thejayman77
ddcfab3a11
New per-row "Articles" button on the Sources table expands a read-only inline
panel of the source's ACTUAL ingested articles — so the automated metrics
(paywall/image/acceptance/duplicate) can be verified against evidence instead of
trusted blind. Distinct from "Check" (which re-samples the LIVE feed for
would-pass quality); this shows what's already in the DB, which is what the table
metrics are computed from.
- Backend: GET /api/admin/sources/{id}/articles?filter=&limit=&offset= (admin,
read-only). queries.source_articles + source_articles_summary — per article:
title, url, date, accepted, reason (the "why"), topic/flavor, paywalled
(domain rule), has_image, duplicate. Summary = counts + source-level paywall
rule.
- Frontend: expandable panel with a summary header ("27 ingested · 18 accepted
· … · paywall rule: ON (domain)"), filter chips (All/Accepted/Rejected/No
image/Duplicates), compact rows with title→link + badges + reason, Load more.
So "100% paywall" or "0% images" becomes clickable evidence: open two articles
to tell a real paywall from a mis-flagged domain, or a true image gap from an
enrichment failure. Test: test_source_articles_inspector. 241 pytest + 11 vitest.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
725 lines
31 KiB
Python
725 lines
31 KiB
Python
"""Read-only query helpers over the goodNews database.
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Pure stdlib and framework-agnostic: returns plain dicts so the same functions
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back both the CLI and the JSON API. All article output is metadata + a link to
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the original source — never stored bodies.
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"""
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from __future__ import annotations
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import sqlite3
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from datetime import UTC, datetime, timedelta
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from .feeds import MAX_BACKOFF_MINUTES
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from .paywall import is_paywalled
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# UA substrings that mark automated clients. Crawlers run JS on a throttled
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# budget and trip the boot-failure beacon routinely — without this filter they
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# read as real users seeing blank screens.
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BOT_UA_MARKS = ("headlesschrome", "bot", "spider", "crawl", "python", "curl", "wget", "phantomjs")
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_NOT_BOT_SQL = " AND ".join(f"instr(lower(user_agent), '{m}')=0" for m in BOT_UA_MARKS)
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def is_bot_ua(ua: str | None) -> bool:
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low = (ua or "").lower()
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return any(m in low for m in BOT_UA_MARKS)
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# Composite ranking used everywhere a "best first" order is needed. Kept as one
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# expression so brief, category feeds, and the API all rank identically.
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RANK_SCORE_SQL = (
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"(s.constructive_score + s.agency_score + s.human_benefit_score + src.trust_score "
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"- s.cortisol_score - s.ragebait_score - s.pr_risk_score)"
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)
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_ARTICLE_COLUMNS = f"""
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a.id,
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a.title,
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a.description,
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a.canonical_url,
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a.published_at,
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a.image_url,
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a.source_id,
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src.name AS source_name,
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s.topic,
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s.flavor,
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s.accepted,
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s.constructive_score,
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s.cortisol_score,
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s.ragebait_score,
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s.agency_score,
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s.human_benefit_score,
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s.pr_risk_score,
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s.reason_code,
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s.reason_text,
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s.model_name,
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(SELECT group_concat(t.tag) FROM article_tags t WHERE t.article_id = a.id) AS tags,
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{RANK_SCORE_SQL} AS rank_score
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"""
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def feed(
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conn: sqlite3.Connection,
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topic: str | None = None,
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flavor: str | None = None,
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accepted_only: bool = True,
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limit: int = 30,
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offset: int = 0,
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include_topics: list[str] | None = None,
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include_flavors: list[str] | None = None,
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mute_topics: list[str] | None = None,
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mute_flavors: list[str] | None = None,
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max_cortisol: int | None = None,
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max_ragebait: int | None = None,
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tag: str | None = None,
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source_id: int | None = None,
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sort: str = "ranked",
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follow_sources: list[int] | None = None,
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follow_tags: list[str] | None = None,
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since: str | None = None,
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) -> list[dict]:
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"""Return articles with categorical filters applied in SQL.
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sort="ranked" (default) is best-first by the composite rank; sort="latest"
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is pure recency (newest first) for the chronological "Latest" feed. Both
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stay accepted-only and respect the same boundaries.
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Categorical filters (topic/flavor include & mute, cortisol/ragebait ceilings)
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must be applied here, not after ranking — otherwise low-ranked-but-matching
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items (e.g. 'discovery' for a Wonder lane) fall outside any over-fetch window.
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Word-boundary avoid-terms remain a Python pass on the caller side.
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"""
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clauses = ["a.duplicate_of IS NULL", "src.content_visible = 1"]
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params: list = []
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if accepted_only:
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clauses.append("s.accepted = 1")
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if topic:
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clauses.append("s.topic = ?")
