upbeatBytes/goodnews/geo.py

"""Subject-geography for articles ("where is this story ABOUT").

Kept deliberately separate from scoring (see article_geo / article_places in db.py):
geography is durable metadata, scoring is volatile. The LLM proposes place NAMES;
this module disposes by normalizing to ISO codes in code, never trusting the model's
free text (so "Europe" never gets stored as a country). 'global'/placeless is a real,
first-class result, not a failure. "local" is NOT stored — it's relative to the reader;
the UI decides "Near you" by comparing these places to the visitor's chosen home.
"""
from __future__ import annotations

import json
import re
import sqlite3

from .llm import LocalModelClient, parse_classifier_json

# Bump when the prompt/taxonomy changes, so a re-backfill can target stale rows.
GEO_VERSION = "geo-v1"

BREADTHS = ("locality", "regional", "national", "multinational", "global", "unknown")
CONFIDENCES = ("high", "medium", "low")

# --- normalization data (LLM returns names; we map to ISO, drop the unmappable) ----

US_STATES = {
    "alabama": "AL", "alaska": "AK", "arizona": "AZ", "arkansas": "AR", "california": "CA",
    "colorado": "CO", "connecticut": "CT", "delaware": "DE", "florida": "FL", "georgia": "GA",
    "hawaii": "HI", "idaho": "ID", "illinois": "IL", "indiana": "IN", "iowa": "IA",
    "kansas": "KS", "kentucky": "KY", "louisiana": "LA", "maine": "ME", "maryland": "MD",
    "massachusetts": "MA", "michigan": "MI", "minnesota": "MN", "mississippi": "MS",
    "missouri": "MO", "montana": "MT", "nebraska": "NE", "nevada": "NV", "new hampshire": "NH",
    "new jersey": "NJ", "new mexico": "NM", "new york": "NY", "north carolina": "NC",
    "north dakota": "ND", "ohio": "OH", "oklahoma": "OK", "oregon": "OR", "pennsylvania": "PA",
    "rhode island": "RI", "south carolina": "SC", "south dakota": "SD", "tennessee": "TN",
    "texas": "TX", "utah": "UT", "vermont": "VT", "virginia": "VA", "washington": "WA",
    "west virginia": "WV", "wisconsin": "WI", "wyoming": "WY",
    "district of columbia": "DC", "washington dc": "DC", "washington d c": "DC",
}

# Common countries + aliases (extensible). Anything not here returns None -> we drop
# the country rather than store garbage. breadth still captures national/global, etc.
COUNTRY_TO_ISO = {
    "united states": "US", "united states of america": "US", "usa": "US", "us": "US", "america": "US",
    "united kingdom": "GB", "uk": "GB", "britain": "GB", "great britain": "GB", "england": "GB",
    "scotland": "GB", "wales": "GB", "northern ireland": "GB",
    "canada": "CA", "australia": "AU", "new zealand": "NZ", "ireland": "IE",
    "france": "FR", "germany": "DE", "spain": "ES", "portugal": "PT", "italy": "IT",
    "netherlands": "NL", "belgium": "BE", "luxembourg": "LU", "switzerland": "CH", "austria": "AT",
    "denmark": "DK", "sweden": "SE", "norway": "NO", "finland": "FI", "iceland": "IS",
    "poland": "PL", "czech republic": "CZ", "czechia": "CZ", "slovakia": "SK", "hungary": "HU",
    "greece": "GR", "romania": "RO", "bulgaria": "BG", "croatia": "HR", "serbia": "RS",
    "ukraine": "UA", "russia": "RU", "turkey": "TR", "turkiye": "TR",
    "china": "CN", "japan": "JP", "south korea": "KR", "korea": "KR", "north korea": "KP",
    "india": "IN", "pakistan": "PK", "bangladesh": "BD", "sri lanka": "LK", "nepal": "NP",
    "indonesia": "ID", "malaysia": "MY", "singapore": "SG", "thailand": "TH", "vietnam": "VN",
    "philippines": "PH", "taiwan": "TW", "hong kong": "HK",
    "israel": "IL", "palestine": "PS", "saudi arabia": "SA", "united arab emirates": "AE",
    "uae": "AE", "qatar": "QA", "iran": "IR", "iraq": "IQ", "egypt": "EG", "jordan": "JO",
    "south africa": "ZA", "nigeria": "NG", "kenya": "KE", "ethiopia": "ET", "ghana": "GH",
    "tanzania": "TZ", "uganda": "UG", "rwanda": "RW", "morocco": "MA", "tunisia": "TN",
    "mexico": "MX", "brazil": "BR", "argentina": "AR", "chile": "CL", "colombia": "CO",
    "peru": "PE", "venezuela": "VE", "ecuador": "EC", "bolivia": "BO", "uruguay": "UY",
    "costa rica": "CR", "panama": "PA", "guatemala": "GT", "cuba": "CU", "jamaica": "JM",
}

