upbeatBytes/goodnews/cli.py

from __future__ import annotations

import argparse
import contextlib
import os
import sqlite3
from pathlib import Path

from .briefs import build_daily_brief, show_brief
from .db import connect, init_db
from .digest import send_due_digests
from .games import generate_daily_puzzles
from .localtime import local_today
from .dedup import DEFAULT_THRESHOLD, DEFAULT_WINDOW_DAYS, cluster_duplicates, dedup as run_dedup
from .geo import tag_articles as tag_geo
from . import art, onthisday, quote, wotd
from .enrich import enrich_brief_images, enrich_read_times, enrich_recent_images, enrich_summarized_images
from .summarize import generate_summary, get_summary
from .feeds import (
    fetch_feed,
    parse_feed,
    poll_all_sources,
    poll_due_sources,
    poll_source,
    preview_feed,
)
from .llm import LocalModelClient, classify_articles
from .scoring import score_article
from .sources import (
    list_candidates,
    load_sources,
    promote_candidate,
    reject_candidate,
    review_sources,
    save_candidate,
    upsert_sources,
)


ROOT = Path(__file__).resolve().parents[1]
DEFAULT_DB = ROOT / "data" / "goodnews.sqlite3"
DEFAULT_SOURCES = ROOT / "config" / "sources.toml"


def _default_db() -> Path:
    # Honor GOODNEWS_DB like the rest of the app (db.connect) does, so `GOODNEWS_DB=… `
    # actually targets that DB instead of being silently ignored — otherwise a copy-DB
    # maintenance run (e.g. dedup --force-recluster) can land on production by surprise.
    return Path(os.environ.get("GOODNEWS_DB") or DEFAULT_DB)


def main() -> None:
    parser = argparse.ArgumentParser(prog="goodnews")
    parser.add_argument("--db", type=Path, default=_default_db(),
                        help="SQLite database path (defaults to $GOODNEWS_DB, else the bundled data/ DB)")
    subparsers = parser.add_subparsers(dest="command", required=True)

    subparsers.add_parser("init-db", help="Create or update the SQLite schema")

    import_parser = subparsers.add_parser("import-sources", help="Load sources from TOML")
    import_parser.add_argument("--sources", type=Path, default=DEFAULT_SOURCES)

    poll_parser = subparsers.add_parser("poll", help="Poll active RSS/Atom sources")
    poll_parser.add_argument("--source", help="Poll one source by exact name")
    poll_parser.add_argument("--limit", type=int, help="Poll only the first N active sources")

    list_parser = subparsers.add_parser("list-recent", help="Show recently discovered articles")
    list_parser.add_argument("--limit", type=int, default=20)
    list_parser.add_argument("--accepted-only", action="store_true")

    source_parser = subparsers.add_parser("list-sources", help="Show configured sources")
    source_parser.add_argument("--active-only", action="store_true")

    cat_parser = subparsers.add_parser("list-category", help="Browse articles by topic and/or flavor")
    cat_parser.add_argument("--topic", help="Filter by topic, e.g. science, environment, animals")
    cat_parser.add_argument("--flavor", help="Filter by flavor, e.g. breakthrough, discovery, feelgood")
    cat_parser.add_argument("--limit", type=int, default=20)
    cat_parser.add_argument("--all", action="store_true", help="Include not-accepted articles")

    subparsers.add_parser("source-report", help="Show source-level ingestion and scoring stats")

    check_feeds_parser = subparsers.add_parser("check-feeds", help="Fetch and parse each feed, reporting health")
    check_feeds_parser.add_argument("--all", action="store_true", help="Include inactive sources")

    preview_parser = subparsers.add_parser("preview-source", help="Score a sample of a feed without adding it")
    preview_parser.add_argument("url", help="Feed URL to preview")
    preview_parser.add_argument("--sample", type=int, default=25)
    preview_parser.add_argument("--classify", action="store_true", help="Also classify with the local model (slower)")
    preview_parser.add_argument("--base-url", help="OpenAI-compatible base URL (with --classify)")
    preview_parser.add_argument("--model", help="Local model name (with --classify)")

    suggest_parser = subparsers.add_parser("suggest-source", help="Preview a feed and stage it as a candidate")
    suggest_parser.add_argument("url", help="Feed URL to suggest")
    suggest_parser.add_argument("--name", help="Display name for the source")
    suggest_parser.add_argument("--homepage", help="Homepage URL")
    suggest_parser.add_argument("--sample", type=int, default=25)
    suggest_parser.add_argument("--classify", action="store_true", help="Classify the sample with the local model")
    suggest_parser.add_argument("--base-url")
    suggest_parser.add_argument("--model")

