec[(m, desc)] if m else None
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valid_manual = bool(md and md.get("status") == "EXACT_NCM")
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d = md if valid_manual else dec[(s_(p["CLASSIFICACAO_FISCAL"]) or "", desc)]
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results[rk] = resolve_product(p, d, comp, refs, evidence, ctx, manual_ncm=(m, valid_manual) if m else None)
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if dup_codes.get(code, 1) > 1: # data-integrity defect (PK violation in the copy): every physical row that shares the code is blocked, all 23 cells stay accounted
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for f in WRITE_FIELDS:
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r = results[rk][f]
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r.update(state=MANUAL, reason="DUPLICATE_KEY_BLOCKED", input_key="duplicate_code", input_scope="PRODUCT", write_eligible=False, proposed=None)
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results[rk]["_ds"] = {k: (v or "")[:8] for k, v in (d.get("dataset_versions") or {}).items() if k in ("ncm", "tipi_ipi", "cest", "ba_st_mva", "cfop", "nfe_moc_tables")}
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return {"name": name, "path": path, "products": products, "company": comp, "company_result": company, "results": results, "groups": groups, "context_entries": len(entries), "engine": {"contract": CONTRACT_VERSION}}
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def _tables(cur):
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cur.execute("SELECT TRIM(RDB$RELATION_NAME) FROM RDB$RELATIONS WHERE COALESCE(RDB$SYSTEM_FLAG,0)=0")
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return cur.fetchall()
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def _cols(cur, table):
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cur.execute("SELECT TRIM(RDB$FIELD_NAME) FROM RDB$RELATION_FIELDS WHERE RDB$RELATION_NAME = ?", (table,))
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return {r[0] for r in cur.fetchall()}
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def summarize(an):
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per = {f: Counter() for f in WRITE_FIELDS}
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reasons = {f: Counter() for f in WRITE_FIELDS}
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manual_keys = Counter()
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for code, res in an["results"].items():
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for f in WRITE_FIELDS:
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r = res[f]
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per[f][r["state"]] += 1
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reasons[f][(r["state"], r["reason"])] += 1
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if r["state"] == MANUAL:
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manual_keys[(f, r["input_key"])] += 1
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roles = Counter(res["_facts"]["role"] for res in an["results"].values())
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return {"name": an["name"], "total_products": len(an["products"]), "company": an["company"], "context_entries": an["context_entries"],
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"fields": {f: dict(per[f]) for f in WRITE_FIELDS}, "reasons": {f: {f"{s}|{r}": n for (s, r), n in reasons[f].most_common(12)} for f in WRITE_FIELDS},
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"manual_inputs": {f"{f}|{k}": n for (f, k), n in manual_keys.most_common()}, "roles": dict(roles),
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"change_required_cells": sum(per[f][CHANGE] for f in WRITE_FIELDS), "write_eligible_cells": sum(1 for res in an["results"].values() for f in WRITE_FIELDS if res[f]["write_eligible"])}
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def write_ndjson(name, an, suffix):
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with gzip.open(STATE / f"{name}_{suffix}.ndjson.gz", "wt", encoding="utf-8") as f:
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for p in an["products"]:
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code = s_(p["CODIGO"]); res = an["results"][p["_rk"]]
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for fld in WRITE_FIELDS:
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r = res[fld]
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f.write(json.dumps({"db": an["name"], "codigo": code, "row_key": p["_rk"], "field": fld, "before": r["current"], "proposed": r["proposed"], "status": r["state"], "reason": r["reason"], "input_key": r["input_key"],
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"provenance": f"{CONTRACT_VERSION}|" + ",".join(f"{k}:{v}" for k, v in res["_ds"].items()) + f"|role={res['_facts']['role']}", "write_eligible": r["write_eligible"]}, ensure_ascii=False, default=str) + "\n")
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# ------------------------------------------------------------------------------------------------------------------ apply / verify
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def apply(name, path, an, snap):
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ex = load_executor()
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ex.assert_local_completion_target(path)
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now_company = cc.resolve(cc.collect_evidence(_ro_cursor(path)), cc.config_from_env(_cfg_env(name)))
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if now_company["status"] != "PASS" or (now_company["company_uf"], now_company["company_crt"]) != (an["company"]["uf"], an["company"]["crt"]):
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raise RuntimeError("company-context guard failed before writing")