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params.append(topic.lower())
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if flavor:
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clauses.append("s.flavor = ?")
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params.append(flavor.lower())
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def _in(column: str, values: list[str], negate: bool = False) -> None:
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vals = [v.lower() for v in values]
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placeholders = ",".join("?" * len(vals))
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op = "NOT IN" if negate else "IN"
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# COALESCE keeps NULL-category rows from being dropped by NOT IN.
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clauses.append(f"COALESCE({column}, '') {op} ({placeholders})")
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params.extend(vals)
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if include_topics:
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_in("s.topic", include_topics)
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if include_flavors:
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_in("s.flavor", include_flavors)
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if mute_topics:
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_in("s.topic", mute_topics, negate=True)
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if mute_flavors:
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_in("s.flavor", mute_flavors, negate=True)
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if max_cortisol is not None:
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clauses.append("COALESCE(s.cortisol_score, 0) <= ?")
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params.append(max_cortisol)
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if max_ragebait is not None:
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clauses.append("COALESCE(s.ragebait_score, 0) <= ?")
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params.append(max_ragebait)
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if tag:
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clauses.append("EXISTS (SELECT 1 FROM article_tags at WHERE at.article_id = a.id AND at.tag = ?)")
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params.append(tag.lower())
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if source_id:
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clauses.append("a.source_id = ?")
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params.append(source_id)
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if since:
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# "New since last visit": articles discovered after the reader's last visit.
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clauses.append("a.discovered_at > ?")
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params.append(since)
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# "Following" feed: articles from a followed source OR carrying a followed tag.
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# Passing either list (even empty) switches to following mode; no follows → none.
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if follow_sources is not None or follow_tags is not None:
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ors: list[str] = []
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for sid in follow_sources or []:
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params.append(sid)
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if follow_sources:
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ors.append(f"a.source_id IN ({','.join('?' * len(follow_sources))})")
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ftags = [t.lower() for t in (follow_tags or [])]
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if ftags:
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ors.append(
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f"EXISTS (SELECT 1 FROM article_tags at WHERE at.article_id = a.id "
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f"AND at.tag IN ({','.join('?' * len(ftags))}))"
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)
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params.extend(ftags)
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clauses.append("(" + " OR ".join(ors) + ")" if ors else "0")
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where = "WHERE " + " AND ".join(clauses)
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params.extend([limit, offset])
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order_by = (
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"COALESCE(a.published_at, a.discovered_at) DESC, rank_score DESC"
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if sort == "latest"
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else "rank_score DESC, COALESCE(a.published_at, a.discovered_at) DESC"
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)
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rows = conn.execute(
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f"""
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SELECT {_ARTICLE_COLUMNS}
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FROM articles a
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JOIN sources src ON src.id = a.source_id
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JOIN article_scores s ON s.article_id = a.id
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{where}
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ORDER BY {order_by}
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LIMIT ? OFFSET ?
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""",
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params,
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).fetchall()
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return [dict(row) for row in rows]
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def brief(conn: sqlite3.Connection, brief_date: str | None = None, limit: int = 10) -> dict:
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"""Return a stored daily brief (latest if no date) with its ranked items."""
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target_date = brief_date or _latest_brief_date(conn)
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if not target_date:
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return {"brief_date": None, "title": None, "items": []}
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header = conn.execute(
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"SELECT brief_date, title, created_at FROM daily_briefs WHERE brief_date = ?",
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(target_date,),
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).fetchone()
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if not header:
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return {"brief_date": target_date, "title": None, "created_at": None, "items": []}
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rows = conn.execute(
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f"""
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SELECT bi.rank, bi.selection_reason, {_ARTICLE_COLUMNS},
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(SELECT summary FROM article_summaries WHERE article_id = a.id) AS summary
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FROM daily_briefs b
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JOIN daily_brief_items bi ON bi.brief_id = b.id
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JOIN articles a ON a.id = bi.article_id
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JOIN sources src ON src.id = a.source_id
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LEFT JOIN article_scores s ON s.article_id = a.id
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WHERE b.brief_date = ? AND src.content_visible = 1
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ORDER BY bi.rank
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LIMIT ?
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""",
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(target_date, limit),
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).fetchall()
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return {
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"brief_date": header["brief_date"],
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"title": header["title"],
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"created_at": header["created_at"],
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"items": [dict(row) for row in rows],
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}
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def saved(conn: sqlite3.Connection, user_id: int, limit: int = 200) -> list[dict]:
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"""Articles a user has saved, newest first (same shape as the feed)."""
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rows = conn.execute(
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f"""
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SELECT {_ARTICLE_COLUMNS}
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FROM saved_articles sv
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JOIN articles a ON a.id = sv.article_id
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JOIN sources src ON src.id = a.source_id
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LEFT JOIN article_scores s ON s.article_id = a.id
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WHERE sv.user_id = ?