    cand_parser = subparsers.add_parser("list-candidates", help="List staged source candidates")
    cand_parser.add_argument("--status", help="Filter by status: suggested|quarantined|rejected|promoted")

    promote_parser = subparsers.add_parser("promote-candidate", help="Copy a candidate into the real sources")
    promote_parser.add_argument("id", type=int)
    promote_parser.add_argument("--active", action="store_true", help="Activate immediately (default: inactive)")
    promote_parser.add_argument("--category", help="default_category for the new source")
    promote_parser.add_argument("--trust", type=int, default=5)
    promote_parser.add_argument("--pr-risk", type=int, default=3)

    reject_parser = subparsers.add_parser("reject-candidate", help="Mark a candidate as rejected")
    reject_parser.add_argument("id", type=int)

    review_parser = subparsers.add_parser(
        "review-sources", help="Recompute advisory review flags (never deactivates anything)"
    )
    review_parser.add_argument("--stale-days", type=int, default=14)

    runs_parser = subparsers.add_parser("list-runs", help="Show recent ingest runs")
    runs_parser.add_argument("--limit", type=int, default=20)

    subparsers.add_parser("rescore", help="Re-run heuristic scores for stored articles")

    classify_parser = subparsers.add_parser("classify", help="Classify candidates with a local LLM")
    classify_parser.add_argument("--limit", type=int, default=10)
    classify_parser.add_argument("--include-rejected", action="store_true")
    classify_parser.add_argument("--dry-run", action="store_true")
    classify_parser.add_argument("--base-url", help="OpenAI-compatible base URL, e.g. http://127.0.0.1:1234/v1")
    classify_parser.add_argument("--model", help="Local model name")

    cycle_parser = subparsers.add_parser(
        "cycle", help="Poll due sources, classify new articles, rebuild today's brief (for scheduling)"
    )
    cycle_parser.add_argument("--classify-limit", type=int, default=40)
    cycle_parser.add_argument("--no-classify", action="store_true", help="Skip the LLM classify step")
    cycle_parser.add_argument("--no-dedup", action="store_true", help="Skip the embedding dedup step")
    cycle_parser.add_argument("--no-geo", action="store_true", help="Skip tagging article subject-geography")
    cycle_parser.add_argument("--geo-limit", type=int, default=60, help="Max articles to geo-tag per cycle")
    cycle_parser.add_argument("--no-art", action="store_true", help="Skip the Daily Art pick")
    cycle_parser.add_argument("--no-joys", action="store_true", help="Skip the small-joys picks (On This Day, etc.)")
    cycle_parser.add_argument("--no-brief", action="store_true", help="Skip rebuilding today's brief")
    cycle_parser.add_argument("--no-review", action="store_true", help="Skip recomputing source review flags")
    cycle_parser.add_argument("--no-digest", action="store_true", help="Skip sending due daily digests")
    cycle_parser.add_argument("--force", action="store_true", help="Poll all active sources, ignoring intervals")
    cycle_parser.add_argument("--base-url", help="OpenAI-compatible base URL for classify")
    cycle_parser.add_argument("--model", help="Local model name for classify")

    digest_parser = subparsers.add_parser("send-digests", help="Send today's digest to opted-in users (morning-gated)")
    digest_parser.add_argument("--force", action="store_true", help="Ignore the morning send window")

    enrich_images_parser = subparsers.add_parser(
        "enrich-images", help="Backfill og:images for already-summarized articles that lack one"
    )
    enrich_images_parser.add_argument("--limit", type=int, default=50, help="Max articles to fetch this batch")

    art_parser = subparsers.add_parser("art", help="Daily Art: harvest the pool and/or pick today's cached piece")
    art_parser.add_argument("--harvest", action="store_true", help="(Re)harvest the curated museum pool")
    art_parser.add_argument("--force", action="store_true", help="Re-pick today's art even if already chosen")

    geo_parser = subparsers.add_parser("geo", help="Tag article subject-geography (backfill / manual). Cycle-locked.")
    geo_parser.add_argument("--limit", type=int, default=200, help="Max articles to tag this batch")
    geo_parser.add_argument("--reclassify", action="store_true", help="Re-tag even rows already at the current geo version")
    geo_parser.add_argument("--base-url", help="OpenAI-compatible base URL")
    geo_parser.add_argument("--model", help="Local model name")

    dedup_parser = subparsers.add_parser("dedup", help="Cluster near-duplicate stories via local embeddings")
    dedup_parser.add_argument("--threshold", type=float, default=DEFAULT_THRESHOLD, help="Cosine similarity cutoff")
    dedup_parser.add_argument("--window-days", type=int, default=DEFAULT_WINDOW_DAYS)
    dedup_parser.add_argument("--embed-limit", type=int, help="Cap how many missing embeddings to compute")
    dedup_parser.add_argument("--base-url", help="OpenAI-compatible base URL")
    dedup_parser.add_argument("--model", help="Chat model name (unused for embeddings)")
    dedup_parser.add_argument("--force-recluster", action="store_true",
                              help="Re-cluster the EXISTING corpus even if no new embeddings "
                                   "(re-applies representative policy; cycle-locked, no model needed)")