# Words that look like countries but are regions/continents -> never a country_code.
_NON_COUNTRY = {"europe", "asia", "africa", "north america", "south america", "latin america",
                "the americas", "middle east", "scandinavia", "eu", "european union", "world",
                "global", "international", "earth", "the world"}


def _norm_key(name) -> str:
    s = re.sub(r"[^a-z0-9 ]", " ", str(name or "").lower())
    s = re.sub(r"\bthe\b", " ", s)
    return re.sub(r"\s+", " ", s).strip()


def normalize_country(name) -> str | None:
    key = _norm_key(name)
    if not key or key in _NON_COUNTRY:
        return None
    return COUNTRY_TO_ISO.get(key)


def normalize_state(name, country_code) -> str | None:
    if country_code != "US":
        return None  # only US subdivisions for v1
    return US_STATES.get(_norm_key(name))


def normalize_places(raw) -> list[dict]:
    """LLM place dicts -> cleaned, deduped [{country_code, state_code, locality}]."""
    out, seen = [], set()
    if not isinstance(raw, list):
        return out
    for p in raw:
        if not isinstance(p, dict):
            continue
        cc = normalize_country(p.get("country"))
        sc = normalize_state(p.get("state_province"), cc)
        loc = str(p.get("locality") or "").strip() or None
        if not (cc or sc or loc):
            continue  # entirely empty -> drop
        key = (cc, sc, (loc or "").lower())
        if key in seen:
            continue
        seen.add(key)
        out.append({"country_code": cc, "state_code": sc, "locality": loc})
    return out


# --- LLM extraction (separate pass; does not touch the scoring prompt) ------------

SYSTEM = (
    "You tag the real-world geography of a news story for a calm good-news site. "
    "Identify the place(s) the story is fundamentally ABOUT or where it HAPPENED, "
    "NOT places mentioned only in passing. Many good-news stories (general science, "
    "space, broad research, health) have no specific place; those are 'global'. If a "
    "location is only incidental or genuinely unclear, use 'unknown'. Never guess. "
    "Reply with ONLY a JSON object, no prose."
)

INSTRUCT = (
    "Return JSON exactly like:\n"
    '{"breadth": "<locality|regional|national|multinational|global|unknown>", '
    '"places": [{"country": "<name or null>", "state_province": "<name or null>", '
    '"locality": "<city/town or null>"}], "confidence": "<high|medium|low>", '
    '"rationale": "<one short clause: where it happened and why>"}\n'
    "breadth: locality=a specific city/town/county; regional=a state/province/region; "
    "national=about a whole country; multinational=a few specific countries; "
    "global=worldwide or no specific country; unknown=incidental/unclear. "
    "places may list more than one when a story genuinely spans regions; use null for parts you can't support.\n"
    "confidence: use 'high' ONLY when the location is explicitly stated or unmistakable; "
    "'medium' when reasonably inferred; 'low' when shaky. Do NOT default to high."
)