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ORDER BY sv.saved_at DESC
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LIMIT ?
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""",
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(user_id, limit),
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).fetchall()
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return [dict(row) for row in rows]
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def saved_ids(conn: sqlite3.Connection, user_id: int) -> list[int]:
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return [r[0] for r in conn.execute(
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"SELECT article_id FROM saved_articles WHERE user_id = ?", (user_id,)
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)]
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def history(conn: sqlite3.Connection, user_id: int, limit: int = 200) -> list[dict]:
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"""Articles in a user's account history, most-recent first."""
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rows = conn.execute(
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f"""
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SELECT {_ARTICLE_COLUMNS}, MAX(h.at) AS seen_at
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FROM user_history h
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JOIN articles a ON a.id = h.article_id
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JOIN sources src ON src.id = a.source_id
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LEFT JOIN article_scores s ON s.article_id = a.id
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WHERE h.user_id = ?
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GROUP BY a.id
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ORDER BY seen_at DESC
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LIMIT ?
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""",
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(user_id, limit),
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).fetchall()
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return [dict(row) for row in rows]
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def content_stats(conn: sqlite3.Connection) -> dict:
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"""Corpus / serving health for the dashboard: how much good news is live."""
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def scalar(sql, params=()):
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return conn.execute(sql, params).fetchone()[0] or 0
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served = scalar(
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"SELECT COUNT(*) FROM article_scores s JOIN articles a ON a.id=s.article_id "
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"WHERE s.accepted=1 AND a.duplicate_of IS NULL"
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)
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accepted_7d = scalar(
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"SELECT COUNT(*) FROM article_scores s JOIN articles a ON a.id=s.article_id "
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"WHERE s.accepted=1 AND a.duplicate_of IS NULL "
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"AND COALESCE(a.published_at, a.discovered_at) >= datetime('now','-7 days')"
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)
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brief = conn.execute(
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"SELECT brief_date, (SELECT COUNT(*) FROM daily_brief_items WHERE brief_id=daily_briefs.id) n "
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"FROM daily_briefs ORDER BY brief_date DESC LIMIT 1"
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).fetchone()
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with_image = scalar(
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"SELECT COUNT(*) FROM article_scores s JOIN articles a ON a.id=s.article_id "
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"WHERE s.accepted=1 AND a.duplicate_of IS NULL AND a.image_url IS NOT NULL AND a.image_url!=''"
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)
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# Coverage is scoped to LIVE articles (accepted, non-duplicate) so the
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# percentages can't drift past 100% as rejected/duplicate rows accrue summaries.
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summaries = scalar(
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"SELECT COUNT(*) FROM article_summaries m JOIN articles a ON a.id=m.article_id "
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"JOIN article_scores s ON s.article_id=a.id WHERE s.accepted=1 AND a.duplicate_of IS NULL"
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)
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summaries_with_image = scalar(
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"SELECT COUNT(*) FROM article_summaries m JOIN articles a ON a.id=m.article_id "
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"JOIN article_scores s ON s.article_id=a.id WHERE s.accepted=1 AND a.duplicate_of IS NULL "
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"AND a.image_url IS NOT NULL AND a.image_url!=''"
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)
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brief_with_image = 0
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if brief:
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brief_with_image = scalar(
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"SELECT COUNT(*) FROM daily_brief_items bi JOIN articles a ON a.id=bi.article_id "
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"JOIN daily_briefs b ON b.id=bi.brief_id "
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"WHERE b.brief_date=? AND a.image_url IS NOT NULL AND a.image_url!=''",
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(brief["brief_date"],),
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)
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return {
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"served": served,
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"total": scalar("SELECT COUNT(*) FROM articles"),
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"rejected": scalar("SELECT COUNT(*) FROM article_scores WHERE accepted=0"),
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"accepted_7d": accepted_7d,
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"added_24h": scalar("SELECT COUNT(*) FROM articles WHERE discovered_at >= datetime('now','-1 day')"),
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"added_7d": scalar("SELECT COUNT(*) FROM articles WHERE discovered_at >= datetime('now','-7 days')"),
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"summaries": summaries,
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"summaries_with_image": summaries_with_image,
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"with_image": with_image,
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# Accepted, imageless articles whose enrichment was tried recently and
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# came up empty (no usable og:image) — the image "misses" to watch.