    check_llm_parser = subparsers.add_parser("check-llm", help="Check local OpenAI-compatible model endpoint")
    check_llm_parser.add_argument("--base-url", help="OpenAI-compatible base URL, e.g. http://127.0.0.1:1234/v1")
    check_llm_parser.add_argument("--model", help="Expected local model name")

    brief_parser = subparsers.add_parser("build-brief", help="Build/freeze a daily brief")
    brief_parser.add_argument("--date", help="Brief date in YYYY-MM-DD format; defaults to today")
    brief_parser.add_argument("--limit", type=int, default=7)
    brief_parser.add_argument("--replace", action="store_true")

    show_brief_parser = subparsers.add_parser("show-brief", help="Show a stored daily brief")
    show_brief_parser.add_argument("--date", help="Brief date in YYYY-MM-DD format; defaults to latest brief")
    show_brief_parser.add_argument("--limit", type=int, default=10)

    serve_parser = subparsers.add_parser("serve", help="Run the web/API server (requires the 'web' extra)")
    serve_parser.add_argument("--host", default="127.0.0.1", help="Bind host; use 0.0.0.0 to expose")
    serve_parser.add_argument("--port", type=int, default=8000)
    serve_parser.add_argument("--reload", action="store_true", help="Auto-reload on code changes (dev)")

    args = parser.parse_args()

    if args.command == "serve":
        serve(args)
        return

    conn = connect(args.db)

    if args.command == "init-db":
        init_db(conn)
        print(f"Initialized {args.db}")
    elif args.command == "import-sources":
        init_db(conn)
        sources = load_sources(args.sources)
        count = upsert_sources(conn, sources)
        print(f"Imported {count} sources from {args.sources}")
    elif args.command == "poll":
        init_db(conn)
        if args.source:
            source = conn.execute("SELECT * FROM sources WHERE name = ?", (args.source,)).fetchone()
            if not source:
                raise SystemExit(f"No source named {args.source!r}")
            result = poll_source(conn, source)
        else:
            result = poll_all_sources(conn, limit=args.limit)
        print(_format_result(result))
    elif args.command == "list-recent":
        list_recent(conn, limit=args.limit, accepted_only=args.accepted_only)
    elif args.command == "list-sources":
        list_sources(conn, active_only=args.active_only)
    elif args.command == "list-category":
        list_category(conn, topic=args.topic, flavor=args.flavor, limit=args.limit, accepted_only=not args.all)
    elif args.command == "source-report":
        source_report(conn)
    elif args.command == "check-feeds":
        check_feeds(conn, include_inactive=args.all)
    elif args.command == "preview-source":
        client = llm_client_from_args(args) if args.classify else None
        preview = preview_feed(args.url, sample=args.sample, client=client)
        print_preview(preview)
    elif args.command == "suggest-source":
        init_db(conn)
        client = llm_client_from_args(args) if args.classify else None
        preview = preview_feed(args.url, sample=args.sample, client=client)
        print_preview(preview)
        cand = save_candidate(conn, args.url, preview=preview, name=args.name, homepage_url=args.homepage)
        print(f"Saved as candidate #{cand['id']} (status {cand['status']}). Review with list-candidates.")
    elif args.command == "list-candidates":
        init_db(conn)
        rows = list_candidates(conn, status=args.status)
        if not rows:
            print("No candidates.")
        for r in rows:
            line = f"[{r['id']}] {r['status']} | {r['name'] or '(unnamed)'} | {r['feed_url']}"
            if r["preview_json"]:
                import json as _json