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cols = snap["columns"]
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conn = connect(path, rw=True)
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audit, failed, attempted, succeeded = [], [], 0, 0
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try:
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for p in an["products"]:
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code = s_(p["CODIGO"]); res = an["results"][code]
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changes = {}
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for f in WRITE_FIELDS:
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r = res[f]
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if r["write_eligible"]:
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changes[f] = (snap["rows"][code][cols.index(f)], r["proposed"])
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if not changes:
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continue
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attempted += len(changes)
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try:
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rec = ex.execute_guarded_product_update(conn, database_path=path, product_code=code, changes=changes, now=datetime.now(timezone.utc).isoformat(timespec="seconds"))
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audit.append({"db": name, **rec, "outcome": "SUCCEEDED"})
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succeeded += len(changes)
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except ex.WriteRefused as e:
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failed.append({"db": name, "product_code": code, "fields": list(changes), "error": str(e)[:200]})
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finally:
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conn.close()
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with gzip.open(STATE / f"{name}_write_audit.ndjson.gz", "wt", encoding="utf-8") as f:
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for a in audit + [{**x, "outcome": "FAILED"} for x in failed]:
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f.write(json.dumps(a, ensure_ascii=False, default=str) + "\n")
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return {"name": name, "cells_attempted": attempted, "cells_succeeded": succeeded, "cells_failed": sum(len(x["fields"]) for x in failed), "products_updated": len(audit), "failures": failed[:20]}
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def _ro_cursor(path):
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return connect(path).cursor()
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def verify(name, path, an, snap):
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conn = connect(path); cur = conn.cursor()
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cols = snap["columns"]
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cur.execute(f"SELECT {', '.join(cols)} FROM C000025 ORDER BY CODIGO")
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after = {s_(r[cols.index("CODIGO")]): tuple(r) for r in cur.fetchall()}
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conn.close()
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norm = lambda v: None if v is None else (round(float(v), 6) if isinstance(v, (int, float)) or v.__class__.__name__ == "Decimal" else str(v).strip()) # noqa: E731
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planned = verified = bad_planned = unplanned_changes = 0
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problems = []
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for p in an["products"]:
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code = s_(p["CODIGO"]); before = snap["rows"][code]; aft = after.get(code)
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if aft is None:
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problems.append((code, "MISSING_AFTER")); continue
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exp = {f: an["results"][code][f]["proposed"] for f in WRITE_FIELDS if an["results"][code][f]["write_eligible"]}
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for i, c in enumerate(cols):
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if c in exp:
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planned += 1
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if norm(aft[i]) == norm(exp[c]):
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verified += 1
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else:
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bad_planned += 1; problems.append((code, c, "AFTER!=PROPOSED", aft[i], exp[c]))
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elif norm(aft[i]) != norm(before[i]):
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unplanned_changes += 1; problems.append((code, c, "UNPLANNED_CHANGE", before[i], aft[i]))
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return {"name": name, "products_after": len(after), "products_before": len(snap["rows"]), "planned_cells": planned, "post_write_verified": verified, "planned_mismatch": bad_planned,
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"unplanned_changes": unplanned_changes, "problems": problems[:20]}
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# ------------------------------------------------------------------------------------------------------------------ questions
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def questions(analyses):
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groups = defaultdict(lambda: {"products": [], "fields": Counter(), "choices": None, "examples": []})
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for an in analyses:
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by_code = {s_(p["CODIGO"]): p for p in an["products"]}