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"recent_enrich_fail": scalar(
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"SELECT COUNT(*) FROM article_scores s JOIN articles a ON a.id=s.article_id "
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"WHERE s.accepted=1 AND a.duplicate_of IS NULL AND (a.image_url IS NULL OR a.image_url='') "
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"AND a.image_checked_at >= datetime('now','-1 day')"
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),
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"active_sources": scalar("SELECT COUNT(*) FROM sources WHERE active=1"),
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"total_sources": scalar("SELECT COUNT(*) FROM sources"),
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"latest_brief_date": brief["brief_date"] if brief else None,
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"latest_brief_size": brief["n"] if brief else 0,
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"brief_with_image": brief_with_image,
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}
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def source_health(conn: sqlite3.Connection) -> list[dict]:
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"""Per source (active, paused, AND retired), the data an operator needs to manage feeds:
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failure streak, last error/success/attempt, computed next poll, and the
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backing metrics (served/accepted/total counts + acceptance & duplicate rates)
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so Pause/Flag decisions aren't vibes-based.
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next_due_at = last attempt + MIN(cap, interval * (1 + consecutive_failures)),
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mirroring feeds.due_source_rows; NULL last attempt means "due now". Paused
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sources are included (their articles stay live; only polling stops).
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"""
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rows = conn.execute(
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"""
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SELECT
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s.id, s.name, s.feed_url, s.homepage_url, s.default_category AS category, s.active,
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s.status, s.content_visible, s.retry_after_at,
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s.consecutive_failures AS failures, s.review_flag, s.review_reason,
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s.poll_interval_minutes AS interval_minutes,
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s.last_success_at, s.last_error_at, substr(s.last_error, 1, 160) AS last_error,
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(SELECT MAX(r.finished_at) FROM ingest_runs r
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WHERE r.source_id = s.id AND r.finished_at IS NOT NULL) AS last_attempt,
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(SELECT COUNT(*) FROM articles a WHERE a.source_id = s.id) AS total_articles,
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(SELECT COUNT(*) FROM articles a JOIN article_scores sc ON sc.article_id = a.id
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WHERE a.source_id = s.id AND sc.accepted = 1) AS accepted_total,
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(SELECT COUNT(*) FROM articles a WHERE a.source_id = s.id AND a.duplicate_of IS NOT NULL) AS duplicates,
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(SELECT COUNT(*) FROM articles a JOIN article_scores sc ON sc.article_id = a.id
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WHERE a.source_id = s.id AND sc.accepted = 1 AND a.duplicate_of IS NULL) AS served,
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(SELECT COUNT(*) FROM articles a JOIN article_scores sc ON sc.article_id = a.id
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WHERE a.source_id = s.id AND sc.accepted = 1 AND a.duplicate_of IS NULL
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AND a.image_url IS NOT NULL AND a.image_url != '') AS images,
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datetime(
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(SELECT MAX(r.finished_at) FROM ingest_runs r
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WHERE r.source_id = s.id AND r.finished_at IS NOT NULL),
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'+' || MIN(?, s.poll_interval_minutes * (1 + s.consecutive_failures)) || ' minutes'
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) AS next_due_at
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FROM sources s
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ORDER BY s.active DESC, s.consecutive_failures DESC, served DESC, s.name
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""",
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(MAX_BACKOFF_MINUTES,),
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).fetchall()
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out = []
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for r in rows:
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d = dict(r)
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total = d["total_articles"] or 0
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accepted = d["accepted_total"] or 0
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d["acceptance_rate"] = round(100 * accepted / total) if total else None
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d["duplicate_rate"] = round(100 * d["duplicates"] / total) if total else None
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# Curation quality: of what this source got ACCEPTED, how much was a
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# duplicate of content already served (accepted_total − served = accepted dupes).
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d["accepted_dup_rate"] = round(100 * (accepted - d["served"]) / accepted) if accepted else None
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d["image_coverage"] = round(100 * (d["images"] or 0) / d["served"]) if d["served"] else None
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# Paywall is a domain-level hint, so it's a per-source flag (not a rate).
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d["paywalled"] = is_paywalled(d.get("homepage_url") or d.get("feed_url"))
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# Match the REAL scheduler gate: due = the later of the streak-backoff time
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# and any retry_after_at rest (UTC strings sort chronologically).
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due_times = [t for t in (d["next_due_at"], d["retry_after_at"]) if t]
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d["next_due_at"] = max(due_times) if due_times else None
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out.append(d)
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return out
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# Attention thresholds — conservative + volume-gated, so the strip is a calm
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# operator nudge rather than a noisy scold. Items are aggregated (one line per
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# condition with a count), not one line per offending source.
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_STALE_DAYS = 10
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_REJECT_MIN_INGESTED = 20
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_REJECT_RATE = 25 # acceptance below this % (with enough volume)
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_DUP_MIN_ACCEPTED = 10
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_DUP_RATE = 50 # accepted-duplicate above this %
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_IMG_MIN_SERVED = 10
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_IMG_COVERAGE = 25 # per-source image coverage below this %
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_LONG_REST_HOURS = 12
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def _attention(content: dict, sources: list[dict], feedback_unread: int, now: datetime | None = None) -> list[dict]:
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"""The 'Attention Needed' strip: what an operator should look at, soft-toned
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(warn = act soon, info = worth a glance). Derived from the same data shown
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elsewhere, so it never disagrees with the detail sections."""
