upstream: integrar 0.9.5 en 34 ficheros mas y portar los modulos nuevos
Segunda tanda. Resueltas por merge de tres vias 15 divergencias que no solapaban, y aplicada la version de 0.9.5 en 19 vistas y modelos donde la rama solo arrastraba codigo antiguo (firmas viejas de renderProductForm, renderJobForm, listTags sin search, y el import sin renderSpreadEditWarning). Se preserva la eliminacion de housing donde la rama lo habia descartado: modules_view, blockchain_view, search_model y tags_model. Modulos nuevos del dev portados: blog, data, gallery, mentions, polls, workflows, comments, recurrence y media_gallery, mas contentPdf, pdfDocument y content_favorites. main_views recupera renderEngagement, spreadsFor y renderSpreadEditWarning, que la rama habia eliminado y que las vistas de 0.9.5 necesitan. Sin ellas polls_view lanzaba TypeError al editar. El overlay de i18n pasa de 15 a 41 claves (46 en castellano): incorpora las 9 que upstream tenia en 0.9.1 y elimino en 0.9.5 mientras nuestro codigo las sigue usando (menuBlogs, spreadHint, chatShareUrl, forumFilterHot, publishBlog...) y las 17 de Karvan, que nunca existieron y tiraban de texto de reserva en ingles; ahora estan traducidas al castellano. Verificado con la app arrancada: declara 0.9.5, 0 claves i18n sin resolver (antes 26), 26 rutas responden 200/302, y la interfaz de la rama sale intacta: 10 categorias y 47 modulos en los hexagonos, topbar con Personal/Community y avatar, 7 accesos rapidos en la barra inferior y 0 etiquetas vacias (antes 1, la de Blogs).
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SITO
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43 changed files with 3221 additions and 2416 deletions
292
src/models/data_model.js
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292
src/models/data_model.js
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const pull = require('../server/node_modules/pull-stream');
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const { getConfig } = require('../configs/config-manager.js');
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const { buildValidatedTombstoneSet } = require('./tombstone_validator');
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const logLimit = getConfig().ssbLogStream?.limit || 1000;
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const norm = (v) => String(v == null ? '' : v).trim().toLowerCase();
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const KINDS = {
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inhabitants: { type: 'curriculum', href: (id, c) => `/author/${encodeURIComponent(c.author)}` },
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jobs: { type: 'job', href: (id) => `/jobs/${encodeURIComponent(id)}` },
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projects: { type: 'project', href: (id) => `/projects/${encodeURIComponent(id)}` },
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events: { type: 'event', href: (id) => `/events/${encodeURIComponent(id)}` },
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tribes: { type: 'tribe', href: (id) => `/tribe/${encodeURIComponent(id)}` },
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market: { type: 'market', href: (id) => `/market/${encodeURIComponent(id)}` },
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housing: { type: 'housing', href: (id) => `/housing/${encodeURIComponent(id)}` },
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industry: { type: 'industry', href: (id) => `/industry/${encodeURIComponent(id)}` },
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tasks: { type: 'task', href: (id) => `/tasks/${encodeURIComponent(id)}` },
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reports: { type: 'report', href: (id) => `/reports/${encodeURIComponent(id)}` },
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votes: { type: 'poll', href: (id) => `/polls/${encodeURIComponent(id)}` },
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audios: { type: 'audio', href: (id) => `/audios/${encodeURIComponent(id)}` },
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videos: { type: 'video', href: (id) => `/videos/${encodeURIComponent(id)}` },
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images: { type: 'image', href: (id) => `/images/${encodeURIComponent(id)}` },
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documents: { type: 'document', href: (id) => `/documents/${encodeURIComponent(id)}` },
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bookmarks: { type: 'bookmark', href: (id) => `/bookmarks/${encodeURIComponent(id)}` },
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torrents: { type: 'torrent', href: (id) => `/torrents/${encodeURIComponent(id)}` },
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chats: { type: 'chat', href: (id) => `/chats/${encodeURIComponent(id)}` },
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pads: { type: 'pad', href: (id) => `/pads/${encodeURIComponent(id)}` },
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maps: { type: 'map', href: (id) => `/maps/${encodeURIComponent(id)}` },
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calendars: { type: 'calendar', href: (id) => `/calendars/${encodeURIComponent(id)}` },
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forum: { type: 'forum', href: (id) => `/forum/${encodeURIComponent(id)}` }
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};
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const KIND_BY_TYPE = Object.fromEntries(Object.entries(KINDS).map(([k, v]) => [v.type, k]));
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const cvSkills = (c) => [
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...(c.personalSkills || []),
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...(c.oasisSkills || []),
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...(c.educationalSkills || []),
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...(c.professionalSkills || [])
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];
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const PLACEHOLDERS = new Set(['unknown', 'n/a', 'na', 'none', '-', 'other']);
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const TITLE_STOPWORDS = new Set([
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'the', 'this', 'that', 'and', 'for', 'with', 'from', 'into', 'about', 'new', 'all', 'not', 'are', 'was', 'you', 'your', 'our',
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'los', 'las', 'del', 'una', 'unos', 'unas', 'este', 'esta', 'esto', 'que', 'con', 'para', 'por', 'sin', 'sobre', 'bajo', 'mas', 'más'
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]);
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const titleTerms = (c) => String(c.title || c.name || c.question || c.concept || '')
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.toLowerCase()
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.split(/[^\p{L}\p{N}]+/u)
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.filter(w => w.length >= 3 && !TITLE_STOPWORDS.has(w) && !/^\d+$/.test(w));
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const termsOf = (kind, c) => {
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const out = [];
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if (kind === 'inhabitants') {
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out.push(...cvSkills(c));
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if (c.languages) out.push(...String(c.languages).split(/[,;]/));
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} else if (kind === 'jobs') {
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out.push(...(c.tasks || []), ...(c.tags || []), c.job_type, c.location);
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} else if (kind === 'industry') {
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out.push(...(c.tags || []), c.sector, ...(c.skills || []));
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} else {
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out.push(...(c.tags || []));
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if (c.category) out.push(c.category);
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}
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return Array.from(new Set(out.map(norm).filter(t => t && !PLACEHOLDERS.has(t))));
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};
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const titleOf = (kind, c, author) => {
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if (kind === 'inhabitants') return c.name || author || '';
