DESKTOP-I1T6TF3\Q
84 lines
3.1 KiB
TypeScript
84 lines
3.1 KiB
TypeScript
import { analyzeImageDataUrl } from "@infiplot/ai-client";
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import type {
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ParseStyleImageRequest,
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ParseStyleImageResponse,
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} from "@infiplot/types";
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import { NextResponse } from "next/server";
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import { loadEngineConfig } from "@/lib/config";
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export const runtime = "nodejs";
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export const maxDuration = 60;
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// Same rationale as /api/vision: the client resizes to 512px max-dim webp
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// (~30-80KB base64 typical) before upload, so 3 MB is generous headroom
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// against malformed / abusive direct-API payloads.
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const MAX_IMAGE_BYTES = 3 * 1024 * 1024;
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const STYLE_EXTRACTION_PROMPT = `You are a senior concept artist helping describe an image's visual style so that a text-to-image diffusion model (FLUX) can reproduce the same aesthetic on different subjects.
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Look at the attached image and produce a single English style-prompt string that captures ONLY its visual style — NOT its subject matter. Focus on:
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- Medium / technique (e.g., watercolor, oil painting, cel-shaded anime, 3D render, pixel art)
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- Line work and rendering (sharp ink outlines, soft shading, painterly brushstrokes, flat colors)
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- Color palette and lighting (pastel, saturated, monochrome, warm golden-hour, cool neon, high contrast)
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- Mood and atmosphere (dreamy, melancholic, cinematic, nostalgic, gritty)
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- Any recognizable artistic influence (Ghibli, Makoto Shinkai, ukiyo-e, vaporwave, cyberpunk anime, etc.)
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Do NOT describe the characters, objects, or scene contents. Output exactly one JSON object:
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{"stylePrompt": "<comma-separated English visual-style attributes, ~30-60 words>"}`;
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export async function POST(req: Request) {
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let body: ParseStyleImageRequest;
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try {
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body = (await req.json()) as ParseStyleImageRequest;
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} catch {
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return NextResponse.json({ error: "Invalid JSON" }, { status: 400 });
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}
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if (
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typeof body.imageDataUrl !== "string" ||
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!body.imageDataUrl.startsWith("data:image/")
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) {
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return NextResponse.json(
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{ error: "imageDataUrl must be a data:image/... base64 URL" },
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{ status: 400 },
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);
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}
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if (body.imageDataUrl.length > MAX_IMAGE_BYTES) {
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return NextResponse.json(
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{ error: `imageDataUrl exceeds ${MAX_IMAGE_BYTES} bytes` },
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{ status: 413 },
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);
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}
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try {
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const config = loadEngineConfig(req.headers);
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const raw = await analyzeImageDataUrl(
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config.vision,
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body.imageDataUrl,
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STYLE_EXTRACTION_PROMPT,
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{ responseFormat: "json_object" },
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);
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let parsed: { stylePrompt?: string };
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try {
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parsed = JSON.parse(raw);
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} catch {
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// Fall back: treat the raw response as the style prompt directly.
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parsed = { stylePrompt: raw };
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}
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const stylePrompt = (parsed.stylePrompt ?? "").trim();
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if (!stylePrompt) {
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return NextResponse.json(
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{ error: "Vision model returned an empty stylePrompt" },
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{ status: 502 },
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);
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}
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const payload: ParseStyleImageResponse = { stylePrompt };
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return NextResponse.json(payload);
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} catch (err) {
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const message = err instanceof Error ? err.message : "Unknown error";
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return NextResponse.json({ error: message }, { status: 500 });
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}
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}
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