def _article_text(row) -> str:
    parts = [f"TITLE: {row['title']}"]
    for label, key in (("SUMMARY", "summary"), ("WHAT HAPPENED", "what_happened"),
                       ("WHY IT MATTERS", "why_matters"), ("PUBLISHER BLURB", "description")):
        try:
            v = row[key]
        except (KeyError, IndexError):
            v = None
        if v:
            parts.append(f"{label}: {v}")
    return "\n".join(parts)


def classify_geo(client: LocalModelClient, row) -> dict:
    """One geo pass over an article row -> normalized result. Raises on unparseable."""
    messages = [
        {"role": "system", "content": SYSTEM},
        {"role": "user", "content": _article_text(row) + "\n\n" + INSTRUCT},
    ]
    data = parse_classifier_json(client.chat_text(messages))
    breadth = data.get("breadth")
    if breadth not in BREADTHS:
        breadth = "unknown"
    confidence = data.get("confidence")
    if confidence not in CONFIDENCES:
        confidence = "low"
    return {
        "breadth": breadth,
        "confidence": confidence,
        "rationale": (str(data.get("rationale") or "")[:300]) or None,
        "places": normalize_places(data.get("places")),
    }


def store_geo(conn: sqlite3.Connection, article_id: int, result: dict, version: str = GEO_VERSION) -> None:
    """Upsert article_geo and replace article_places. Geo is fully re-derivable, so
    replacing places (unlike scores, which we never delete) is safe."""
    conn.execute(
        "INSERT INTO article_geo (article_id, breadth, confidence, rationale, geo_version, updated_at) "
        "VALUES (?,?,?,?,?, datetime('now')) "
        "ON CONFLICT(article_id) DO UPDATE SET breadth=excluded.breadth, confidence=excluded.confidence, "
        "rationale=excluded.rationale, geo_version=excluded.geo_version, updated_at=excluded.updated_at",
        (article_id, result["breadth"], result["confidence"], result.get("rationale"), version),
    )
    conn.execute("DELETE FROM article_places WHERE article_id=?", (article_id,))
    for i, p in enumerate(result.get("places") or []):
        conn.execute(
            "INSERT INTO article_places (article_id, country_code, state_code, locality, ord) VALUES (?,?,?,?,?)",
            (article_id, p.get("country_code"), p.get("state_code"), p.get("locality"), i),
        )


def tag_articles(conn: sqlite3.Connection, client: LocalModelClient, limit: int = 200,
                 reclassify: bool = False) -> dict:
    """Tag accepted, non-duplicate articles that lack current geo. Idempotent: skips
    rows already at GEO_VERSION unless reclassify=True. Used both by the cycle (new
    articles) and the backfill (existing ones). Per-article failure is non-fatal."""
    if reclassify:
        where = "1=1"
    else:
        where = "(g.article_id IS NULL OR g.geo_version IS NOT ?)"
    rows = conn.execute(
        f"""SELECT a.id, a.title, a.description,
                   sm.summary, sm.what_happened, sm.why_matters
            FROM articles a
            JOIN article_scores s ON s.article_id = a.id
            LEFT JOIN article_summaries sm ON sm.article_id = a.id
            LEFT JOIN article_geo g ON g.article_id = a.id
            WHERE s.accepted = 1 AND a.duplicate_of IS NULL AND {where}
            ORDER BY a.discovered_at DESC
            LIMIT ?""",
        (() if reclassify else (GEO_VERSION,)) + (limit,),
    ).fetchall()
    tagged = errors = 0
    for r in rows:
        try:
            store_geo(conn, r["id"], classify_geo(client, r))
            # Keep live auth/admin writes healthy while the scheduled cycle runs.
            # Geo classification calls the LLM per article; if we batch commits, the
            # first stored article opens a write transaction that can stay open while
            # the next several LLM calls run. That starves login/session writes long
            # enough to trip SQLite's busy timeout. Commit each successful article so
            # the writer lock is held for milliseconds, not minutes.
            conn.commit()
            tagged += 1
        except Exception:  # noqa: BLE001 — non-fatal, like other cycle steps
            conn.rollback()
            errors += 1
    conn.commit()
    return {"candidates": len(rows), "tagged": tagged, "errors": errors}