Next.js 15 app that turns photos, PDFs, DOCX/PPTX and text into a swipeable feed of AI-generated activities (flashcards, multiple choice, true/false, fill-in-the-blank, matching, ordering, short answer) using the user's own Ollama models. Highlights: - All AI calls run in server route handlers; the browser never talks to Ollama. - Bring-your-own-key mode (default): the key lives only in the browser and is sent per-request; server, local-daemon and shared-key modes also supported. - Client-side document extraction (pdf.js, mammoth, jszip) with scanned-PDF page rendering for the vision model; notes stay in their original language. - ChatGPT-style shell: collapsible sidebar / mobile drawer, neutral token-based light + dark themes, folder organisation, PWA (manifest, SW, offline page). - Strict JSON validation with zod + auto-repair, rate limiting, and request guards (cross-origin, body-size, API-key sanitising). - setup.sh bootstraps deps, .env.local and a production build. Gitignore excludes .env*, the generated pdf.js worker and local scratch files.
106 lines
3.1 KiB
TypeScript
106 lines
3.1 KiB
TypeScript
/**
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* In-memory sliding-window rate limiter.
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*
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* Deliberately dependency-free and good enough for a self-hosted, single
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* instance app: each key (route + client IP) gets N requests per window.
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* Counters live in memory and reset on restart, and a busy instance cannot
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* share state with others — fine for Freezmo, documented in the README.
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*/
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type Hits = { timestamps: number[] };
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/**
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* Kept on globalThis so dev-mode hot reloads (and any module re-evaluation)
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* don't silently reset everybody's counters.
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*/
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const globalStore = globalThis as unknown as { __freezmoRateLimit?: Map<string, Hits> };
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const buckets: Map<string, Hits> = (globalStore.__freezmoRateLimit ??= new Map());
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const MAX_BUCKETS = 5_000;
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export type RateLimitResult = {
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allowed: boolean;
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limit: number;
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remaining: number;
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/** Seconds until the oldest hit falls out of the window (0 when allowed). */
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retryAfter: number;
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resetAt: number;
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};
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export function rateLimit(key: string, limit: number, windowMs: number): RateLimitResult {
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const now = Date.now();
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const windowStart = now - windowMs;
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let bucket = buckets.get(key);
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if (!bucket) {
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if (buckets.size >= MAX_BUCKETS) prune(now, windowMs);
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bucket = { timestamps: [] };
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buckets.set(key, bucket);
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}
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bucket.timestamps = bucket.timestamps.filter((timestamp) => timestamp > windowStart);
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if (bucket.timestamps.length >= limit) {
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const oldest = bucket.timestamps[0];
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const retryAfter = Math.max(1, Math.ceil((oldest + windowMs - now) / 1000));
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return {
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allowed: false,
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limit,
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remaining: 0,
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retryAfter,
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resetAt: oldest + windowMs,
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};
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}
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bucket.timestamps.push(now);
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return {
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allowed: true,
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limit,
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remaining: Math.max(0, limit - bucket.timestamps.length),
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retryAfter: 0,
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resetAt: now + windowMs,
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};
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}
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/** Drop stale buckets so long-running servers don't grow without bound. */
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function prune(now: number, windowMs: number): void {
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const cutoff = now - windowMs;
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for (const [key, bucket] of buckets) {
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if (bucket.timestamps.length === 0 || bucket.timestamps[bucket.timestamps.length - 1] < cutoff) {
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buckets.delete(key);
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}
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if (buckets.size < MAX_BUCKETS / 2) break;
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}
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}
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/** Best-effort client identity: proxy headers first, then a shared bucket. */
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export function clientKey(request: Request): string {
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const forwarded = request.headers.get("x-forwarded-for");
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if (forwarded) {
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const first = forwarded.split(",")[0]?.trim();
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if (first) return first;
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}
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return (
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request.headers.get("x-real-ip") ??
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request.headers.get("cf-connecting-ip") ??
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"local"
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);
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}
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export function rateLimitResponse(result: RateLimitResult, message?: string): Response {
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return Response.json(
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{
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error:
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message ??
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`Too many requests — this app allows ${result.limit} per window. Try again in ${result.retryAfter}s.`,
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kind: "rate_limited",
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retryAfter: result.retryAfter,
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},
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{
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status: 429,
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headers: {
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"Retry-After": String(result.retryAfter),
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"Cache-Control": "no-store",
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},
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},
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);
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}
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