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items: list[dict] = []
|
||
n = lambda c: "" if c == 1 else "s" # noqa: E731 — tiny pluralizer
|
||
now = now or datetime.now(UTC)
|
||
active = [s for s in sources if (s.get("status") or ("active" if s.get("active") else "paused")) == "active"]
|
||
|
||
resting = [s for s in active if (s.get("failures") or 0) > 0]
|
||
if resting:
|
||
items.append({"level": "warn", "text": f"{len(resting)} source{n(len(resting))} backing off after failures"})
|
||
flagged = [s for s in sources if s.get("review_flag")]
|
||
if flagged:
|
||
items.append({"level": "warn", "text": f"{len(flagged)} source{n(len(flagged))} flagged for review"})
|
||
|
||
# Stale: active, visible feeds whose last success is well in the past.
|
||
stale_cutoff = (now - timedelta(days=_STALE_DAYS)).strftime("%Y-%m-%d %H:%M:%S")
|
||
stale = [s for s in active if s.get("content_visible", 1) and s.get("last_success_at") and s["last_success_at"] < stale_cutoff]
|
||
if stale:
|
||
items.append({"level": "warn", "text": f"{len(stale)} source{n(len(stale))} haven't updated in over {_STALE_DAYS} days"})
|
||
|
||
# High rejection: enough ingested volume, low acceptance.
|
||
rejecting = [s for s in active if (s.get("total_articles") or 0) >= _REJECT_MIN_INGESTED
|
||
and s.get("acceptance_rate") is not None and s["acceptance_rate"] < _REJECT_RATE]
|
||
if rejecting:
|
||
items.append({"level": "info", "text": f"{len(rejecting)} source{n(len(rejecting))} accepting under {_REJECT_RATE}% of submissions"})
|
||
|
||
# High accepted-duplicate: enough accepted volume, mostly echoing others.
|
||
duping = [s for s in active if (s.get("accepted_total") or 0) >= _DUP_MIN_ACCEPTED
|
||
and s.get("accepted_dup_rate") is not None and s["accepted_dup_rate"] > _DUP_RATE]
|
||
if duping:
|
||
items.append({"level": "info", "text": f"{len(duping)} source{n(len(duping))} mostly duplicating other feeds"})
|
||
|
||
# Low image coverage (info, not a warning).
|
||
thin = [s for s in active if (s.get("served") or 0) >= _IMG_MIN_SERVED
|
||
and s.get("image_coverage") is not None and s["image_coverage"] < _IMG_COVERAGE]
|
||
if thin:
|
||
items.append({"level": "info", "text": f"{len(thin)} source{n(len(thin))} with thin image coverage (under {_IMG_COVERAGE}%)"})
|
||
|
||
# Long rate-limit rest (info).
|
||
rest_cutoff = (now + timedelta(hours=_LONG_REST_HOURS)).strftime("%Y-%m-%d %H:%M:%S")
|
||
long_rest = [s for s in active if s.get("retry_after_at") and s["retry_after_at"] > rest_cutoff]
|
||
if long_rest:
|
||
items.append({"level": "info", "text": f"{len(long_rest)} source{n(len(long_rest))} rate-limited for {_LONG_REST_HOURS}h+"})
|
||
|
||
# Site-wide signals.
|
||
served = content.get("served") or 0
|
||
with_image = content.get("with_image") or 0
|
||
if served and (with_image / served) < 0.70:
|
||
items.append({"level": "info", "text": f"Image coverage at {round(100 * with_image / served)}% (aim for 70%+)"})
|
||
|
||
brief_size = content.get("latest_brief_size") or 0
|
||
if brief_size < 7:
|
||
items.append({"level": "info", "text": f"Today's brief has only {brief_size} item{n(brief_size)}"})
|
||
|
||
if feedback_unread:
|
||
items.append({"level": "info", "text": f"{feedback_unread} unread feedback message{n(feedback_unread)}"})
|
||
return items
|
||
|
||
|
||
# --- Source article inspector: the real articles behind the source metrics -----
|
||
|
||
_SRC_ART_FILTERS = {
|
||
"accepted": "AND s.accepted = 1",
|
||
"rejected": "AND s.accepted = 0",
|
||
"no_image": "AND (a.image_url IS NULL OR a.image_url = '')",
|
||
"duplicates": "AND a.duplicate_of IS NOT NULL",
|
||
}
|
||
|
||
|
||
def source_articles(conn: sqlite3.Connection, source_id: int, filter: str = "all",
|
||
limit: int = 25, offset: int = 0) -> list[dict]:
|
||
"""The actual ingested articles for a source, newest first — so admins can
|
||
verify the metric (paywall/image/acceptance) against real evidence."""