                p = _json.loads(r["preview_json"])
                _rate = p.get("acceptance_rate")
                _rate_str = f"{round(_rate * 100)}%" if _rate is not None else "—"
                line += f"  (accept {_rate_str}, sampled {p.get('sampled', 0)})"
            print(line)
    elif args.command == "promote-candidate":
        init_db(conn)
        try:
            source_id = promote_candidate(
                conn, args.id, active=args.active, default_category=args.category,
                trust_score=args.trust, pr_risk_score=args.pr_risk,
            )
        except ValueError as exc:
            raise SystemExit(str(exc))
        state = "active" if args.active else "inactive"
        print(f"Promoted candidate #{args.id} -> source #{source_id} ({state}).")
    elif args.command == "reject-candidate":
        init_db(conn)
        ok = reject_candidate(conn, args.id)
        print(f"Rejected candidate #{args.id}." if ok else f"No candidate #{args.id}.")
    elif args.command == "review-sources":
        init_db(conn)
        flagged = review_sources(conn, stale_days=args.stale_days)
        if not flagged:
            print("All active sources look healthy.")
        else:
            print(f"{len(flagged)} source(s) flagged for review (advisory — none deactivated):")
            for f in flagged:
                print(f"  [{f['id']}] {f['name']}: {f['reason']}")
    elif args.command == "list-runs":
        list_runs(conn, limit=args.limit)
    elif args.command == "rescore":
        count = rescore_articles(conn)
        print(f"Rescored {count} articles")
    elif args.command == "classify":
        init_db(conn)
        client = llm_client_from_args(args)
        report = classify_articles(
            conn,
            client,
            limit=args.limit,
            include_rejected=args.include_rejected,
            dry_run=args.dry_run,
        )
        for article_id, scores in report.results:
            accepted = "yes" if scores["accepted"] else "no"
            print(
                f"[{article_id}] accepted={accepted} {scores['topic']}/{scores['flavor']} "
                f"reason={scores['reason_code']}"
            )
            print(f"  {scores['reason_text']}")
        print(
            f"classify: attempted={report.attempted} succeeded={report.succeeded} "
            f"skipped={report.skipped}"
        )
        if args.dry_run:
            print("Dry run only; database was not updated.")
    elif args.command == "cycle":
        run_cycle(conn, args)
    elif args.command == "send-digests":
        init_db(conn)
        sent = send_due_digests(conn, force=args.force)
        print(f"send-digests: sent {sent}")
    elif args.command == "enrich-images":
        found = enrich_summarized_images(conn, limit=args.limit)
        print(f"enrich-images: {found} new image(s) for summarized articles")
    elif args.command == "art":
        init_db(conn)
        if args.harvest:
            h = art.harvest_pool(conn)
            print(f"art harvest: found={h['found']} added={h['added']} pool={h['pool']} errors={h['errors']}")
        picked = art.pick_daily(conn, force=args.force, client=LocalModelClient.from_env())
        if picked:
            print(f"art pick: {picked['art_date']} -> #{picked['object_id']} "
                  f"\"{picked['title']}\" — {picked['artist'] or 'Unknown'}")
        else:
            print("art pick: nothing fetched (kept the last piece)")
    elif args.command == "geo":
        init_db(conn)
        # Cycle-locked so a manual backfill can't contend with the scheduled cycle.
        with cycle_lock(args.db) as acquired:
            if not acquired:
                print("geo: a cycle is already running; try again after it finishes")
                return
            g = tag_geo(conn, llm_client_from_args(args), limit=args.limit, reclassify=args.reclassify)
        print(f"geo: tagged={g['tagged']} errors={g['errors']} (of {g['candidates']} candidates)")
    elif args.command == "dedup":
        init_db(conn)
        if args.force_recluster:
            # Re-apply representative policy to the EXISTING corpus. The normal path
            # fast-skips when no new embeddings exist, so it would NOT pick up a policy
            # change. Cycle-locked so it can't overlap the scheduled timer; no model
            # needed (pure re-cluster over stored embeddings).
            with cycle_lock(args.db) as acquired:
                if not acquired:
                    print("dedup: a cycle is already running; re-run --force-recluster after it finishes")
                    return
                stats = cluster_duplicates(conn, threshold=args.threshold, window_days=args.window_days)
            print(
                f"dedup (forced recluster): articles={stats['articles']} "
                f"clusters={stats['clusters']} duplicate_clusters={stats['duplicate_clusters']} "
                f"duplicates_hidden={stats['duplicates']}"
            )
        else:
            client = llm_client_from_args(args)
            stats = run_dedup(
                conn, client, threshold=args.threshold, window_days=args.window_days, embed_limit=args.embed_limit
            )
            print(
                f"dedup: embedded={stats['embedded']} articles={stats['articles']} "
                f"clusters={stats['clusters']} duplicate_clusters={stats['duplicate_clusters']} "
                f"duplicates_hidden={stats['duplicates']}"
            )
    elif args.command == "check-llm":
        client = llm_client_from_args(args)
        try:
            models = client.list_models()
        except RuntimeError as exc:
            raise SystemExit(str(exc))
        print(f"Connected to {client.base_url}")
        if models:
            print("Models:")
            for model in models:
                marker = " *" if model == client.model else ""
                print(f"  {model}{marker}")
        else:
            print("Endpoint responded, but no models were listed.")
    elif args.command == "build-brief":
        init_db(conn)
        brief_id = build_daily_brief(
            conn,
            brief_date=args.date,
            limit=args.limit,
            replace=args.replace,
        )
        print(f"Built brief {brief_id}")
        bdate = args.date or local_today()
        found = enrich_brief_images(conn, bdate)
        if found:
            print(f"Enriched {found} hero image(s)")
        print_brief(show_brief(conn, brief_date=args.date, limit=args.limit))
    elif args.command == "show-brief":
        print_brief(show_brief(conn, brief_date=args.date, limit=args.limit))