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for code, res in an["results"].items():
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p = by_code[code]
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for f in WRITE_FIELDS:
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r = res[f]
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if r["state"] != MANUAL:
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continue
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key, scope = r["input_key"], r["input_scope"]
|
||||
if key in ("ncm",):
|
||||
gk = (an["name"], key, "PRODUCT", code, "")
|
||||
elif key in ("cest", "ex_tipi"):
|
||||
ncm = "".join(c for c in (p["CLASSIFICACAO_FISCAL"] or "") if c.isdigit())
|
||||
gk = (an["name"], key, "NCM", ncm, "")
|
||||
elif scope == "COMPANY" or key in ("entry_scope", "rounding", "icms_rate", "credit_rate", "pis_cofins_profile", "crt"):
|
||||
gk = (an["name"], key, "COMPANY", "", "")
|
||||
else:
|
||||
grp = s_(p["CODGRUPO"]) or "(sem grupo)"
|
||||
gk = (an["name"], key, "GROUP", grp, an["groups"].get(grp, ""))
|
||||
g = groups[gk]
|
||||
g["products"].append(code); g["fields"][f] += 1
|
||||
if r.get("choices") and g["choices"] is None:
|
||||
g["choices"] = r["choices"]
|
||||
if len(g["examples"]) < 3:
|
||||
g["examples"].append(f"{code} {s_(p['PRODUTO'])[:40]}")
|
||||
from central.app.context_keys import ENUMS
|
||||
out = []
|
||||
for (db, key, scope, skey, gname), g in sorted(groups.items()):
|
||||
allowed = sorted(ENUMS[key]) if key in ENUMS else ("JSON {pis_in_cst,pis_out_cst,pis_rate,cofins_in_cst,cofins_out_cst,cofins_rate}" if key == "pis_cofins_profile" else "free text (see key)")
|
||||
out.append({"database": db, "company_key": next(a["company"]["company_key"] for a in analyses if a["name"] == db), "key": key, "scope": scope, "scope_key": skey, "group_name": gname,
|
||||
"products_affected": len(set(g["products"])), "fields_affected": dict(g["fields"]), "allowed_values": allowed, "candidate_choices": g["choices"], "examples": g["examples"], "answer": ""})
|
||||
return out
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("stage"); ap.add_argument("--db", nargs="*")
|
||||
a = ap.parse_args()
|
||||
base.load_dotenv(ROOT / ".env")
|
||||
token = os.environ["AGENT_DEV_TOKEN"]
|
||||
allow = allowlist()
|
||||
names = a.db or list(allow)
|
||||
for n in names:
|
||||
if n not in allow:
|
||||
raise SystemExit(f"{n} is not in the exact write allowlist")
|
||||
STATE.mkdir(parents=True, exist_ok=True)
|
||||
analyses = []
|
||||
for name in names:
|
||||
path = allow[name]
|
||||
print(f"== {name} :: {a.stage}", flush=True)
|
||||
if a.stage in ("snapshot", "all"):
|
||||
snap = snapshot(name, path)
|
||||
print(f" snapshot: {snap['product_count']} products, company {snap['company'].get('classification')}", flush=True)
|
||||
if a.stage in ("dryrun", "apply", "verify", "second", "report", "questions", "all"):
|
||||
snap = load_snapshot(name)
|
||||
if a.stage in ("dryrun", "all", "apply"):
|
||||
an = analyze(name, path, token, snap)
|
||||
(STATE / f"{name}_dryrun_summary.json").write_text(json.dumps(summarize(an), ensure_ascii=False, indent=1, default=str), encoding="utf-8")
|
||||
write_ndjson(name, an, "dryrun")
|
||||
print(" dry-run:", json.dumps({k: summarize(an)[k] for k in ("total_products", "change_required_cells", "write_eligible_cells")}), flush=True)
|
||||
if a.stage in ("apply", "all"):
|
||||
res = apply(name, path, an, snap)
|
||||
(STATE / f"{name}_apply_summary.json").write_text(json.dumps(res, ensure_ascii=False, indent=1, default=str), encoding="utf-8")
|
||||
print(" apply:", json.dumps({k: res[k] for k in ("cells_attempted", "cells_succeeded", "cells_failed", "products_updated")}), flush=True)
|
||||
v = verify(name, path, an, snap)
|
||||
(STATE / f"{name}_verify_summary.json").write_text(json.dumps(v, ensure_ascii=False, indent=1, default=str), encoding="utf-8")
|
||||
print(" verify:", json.dumps({k: v[k] for k in ("planned_cells", "post_write_verified", "planned_mismatch", "unplanned_changes")}), flush=True)
|
||||
if a.stage in ("second", "all"):
|
||||
an2 = analyze(name, path, token, snap)
|
||||
s2 = summarize(an2)
|
||||
second = {"name": name, "SECOND_PASS_CHANGE_REQUIRED": s2["change_required_cells"], "SECOND_PASS_WRITES": 0, "fields": s2["fields"]}
|
||||
if s2["write_eligible_cells"]:
|
||||
second["SECOND_PASS_WRITES"] = apply(name, path, an2, snap)["cells_succeeded"]
|
||||
(STATE / f"{name}_second_pass.json").write_text(json.dumps(second, ensure_ascii=False, indent=1, default=str), encoding="utf-8")
|
||||
write_ndjson(name, an2, "second")
|
||||
print(" second pass:", second["SECOND_PASS_CHANGE_REQUIRED"], second["SECOND_PASS_WRITES"], flush=True)
|
||||
if a.stage in ("questions",):
|
||||
analyses.append(analyze(name, path, token, snap))
|
||||
if a.stage == "questions":
|
||||
q = questions(analyses)
|
||||
(ROOT / "docs" / "LOCAL_FISCAL_CONTEXT_QUESTIONS_V1.json").write_text(json.dumps({"artifact": "LOCAL_FISCAL_CONTEXT_QUESTIONS_V1", "total_questions": len(q), "questions": q}, ensure_ascii=False, indent=1, default=str), encoding="utf-8")
|
||||
import csv
|
||||
with open(ROOT / "docs" / "LOCAL_FISCAL_CONTEXT_QUESTIONS_V1.csv", "w", newline="", encoding="utf-8-sig") as f:
|
||||
w = csv.writer(f)
|
||||
w.writerow(["database", "company_key", "scope", "scope_key", "key", "group_name", "products_affected", "fields_affected", "allowed_values", "examples", "value", "reason"])
|
||||
for x in q:
|
||||
w.writerow([x["database"], x["company_key"], x["scope"], x["scope_key"], x["key"], x["group_name"], x["products_affected"], json.dumps(x["fields_affected"], ensure_ascii=False),
|
||||
x["allowed_values"] if isinstance(x["allowed_values"], str) else "|".join(x["allowed_values"]), " ; ".join(x["examples"]), "", ""])
|
||||
print("questions:", len(q))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user