|
||
where = _SRC_ART_FILTERS.get(filter, "")
|
||
rows = conn.execute(
|
||
f"""
|
||
SELECT a.id, a.title, a.canonical_url, a.published_at, a.discovered_at,
|
||
a.image_url, a.duplicate_of,
|
||
s.accepted, s.reason_code, s.reason_text, s.topic, s.flavor
|
||
FROM articles a
|
||
LEFT JOIN article_scores s ON s.article_id = a.id
|
||
WHERE a.source_id = ? {where}
|
||
ORDER BY COALESCE(a.published_at, a.discovered_at) DESC
|
||
LIMIT ? OFFSET ?
|
||
""",
|
||
(source_id, limit, offset),
|
||
).fetchall()
|
||
return [
|
||
{
|
||
"id": r["id"],
|
||
"title": r["title"],
|
||
"url": r["canonical_url"],
|
||
"published_at": r["published_at"] or r["discovered_at"],
|
||
"accepted": r["accepted"],
|
||
"reason": r["reason_text"] or r["reason_code"], # the "why" behind accept/reject
|
||
"topic": r["topic"],
|
||
"flavor": r["flavor"],
|
||
"paywalled": is_paywalled(r["canonical_url"]), # domain rule — same for the source
|
||
"has_image": bool(r["image_url"]),
|
||
"duplicate": r["duplicate_of"] is not None,
|
||
}
|
||
for r in rows
|
||
]
|
||
|
||
|
||
def source_articles_summary(conn: sqlite3.Connection, source_id: int) -> dict:
|
||
"""Counts behind the table metrics + the source-level paywall rule, so the
|
||
panel header reads e.g. '120 · 96 accepted · 24 rejected · 3 no image · paywall: ON'."""
|
||
agg = conn.execute(
|
||
"""
|
||
SELECT COUNT(*) total,
|
||
COALESCE(SUM(s.accepted = 1), 0) accepted,
|
||
COALESCE(SUM(s.accepted = 0), 0) rejected,
|
||
COALESCE(SUM(a.image_url IS NULL OR a.image_url = ''), 0) no_image,
|
||
COALESCE(SUM(a.duplicate_of IS NOT NULL), 0) duplicates
|
||
FROM articles a LEFT JOIN article_scores s ON s.article_id = a.id
|
||
WHERE a.source_id = ?
|
||
""",
|
||
(source_id,),
|
||
).fetchone()
|
||
one = conn.execute("SELECT canonical_url FROM articles WHERE source_id = ? LIMIT 1", (source_id,)).fetchone()
|
||
return {
|
||
"total": agg["total"], "accepted": agg["accepted"], "rejected": agg["rejected"],
|
||
"no_image": agg["no_image"], "duplicates": agg["duplicates"],
|
||
"paywalled": is_paywalled(one["canonical_url"]) if one else False,
|
||
}
|
||
|
||
|
||
def admin_stats(conn: sqlite3.Connection, days: int = 30) -> dict:
|
||
"""Aggregate, non-personal usage stats for the admin dashboard."""
|
||
since = f"-{days} days"
|
||
|
||
def scalar(sql, params=()):
|
||
return conn.execute(sql, params).fetchone()[0] or 0
|
||
|
||
visitors = {
|
||
"today": scalar("SELECT COUNT(DISTINCT visitor_hash) FROM events "
|
||
"WHERE kind='visit' AND visitor_hash!='' AND day=date('now')"),
|
||
"d7": scalar("SELECT COUNT(DISTINCT visitor_hash) FROM events "
|
||
"WHERE kind='visit' AND visitor_hash!='' AND day>=date('now','-7 days')"),
|
||
"d30": scalar("SELECT COUNT(DISTINCT visitor_hash) FROM events "
|
||
"WHERE kind='visit' AND visitor_hash!='' AND day>=date('now',?)", (since,)),
|
||
}
|
||
|
||
# Returning (seen on ≥2 distinct days) vs one-and-done, over the window.
|
||
rows = conn.execute(
|
||
"SELECT CASE WHEN d>=2 THEN 'returning' ELSE 'once' END g, COUNT(*) n FROM ("
|
||
" SELECT visitor_hash, COUNT(DISTINCT day) d FROM events "
|
||
" WHERE kind='visit' AND visitor_hash!='' AND day>=date('now',?) GROUP BY visitor_hash"
|
||
") GROUP BY g", (since,),
|
||
).fetchall()