def list_recent(conn: sqlite3.Connection, limit: int, accepted_only: bool) -> None:
    where = "WHERE s.accepted = 1" if accepted_only else ""
    rows = conn.execute(
        f"""
        SELECT
            a.id,
            a.published_at,
            src.name AS source_name,
            a.title,
            a.canonical_url,
            s.accepted,
            s.constructive_score,
            s.cortisol_score,
            s.ragebait_score,
            s.reason_code
        FROM articles a
        JOIN sources src ON src.id = a.source_id
        LEFT JOIN article_scores s ON s.article_id = a.id
        {where}
        ORDER BY COALESCE(a.published_at, a.discovered_at) DESC
        LIMIT ?
        """,
        (limit,),
    ).fetchall()
    for row in rows:
        accepted = "yes" if row["accepted"] else "no"
        print(f"[{row['id']}] {row['published_at'] or 'no date'} | {row['source_name']} | accepted={accepted}")
        print(f"  {row['title']}")
        print(
            "  scores: "
            f"constructive={row['constructive_score']} "
            f"cortisol={row['cortisol_score']} "
            f"ragebait={row['ragebait_score']} "
            f"reason={row['reason_code']}"
        )
        print(f"  {row['canonical_url']}")


def print_preview(p: dict) -> None:
    mode = "model" if p["classified"] else "heuristic"
    print(f"Preview of {p['url']}  ({mode})")
    rate = p.get("acceptance_rate")
    rate_str = f"{rate * 100:.0f}%" if rate is not None else "— (all held)"
    print(f"  sampled={p['sampled']} accepted={p['accepted']} ({rate_str})")
    print(f"  freshness: newest={p['newest_published'] or 'unknown'} in_last_7d={p['recent_7d']}")
    print(f"  averages: cortisol={p['avg_cortisol']} ragebait={p['avg_ragebait']} pr_risk={p['avg_pr_risk']}")
    if p["topic_mix"]:
        print(f"  topics: {p['topic_mix']}")
        print(f"  flavors: {p['flavor_mix']}")
    if p["examples_accepted"]:
        print("  would accept:")
        for t in p["examples_accepted"]:
            print(f"    + {t[:80]}")
    if p["examples_rejected"]:
        print("  would skip:")
        for ex in p["examples_rejected"]:
            print(f"    - {ex['title'][:70]} ({ex['reason']})")


def check_feeds(conn: sqlite3.Connection, include_inactive: bool = False) -> None:
    where = "" if include_inactive else "WHERE active = 1"
    rows = conn.execute(f"SELECT name, feed_url FROM sources {where} ORDER BY name").fetchall()
    ok = 0
    for row in rows:
        try:
            items = parse_feed(fetch_feed(row["feed_url"]))
            ok += 1
            print(f"OK   {row['name']}: {len(items)} items")
        except Exception as exc:
            print(f"FAIL {row['name']}: {exc}")
    print(f"--- {ok}/{len(rows)} feeds healthy ---")


@contextlib.contextmanager
def cycle_lock(db_path):
    """Exclusive, non-blocking lock shared by the scheduled cycle and any manual job
    that mutates the corpus (e.g. a forced dedup re-cluster), so they can never overlap
    and contend on the database/model. Yields True if acquired, False if already held."""
    import fcntl

    lock_path = Path(db_path).parent / ".goodnews-cycle.lock"
    lock_file = open(lock_path, "w")
    try:
        fcntl.flock(lock_file, fcntl.LOCK_EX | fcntl.LOCK_NB)
    except OSError:
        lock_file.close()
        yield False
        return
    try:
        yield True
    finally:
        fcntl.flock(lock_file, fcntl.LOCK_UN)
        lock_file.close()


def run_cycle(conn: sqlite3.Connection, args: argparse.Namespace) -> None:
    """One end-to-end pass for a scheduler: poll due sources, classify the new
    arrivals, dedup, rebuild today's brief. Each step is independent and
    non-fatal so a down model endpoint or empty day never aborts the cycle.