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return c.title || c.name || c.question || c.concept || '';
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};
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const jaccard = (a, b) => {
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if (!a.size || !b.size) return { score: 0, common: [] };
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const common = [...b].filter(x => a.has(x));
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const union = a.size + b.size - common.length;
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return { score: union > 0 ? common.length / union : 0, common };
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};
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module.exports = ({ cooler }) => {
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let ssb;
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const openSsb = async () => { if (!ssb) ssb = await cooler.open(); return ssb; };
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const getAllMessages = async (ssbClient) =>
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new Promise((resolve, reject) => {
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pull(
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ssbClient.createLogStream({ limit: logLimit }),
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pull.collect((err, msgs) => (err ? reject(err) : resolve(msgs)))
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);
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});
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const buildGraph = async () => {
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const ssbClient = await openSsb();
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const viewerId = ssbClient.id;
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const messages = await getAllMessages(ssbClient);
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const tomb = buildValidatedTombstoneSet(messages);
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const latestByKey = new Map();
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const replaced = new Set();
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for (const m of messages) {
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const v = m && m.value;
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const c = v && v.content;
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if (!c || typeof c !== 'object' || !c.type) continue;
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if (tomb.has(m.key)) continue;
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if (c.encryptedPayload || c.encryptedQuestion) continue;
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if (c.tribeId && c.type !== 'tribe') continue;
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const kind = KIND_BY_TYPE[c.type];
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if (!kind) continue;
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if (typeof c.replaces === 'string') replaced.add(c.replaces);
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latestByKey.set(m.key, { key: m.key, author: v.author, ts: v.timestamp || m.timestamp || 0, kind, c });
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}
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const nodes = [];
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const byAuthorCv = new Map();
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for (const node of latestByKey.values()) {
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if (replaced.has(node.key)) continue;
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const coreTerms = termsOf(node.kind, node.c);
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const extra = node.kind === 'inhabitants' ? [] : titleTerms(node.c).map(norm).filter(t => t && !PLACEHOLDERS.has(t));
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const terms = Array.from(new Set([...coreTerms, ...extra]));
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if (!terms.length) continue;
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const entry = {
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id: node.key,
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kind: node.kind,
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author: node.c.author || node.author,
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title: titleOf(node.kind, node.c, node.c.author || node.author),
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terms,
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termSet: new Set(terms),
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coreTermSet: new Set(coreTerms),
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ts: node.ts,
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createdAt: node.c.createdAt || new Date(node.ts).toISOString(),
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href: KINDS[node.kind].href(node.key, node.c)
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};
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if (node.kind === 'inhabitants') {
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const prev = byAuthorCv.get(entry.author);
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if (prev && prev.ts >= entry.ts) continue;
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byAuthorCv.set(entry.author, entry);
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continue;
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}
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nodes.push(entry);
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}
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for (const cv of byAuthorCv.values()) nodes.push(cv);
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return { viewerId, nodes, cvByAuthor: byAuthorCv };
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};
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const MAX_ENTITIES = 400;
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const MAX_PAIRS = 300;
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const strip = (n) => ({
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id: n.id, kind: n.kind, author: n.author, title: n.title,
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href: n.href, createdAt: n.createdAt, ts: n.ts
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});
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return {
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KINDS: Object.keys(KINDS),
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async listMatches(filter = 'ALL', opts = {}) {
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const { viewerId, nodes, cvByAuthor } = await buildGraph();
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const use = nodes.slice(0, MAX_ENTITIES);
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const f = String(filter || 'ALL').toUpperCase();
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const myTermSet = new Set();
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const mineCv = cvByAuthor.get(viewerId);
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if (mineCv) for (const t of mineCv.terms) myTermSet.add(t);
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for (const n of use) {
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if (String(n.author) === String(viewerId)) for (const t of n.terms) myTermSet.add(t);
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}
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const df = new Map();
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for (const n of use) for (const t of n.termSet) df.set(t, (df.get(t) || 0) + 1);
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const total = use.length || 1;
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const weightOf = (t) => Math.log(1 + total / (df.get(t) || 1));
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let out = [];
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for (const n of use) {
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if (String(n.author) === String(viewerId)) continue;
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const common = [...n.termSet].filter(t => myTermSet.has(t));
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if (!common.length) continue;
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let commonW = 0;
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for (const t of common) commonW += weightOf(t);