|
||
loyalty = {r["g"]: r["n"] for r in rows}
|
||
|
||
# An "open" = opening the article via our summary page (legacy 'open' + 'summary_viewed').
|
||
OPEN = "e.kind IN ('open','summary_viewed')"
|
||
|
||
top_articles = [dict(r) for r in conn.execute(
|
||
f"SELECT e.article_id AS id, a.title, src.name AS source, COUNT(*) AS opens "
|
||
f"FROM events e JOIN articles a ON a.id=e.article_id JOIN sources src ON src.id=a.source_id "
|
||
f"WHERE {OPEN} AND e.article_id>0 AND e.day>=date('now',?) "
|
||
f"GROUP BY e.article_id ORDER BY opens DESC LIMIT 12", (since,),
|
||
)]
|
||
|
||
top_groupings = [dict(r) for r in conn.execute(
|
||
f"SELECT t.tag, COUNT(*) AS opens FROM events e JOIN article_tags t ON t.article_id=e.article_id "
|
||
f"WHERE {OPEN} AND e.day>=date('now',?) GROUP BY t.tag ORDER BY opens DESC LIMIT 12", (since,),
|
||
)]
|
||
|
||
top_topics = [dict(r) for r in conn.execute(
|
||
f"SELECT s.topic, COUNT(*) AS opens FROM events e JOIN article_scores s ON s.article_id=e.article_id "
|
||
f"WHERE {OPEN} AND s.topic IS NOT NULL AND e.day>=date('now',?) "
|
||
f"GROUP BY s.topic ORDER BY opens DESC", (since,),
|
||
)]
|
||
|
||
# Counts per event kind over the window (each = distinct visitor-days, by dedup).
|
||
kc_rows = conn.execute(
|
||
"SELECT kind, COUNT(*) n FROM events WHERE day>=date('now',?) GROUP BY kind", (since,)
|
||
).fetchall()
|
||
kc = {r["kind"]: r["n"] for r in kc_rows}
|
||
|
||
shares = {k: kc.get(k, 0) for k in ("share_ub", "copy_source", "native_share", "source_click")}
|
||
|
||
summary_views = kc.get("summary_viewed", 0)
|
||
source_clicks = kc.get("source_click", 0)
|
||
funnel = {
|
||
"summary_viewed": summary_views,
|
||
"source_click": source_clicks,
|
||
"full_story": kc.get("full_story", 0),
|
||
"source_rate": round(100 * source_clicks / summary_views) if summary_views else 0,
|
||
}
|
||
emotional_mix = {
|
||
"not_today": kc.get("not_today", 0),
|
||
"less_like_this": kc.get("less_like_this", 0),
|
||
"hide_topic": kc.get("hide_topic", 0),
|
||
}
|
||
paywall = {
|
||
"paywall_replace": kc.get("paywall_replace", 0),
|
||
"paywalled_source_open": kc.get("paywalled_source_open", 0),
|
||
}
|
||
replace = {"used": kc.get("replace_used", 0), "none": kc.get("replace_none", 0)}
|
||
|
||
# Accounts — aggregate counts only (no emails, no per-user listing).
|
||
accounts = {
|
||
"total": scalar("SELECT COUNT(*) FROM users"),
|
||
"new_today": scalar("SELECT COUNT(*) FROM users WHERE date(created_at)=date('now')"),
|
||
"new_7d": scalar("SELECT COUNT(*) FROM users WHERE created_at>=date('now','-7 days')"),
|
||
"new_30d": scalar("SELECT COUNT(*) FROM users WHERE created_at>=date('now',?)", (since,)),
|
||
"active_7d": scalar("SELECT COUNT(DISTINCT user_id) FROM sessions WHERE last_seen_at>=date('now','-7 days')"),