    Holds an exclusive lock so a manual run and the systemd timer (or two timer
    ticks) can never overlap and contend on the database and model.
    """
    with cycle_lock(args.db) as acquired:
        if not acquired:
            print("cycle: another cycle is already running; skipping")
            return
        _run_cycle_locked(conn, args)


def _run_cycle_locked(conn: sqlite3.Connection, args: argparse.Namespace) -> None:
    init_db(conn)

    if args.force:
        poll_result = poll_all_sources(conn)
    else:
        poll_result = poll_due_sources(conn)
    print(f"poll: {_format_result(poll_result)}", flush=True)

    if not args.no_classify:
        client = llm_client_from_args(args)

        def _progress(done: int, total: int, article_id: int) -> None:
            print(f"  classify {done}/{total} (article {article_id})", flush=True)

        try:
            report = classify_articles(
                conn,
                client,
                limit=args.classify_limit,
                include_rejected=True,
                only_unclassified=True,
                progress=_progress,
            )
            print(
                f"classify: attempted={report.attempted} succeeded={report.succeeded} "
                f"skipped={report.skipped} (model {client.model})",
                flush=True,
            )
        except Exception as exc:  # endpoint down, timeout, etc. — keep going
            print(f"classify: skipped ({exc})", flush=True)

    if not args.no_dedup:
        try:
            stats = run_dedup(conn, llm_client_from_args(args))
            print(f"dedup: embedded={stats['embedded']} duplicates_hidden={stats['duplicates']}")
        except Exception as exc:
            print(f"dedup: skipped ({exc})")

    # Geo: tag newly-accepted, non-duplicate articles with subject geography (its own
    # LLM pass, decoupled from scoring). Bounded per cycle; idempotent (skips rows
    # already at the current GEO_VERSION). Non-fatal like every other step.
    if not args.no_geo:
        try:
            g = tag_geo(conn, llm_client_from_args(args), limit=args.geo_limit)
            print(f"geo: tagged={g['tagged']} errors={g['errors']} (of {g['candidates']} untagged)")
        except Exception as exc:
            print(f"geo: skipped ({exc})")

    # Daily Art: ensure the pool exists, then ensure today has a cached piece. No-ops
    # once the day is picked; non-fatal like every other step.
    if not args.no_art:
        try:
            a = art.run_daily(conn, client=LocalModelClient.from_env())   # client → the guide blurb
            print(f"art: pool={a['pool']} picked={a['picked_object']}")
        except Exception as exc:
            print(f"art: skipped ({exc})")

    # Small joys: On This Day (history), Quote of the Day, Word of the Day. Each is
    # bounded + non-fatal; one shared LLM client for tone/explainer/word proposals.
    if not args.no_joys:
        joy_client = LocalModelClient.from_env()
        try:
            o = onthisday.run_daily(conn, client=joy_client)
            print(f"onthisday: md={o['md']} picked={'yes' if o['picked'] else 'no'}")
        except Exception as exc:
            print(f"onthisday: skipped ({exc})")
        try:
            q = quote.run_daily(conn, client=joy_client)
            print(f"quote: pool={q['pool']} picked={q['picked']}")
        except Exception as exc:
            print(f"quote: skipped ({exc})")
        try:
            w = wotd.run_daily(conn, client=joy_client)
            print(f"word: pool={w['pool']} picked={w['picked']}")
        except Exception as exc:
            print(f"word: skipped ({exc})")

    if not args.no_brief:
        today = local_today()
        try:
            brief_id = build_daily_brief(conn, brief_date=today, limit=7, replace=True)
            found = enrich_brief_images(conn, today)
            print(f"brief: rebuilt {today} (id {brief_id}); {found} hero image(s) enriched")
        except Exception as exc:
            print(f"brief: skipped ({exc})")

        # Keep the Latest feed photo-rich: fetch quality og:images for the newest
        # accepted articles that lack one (bounded per cycle).
        try:
            recent = enrich_recent_images(conn)
            if recent:
                print(f"recent images: {recent} enriched")
        except Exception as exc:
            print(f"recent images: skipped ({exc})")

        # Full-article read-times: count words for recent accepted articles so the
        # front door can show "Full story · ~N min" next to our gist (bounded per cycle).
        try:
            reads = enrich_read_times(conn)
            if reads:
                print(f"read-times: {reads} counted")
        except Exception as exc:
            print(f"read-times: skipped ({exc})")

        # Pre-warm summaries for today's brief so Today reads as a calm briefing.
        # Idempotent: cached items are skipped, so this only hits the LLM for new ones.
        try:
            client = llm_client_from_args(args)
            ids = [r[0] for r in conn.execute(
                "SELECT bi.article_id FROM daily_briefs b JOIN daily_brief_items bi "
                "ON bi.brief_id = b.id WHERE b.brief_date = ?", (today,)
            )]
            made = 0
            for aid in ids:
                had = get_summary(conn, aid)
                generate_summary(conn, aid, client=client)
                if not had and get_summary(conn, aid):
                    made += 1
            print(f"summaries: {made} new (of {len(ids)} brief items)")
        except Exception as exc:
            print(f"summaries: skipped ({exc})")

    if not args.no_review:
        try:
            flagged = review_sources(conn)
            print(f"review: {len(flagged)} source(s) flagged for review (advisory)")
        except Exception as exc:
            print(f"review: skipped ({exc})")

    try:
        from .queries import reindex_search
        print(f"search: indexed {reindex_search(conn)} articles")
    except Exception as exc:  # noqa: BLE001 — search index is non-critical
        print(f"search: skipped ({exc})")

    if not args.no_digest:
        try:
            sent = send_due_digests(conn)  # morning-gated + deduped internally
            if sent:
                print(f"digest: sent {sent}")
        except Exception as exc:
            print(f"digest: skipped ({exc})")

