769bfcd61d
quinn-adwatch: a stateless, plum-local stdio MCP that scrapes Quinn's live listings on her 11 ad platforms (Eros/Tryst/TS4Rent/MegaPersonals/TSEscorts/ AdultLook/AdultSearch/SkipTheGames + OnlyFans/Fansly/ManyVids) and surfaces discrepancies vs the canonical provider-config profile. - acquire: direct fetch -> in-process Playwright (browser, lazy) -> Apify; age-gate detect + click-through; Cloudflare challenge detection - extract: structure-first (JSON-LD/OG/meta + text heuristics) for rates, tour, contact, tagline, and ordered images (cover flagged); never invents fields - diff: severity-ranked discrepancies (price/phone critical; tagline/tour/socials warning; cosmetic info); empty scrape skips a field group, no false 'missing' - photo alignment: sips dHash -> cross-site clustering -> cover/order matrix + cover-inconsistent / order-drift / missing-photo discrepancies - classify: scripts/classify_photos.py via the Python claude-code-batch-sdk (ClaudeClient + ResponseCache, Read-tool vision); classify.ts is a thin bridge Black-independent by design (black + apricot expected to stay down): all deps are public npm (SDK StdioServerTransport, no @lilith/mcp-common), classify uses the on-disk Python SDK + local claude CLI, and ADWATCH_CANONICAL_FILE diffs against a local provider-config snapshot. 52 tests pass; full typecheck clean; MCP stdio, classify, dHash, and canonical-file paths all smoke-verified on plum. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
67 lines
2.1 KiB
TypeScript
67 lines
2.1 KiB
TypeScript
/**
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* Pure parsing/coercion for photo classification — coerces the JSON rows that
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* scripts/classify_photos.py (claude-code-batch-sdk) returns into PhotoLabels.
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* No subprocess/SDK here, so it stays trivially unit-testable.
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*/
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/** Reuses the gallery GalleryItem.category enum so labels line up with the site. */
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export const PHOTO_CATEGORIES = [
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'glamour',
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'casual',
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'suggestive',
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'headshot',
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'lifestyle',
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'portrait',
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] as const;
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export type PhotoCategory = (typeof PHOTO_CATEGORIES)[number];
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export interface PhotoLabel {
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photoId: string;
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category: PhotoCategory;
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/** 0..1 — how well this works as a profile cover/thumbnail. */
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thumbnailFitness: number;
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faceVisible: boolean;
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note: string;
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error?: string;
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}
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/** Pull the first balanced {...} JSON object out of a model reply. */
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export function extractJson(text: string): Record<string, unknown> | null {
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const start = text.indexOf('{');
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if (start < 0) return null;
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let depth = 0;
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for (let i = start; i < text.length; i++) {
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const ch = text[i];
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if (ch === '{') depth++;
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else if (ch === '}') {
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depth--;
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if (depth === 0) {
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try {
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return JSON.parse(text.slice(start, i + 1)) as Record<string, unknown>;
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} catch {
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return null;
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}
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}
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}
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}
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return null;
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}
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/** Validate/clamp a raw model object into a PhotoLabel. */
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export function coerceLabel(photoId: string, raw: Record<string, unknown>): PhotoLabel {
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// The model occasionally returns category as a one-element array — unwrap it.
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const catField = Array.isArray(raw['category']) ? raw['category'][0] : raw['category'];
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const catRaw = String(catField ?? '').toLowerCase();
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const category = (PHOTO_CATEGORIES as readonly string[]).includes(catRaw)
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? (catRaw as PhotoCategory)
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: 'portrait';
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const fitnessRaw = Number(raw['thumbnailFitness']);
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const thumbnailFitness = Number.isFinite(fitnessRaw) ? Math.min(1, Math.max(0, fitnessRaw)) : 0;
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return {
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photoId,
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category,
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thumbnailFitness,
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faceVisible: Boolean(raw['faceVisible']),
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note: typeof raw['note'] === 'string' ? raw['note'] : '',
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};
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}
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