|
||
}
|
||
|
||
# Returning-visitor buckets by distinct active days in the window.
|
||
bucket_rows = conn.execute(
|
||
"SELECT CASE WHEN d>=8 THEN '8+' WHEN d>=4 THEN '4-7' WHEN d>=2 THEN '2-3' ELSE 'new' END b, "
|
||
"COUNT(*) n FROM (SELECT visitor_hash, COUNT(DISTINCT day) d FROM events "
|
||
"WHERE kind='visit' AND visitor_hash!='' AND day>=date('now',?) GROUP BY visitor_hash) GROUP BY b",
|
||
(since,),
|
||
).fetchall()
|
||
buckets = {r["b"]: r["n"] for r in bucket_rows}
|
||
retention = {k: buckets.get(k, 0) for k in ("new", "2-3", "4-7", "8+")}
|
||
|
||
daily = [dict(r) for r in conn.execute(
|
||
"SELECT day, SUM(kind IN ('open','summary_viewed')) AS opens, SUM(kind='visit') AS visits "
|
||
"FROM events WHERE day>=date('now',?) GROUP BY day ORDER BY day", (since,),
|
||
)]
|
||
|
||
content = content_stats(conn)
|
||
sources = source_health(conn)
|
||
feedback_7d = scalar("SELECT COUNT(*) FROM feedback WHERE created_at >= datetime('now','-7 days')")
|
||
feedback_unread = scalar("SELECT COUNT(*) FROM feedback WHERE read_at IS NULL")
|
||
|
||
return {
|
||
"days": days,
|
||
"content": content,
|
||
"sources": sources,
|
||
"attention": _attention(content, sources, feedback_unread),
|
||
"feedback_7d": feedback_7d,
|
||
"feedback_unread": feedback_unread,
|
||
"visitors": visitors,
|
||
"returning": loyalty.get("returning", 0),
|
||
"once": loyalty.get("once", 0),
|
||
"retention": retention,
|
||
"accounts": accounts,
|
||
"funnel": funnel,
|
||
"emotional_mix": emotional_mix,
|
||
"paywall": paywall,
|
||
"replace": replace,
|
||
"top_articles": top_articles,
|
||
"top_groupings": top_groupings,
|
||
"top_topics": top_topics,
|
||
"shares": shares,
|
||
"daily": daily,
|
||
# Boot-failure seatbelt signal — blank-screen risk surfacing. Bots are
|
||
# excluded from the headline counts: throttled crawlers fail the boot
|
||
# check routinely and would read as real users seeing blank screens.
|
||
"client_errors": {
|
||
"today": scalar(
|
||
f"SELECT COUNT(*) FROM client_errors WHERE date(created_at)=date('now') AND {_NOT_BOT_SQL}"
|
||
),
|
||
"window": scalar(
|
||
f"SELECT COUNT(*) FROM client_errors WHERE created_at>=date('now',?) AND {_NOT_BOT_SQL}",
|
||
(since,),
|
||
),
|
||
},
|
||
}
|
||
|
||
|
||
def existing_article_ids(conn: sqlite3.Connection, ids: list[int]) -> list[int]:
|
||
"""Filter to ids that still exist (FK-safe inserts for save/history/import)."""
|
||
clean = list({int(i) for i in ids})[:1000]
|
||
if not clean:
|
||
return []
|
||
placeholders = ",".join("?" * len(clean))
|
||
return [r[0] for r in conn.execute(
|
||
f"SELECT id FROM articles WHERE id IN ({placeholders})", clean
|
||
)]
|
||
|
||
|
||
def tag_counts(conn: sqlite3.Connection, accepted_only: bool = True) -> dict:
|
||
"""How many shown (accepted, non-duplicate) articles carry each grouping tag."""
|
||
where = "WHERE a.duplicate_of IS NULL" + (" AND s.accepted = 1" if accepted_only else "")
|
||
rows = conn.execute(
|
||
f"""
|
||
SELECT t.tag, COUNT(*) AS count
|
||
FROM article_tags t
|
||
JOIN articles a ON a.id = t.article_id
|
||
JOIN article_scores s ON s.article_id = a.id
|
||
{where}
|
||
GROUP BY t.tag
|
||
"""
|
||
).fetchall()
|
||
return {r["tag"]: r["count"] for r in rows}
|
||
|
||
|
||
def category_counts(conn: sqlite3.Connection, accepted_only: bool = True) -> list[dict]:
|
||
"""Return per topic/flavor article counts for building browse UIs.
|
||
|
||
Joins articles and excludes duplicates so the counts match exactly what the
|
||
feed endpoint will actually return for each topic/flavor.
|
||
"""
|
||
clauses = ["a.duplicate_of IS NULL"]
|
||
clauses.append("s.accepted = 1" if accepted_only else "s.topic IS NOT NULL")
|
||
rows = conn.execute(
|
||
f"""
|
||
SELECT s.topic, s.flavor, COUNT(*) AS count
|
||
FROM article_scores s
|
||
JOIN articles a ON a.id = s.article_id
|
||
WHERE {" AND ".join(clauses)}
|
||
GROUP BY s.topic, s.flavor
|
||
ORDER BY s.topic, s.flavor
|
||
"""
|
||
).fetchall()
|
||
return [dict(row) for row in rows]
|
||
|
||
|
||
def available_dates(conn: sqlite3.Connection, limit: int = 30) -> list[str]:
|
||
rows = conn.execute(
|
||
"SELECT brief_date FROM daily_briefs ORDER BY brief_date DESC LIMIT ?",
|
||
(limit,),
|
||
).fetchall()
|
||
return [row["brief_date"] for row in rows]
|
||
|
||
|
||
def _latest_brief_date(conn: sqlite3.Connection) -> str | None:
|
||
row = conn.execute(
|
||
"SELECT brief_date FROM daily_briefs ORDER BY brief_date DESC LIMIT 1"
|
||
).fetchone()
|
||
return row["brief_date"] if row else None
|