    # Pre-generate today's daily puzzles (with the LLM 'why'); idempotent.
    try:
        made = generate_daily_puzzles(conn, local_today(), client=llm_client_from_args(args))
        if made:
            print(f"puzzles: generated {made}")
    except Exception as exc:
        print(f"puzzles: skipped ({exc})")


def serve(args: argparse.Namespace) -> None:
    try:
        import uvicorn
    except ModuleNotFoundError:
        raise SystemExit(
            "The web server needs the optional 'web' extra. Install it with:\n"
            "  pip install -e '.[web]'"
        )
    # Make sure the API reads the same database the CLI was pointed at.
    os.environ.setdefault("GOODNEWS_DB", str(args.db))
    print(f"Serving goodNews on http://{args.host}:{args.port}  (docs at /docs)")
    uvicorn.run("goodnews.api:app", host=args.host, port=args.port, reload=args.reload)


def list_category(
    conn: sqlite3.Connection,
    topic: str | None,
    flavor: str | None,
    limit: int,
    accepted_only: bool,
) -> None:
    clauses = []
    params: list = []
    if accepted_only:
        clauses.append("s.accepted = 1")
    if topic:
        clauses.append("s.topic = ?")
        params.append(topic.lower())
    if flavor:
        clauses.append("s.flavor = ?")
        params.append(flavor.lower())
    where = ("WHERE " + " AND ".join(clauses)) if clauses else ""
    params.append(limit)

    rows = conn.execute(
        f"""
        SELECT
            a.id, a.title, a.canonical_url, a.published_at,
            src.name AS source_name,
            s.topic, s.flavor, s.accepted,
            s.constructive_score, s.cortisol_score, s.reason_code,
            (s.constructive_score + s.agency_score + s.human_benefit_score + src.trust_score
             - s.cortisol_score - s.ragebait_score - s.pr_risk_score) AS rank_score
        FROM articles a
        JOIN sources src ON src.id = a.source_id
        JOIN article_scores s ON s.article_id = a.id
        {where}
        ORDER BY rank_score DESC, COALESCE(a.published_at, a.discovered_at) DESC
        LIMIT ?
        """,
        params,
    ).fetchall()

    label = " / ".join(filter(None, [topic, flavor])) or "all categories"
    print(f"{label} ({len(rows)} shown)")
    for row in rows:
        accepted = "" if row["accepted"] else " [not accepted]"
        print(f"[{row['id']}] {row['topic']}/{row['flavor']} | {row['source_name']}{accepted}")
        print(f"  {row['title']}")
        print(f"  score={row['rank_score']} reason={row['reason_code']}")
        print(f"  {row['canonical_url']}")


def llm_client_from_args(args: argparse.Namespace) -> LocalModelClient:
    client = LocalModelClient.from_env()
    if getattr(args, "base_url", None):
        client.base_url = args.base_url.rstrip("/")
    if getattr(args, "model", None):
        client.model = args.model
    return client


def list_sources(conn: sqlite3.Connection, active_only: bool) -> None:
    where = "WHERE active = 1" if active_only else ""
    rows = conn.execute(
        f"""
        SELECT id, name, active, default_category, trust_score, pr_risk_score, feed_url
        FROM sources
        {where}
        ORDER BY name
        """
    ).fetchall()
    for row in rows:
        state = "active" if row["active"] else "inactive"
        print(
            f"[{row['id']}] {row['name']} ({state}, {row['default_category']}, "
            f"trust={row['trust_score']}, pr={row['pr_risk_score']})"
        )
        print(f"  {row['feed_url']}")