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let itemW = 0;
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for (const t of n.termSet) itemW += weightOf(t);
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const score = itemW > 0 ? Math.min(1, commonW / itemW) : 0;
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if (score <= 0) continue;
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common.sort((x, y) => weightOf(y) - weightOf(x) || x.localeCompare(y));
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out.push({ ...strip(n), score, common, connections: common.length });
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}
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if (f === 'RECENT') {
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const cutoff = Date.now() - 7 * 24 * 60 * 60 * 1000;
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out = out.filter(s => s.ts >= cutoff);
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} else if (f !== 'ALL' && f !== 'TOP') {
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const kind = f.toLowerCase();
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if (KINDS[kind]) out = out.filter(s => s.kind === kind);
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}
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const q = norm(opts.q);
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if (q) out = out.filter(s => norm(s.title).includes(q) || s.common.some(t => t.includes(q)));
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if (f === 'RECENT') out.sort((x, y) => y.ts - x.ts || y.score - x.score);
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else out.sort((x, y) => y.score - x.score || y.ts - x.ts);
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return {
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matches: out.slice(0, MAX_PAIRS),
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total: out.length,
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hasProfile: myTermSet.size > 0,
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myTerms: [...myTermSet]
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};
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},
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async jobMatchesFor(viewerId, { minScore = 0.8 } = {}) {
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const { nodes, cvByAuthor } = await buildGraph();
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const mine = cvByAuthor.get(viewerId);
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if (!mine) return [];
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return nodes
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.filter(n => n.kind === 'jobs' && String(n.author) !== String(viewerId))
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.map(n => {
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const { score, common } = jaccard(mine.termSet, n.coreTermSet || n.termSet);
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return { id: n.id, title: n.title, author: n.author, href: n.href, score, common };
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})
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.filter(m => m.score >= minScore)
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.sort((a, b) => b.score - a.score);
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},
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async cohesion() {
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const { cvByAuthor, nodes } = await buildGraph();
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const use = nodes.slice(0, MAX_ENTITIES);
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let comparisons = 0;
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let sum = 0;
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let connectedPairs = 0;
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const linkedEntities = new Set();
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for (let i = 0; i < use.length; i++) {
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for (let j = i + 1; j < use.length; j++) {
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const a = use[i], b = use[j];
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if (a.kind === 'inhabitants' && b.kind === 'inhabitants' && a.author === b.author) continue;
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const { score } = jaccard(a.termSet, b.termSet);
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comparisons += 1;
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sum += score;
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if (score > 0) { connectedPairs += 1; linkedEntities.add(a.id); linkedEntities.add(b.id); }
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}
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}
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const coefficient = comparisons > 0 ? sum / comparisons : 0;
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const people = [...cvByAuthor.values()];
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const linked = new Set();
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let cvComparisons = 0;
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let cvSum = 0;
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for (let i = 0; i < people.length; i++) {
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for (let j = i + 1; j < people.length; j++) {
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const { score } = jaccard(people[i].termSet, people[j].termSet);
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cvComparisons += 1;
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cvSum += score;
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if (score > 0) { linked.add(people[i].author); linked.add(people[j].author); }
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}
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}
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const cvCoefficient = cvComparisons > 0 ? cvSum / cvComparisons : 0;
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const skillSet = new Set();
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for (const p of people) for (const t of p.terms) skillSet.add(t);
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const termCount = new Map();
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for (const n of nodes) for (const t of n.terms) termCount.set(t, (termCount.get(t) || 0) + 1);
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const topTerms = [...termCount.entries()]
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.sort((a, b) => b[1] - a[1] || a[0].localeCompare(b[0]))
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.slice(0, 12)
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.map(([term, count]) => ({ term, count }));
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const perKind = {};
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for (const n of nodes) perKind[n.kind] = (perKind[n.kind] || 0) + 1;
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return {
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coefficient,
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percent: Math.round(coefficient * 1000) / 10,
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comparisons,
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pairs: connectedPairs,
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entities: use.length,
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distinctTerms: termCount.size,
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topTerms,
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perKind,
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people: people.length,
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skills: skillSet.size,
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connected: linkedEntities.size,
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isolated: Math.max(0, use.length - linkedEntities.size),
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cvCoefficient,
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cvPercent: Math.round(cvCoefficient * 1000) / 10
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};
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}
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};
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};
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