def source_report(conn: sqlite3.Connection) -> None:
    rows = conn.execute(
        """
        SELECT
            src.name,
            src.default_category,
            src.trust_score,
            src.pr_risk_score AS source_pr_risk,
            src.review_flag,
            src.review_reason,
            src.consecutive_failures,
            COUNT(a.id) AS articles,
            SUM(CASE WHEN s.accepted = 1 THEN 1 ELSE 0 END) AS accepted,
            ROUND(AVG(s.constructive_score), 1) AS avg_constructive,
            ROUND(AVG(s.cortisol_score), 1) AS avg_cortisol,
            ROUND(AVG(s.ragebait_score), 1) AS avg_ragebait,
            MAX(a.published_at) AS newest_article
        FROM sources src
        LEFT JOIN articles a ON a.source_id = src.id
        LEFT JOIN article_scores s ON s.article_id = a.id
        GROUP BY src.id
        ORDER BY accepted DESC, articles DESC, src.name
        """
    ).fetchall()
    for row in rows:
        articles = row["articles"] or 0
        accepted = row["accepted"] or 0
        rate = (accepted / articles * 100) if articles else 0
        print(
            f"{row['name']} | {row['default_category']} | "
            f"articles={articles} accepted={accepted} ({rate:.1f}%)"
        )
        print(
            f"  trust={row['trust_score']} pr={row['source_pr_risk']} "
            f"avg_constructive={row['avg_constructive']} "
            f"avg_cortisol={row['avg_cortisol']} "
            f"avg_ragebait={row['avg_ragebait']}"
        )
        print(f"  newest={row['newest_article'] or 'none'}")
        if row["review_flag"]:
            print(f"  ⚑ review: {row['review_reason']}")


def list_runs(conn: sqlite3.Connection, limit: int) -> None:
    rows = conn.execute(
        """
        SELECT r.id, r.started_at, r.finished_at, r.status, src.name AS source_name,
               r.items_seen, r.items_inserted, r.items_duplicate, r.error
        FROM ingest_runs r
        LEFT JOIN sources src ON src.id = r.source_id
        ORDER BY r.id DESC
        LIMIT ?
        """,
        (limit,),
    ).fetchall()
    for row in rows:
        print(
            f"[{row['id']}] {row['status']} | {row['source_name'] or 'unknown'} | "
            f"seen={row['items_seen']} inserted={row['items_inserted']} duplicate={row['items_duplicate']}"
        )
        if row["error"]:
            print(f"  error: {row['error']}")


def rescore_articles(conn: sqlite3.Connection) -> int:
    rows = conn.execute(
        """
        SELECT a.id, a.title, a.description, src.pr_risk_score
        FROM articles a
        JOIN sources src ON src.id = a.source_id
        ORDER BY a.id
        """
    ).fetchall()
    for row in rows:
        scores = score_article(row["title"], row["description"], int(row["pr_risk_score"]))
        conn.execute(
            """
            INSERT INTO article_scores (
                article_id, constructive_score, cortisol_score, ragebait_score,
                agency_score, human_benefit_score, novelty_score, pr_risk_score,
                accepted, reason_code, reason_text, model_name, scored_at
            )
            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
            ON CONFLICT(article_id) DO UPDATE SET
                constructive_score = excluded.constructive_score,
                cortisol_score = excluded.cortisol_score,
                ragebait_score = excluded.ragebait_score,
                agency_score = excluded.agency_score,
                human_benefit_score = excluded.human_benefit_score,
                novelty_score = excluded.novelty_score,
                pr_risk_score = excluded.pr_risk_score,
                accepted = excluded.accepted,
                reason_code = excluded.reason_code,
                reason_text = excluded.reason_text,
                model_name = excluded.model_name,
                scored_at = CURRENT_TIMESTAMP
            """,
            (
                row["id"],
                scores["constructive_score"],
                scores["cortisol_score"],
                scores["ragebait_score"],
                scores["agency_score"],
                scores["human_benefit_score"],
                scores["novelty_score"],
                scores["pr_risk_score"],
                scores["accepted"],
                scores["reason_code"],
                scores["reason_text"],
                scores["model_name"],
            ),
        )
    conn.commit()
    return len(rows)


def print_brief(rows: list[sqlite3.Row]) -> None:
    if not rows:
        print("No brief items found.")
        return
    date = rows[0]["brief_date"]
    print(f"Highlights from Today - {date}")
    for row in rows:
        print(f"{row['rank']}. {row['title']}")
        print(f"   {row['source_name']} | {row['default_category']} | {row['model_name']}")
        print(f"   reason: {row['reason_code']}")
        print(f"   {row['canonical_url']}")


def _format_result(result: dict) -> str:
    if "sources" in result:
        return (
            f"Polled {result['sources']} sources: seen={result['seen']} "
            f"inserted={result['inserted']} duplicate={result['duplicate']} failed={result['failed']}"
        )
    if result.get("status") == "failed":
        return (
            f"Poll failed: seen={result['seen']} inserted={result['inserted']} "
            f"duplicate={result['duplicate']} error={result['error']}"
        )
    return (
        f"Poll ok: seen={result['seen']} inserted={result['inserted']} "
        f"duplicate={result['duplicate']}"
    )


if __name__ == "__main__":
    main()