Inferencia IA Serverless

import * as fal from "@fal-ai/client";
import { NextRequest, NextResponse } from "next/server";

fal.config({ credentials: process.env.FAL_KEY });

export async function POST(req: NextRequest) {
  const { prompt, quality, size } = await req.json();

  // Selección dinámica de modelo según calidad solicitada
  const modelId = quality === "draft"
    ? "fal-ai/flux/schnell"   // Borrador: 4 pasos, ~2s
    : "fal-ai/flux-pro";       // Final: 28 pasos, alta calidad

  const result = await fal.subscribe(modelId, {
    input: {
      prompt: prompt,
      image_size: size ?? "landscape_4_3",
      num_images: 1,
      num_inference_steps: quality === "draft" ? 4 : 28,
      guidance_scale: 3.5,
      safety_tolerance: "2",
    },
    onQueueUpdate: (update) => {
      if (update.status === "IN_PROGRESS") {
        console.log("[fal] progreso:", update.logs?.at(-1)?.message);
      }
    },
  });

  return NextResponse.json({
    url: result.data.images[0].url,
    seed: result.data.seed,
    model: modelId,
  });
}

// 1. SUBMIT — app/api/jobs/submit/route.ts
import { supabase } from "@/lib/supabase";

export async function POST(req: NextRequest) {
  const { prompt, userId } = await req.json();

  // Enviar job y retornar inmediatamente
  const { request_id } = await fal.queue.submit("fal-ai/flux-pro", {
    input: { prompt, num_images: 1 },
    webhookUrl: `${process.env.NEXT_PUBLIC_URL}/api/webhooks/fal`,
  });

  // Guardar en Supabase con estado pendiente
  await supabase.from("generations").insert({
    request_id, userId, prompt, status: "pending"
  });

  return NextResponse.json({ request_id, status: "queued" });
}

// 2. WEBHOOK — app/api/webhooks/fal/route.ts
export async function POST(req: Request) {
  const payload = await req.json();

  if (payload.status === "OK") {
    const imageUrl = payload.payload.images[0].url;
    await supabase.from("generations").update({
      image_url: imageUrl,
      status: "completed"
    }).eq("request_id", payload.request_id);

    // Notificar al usuario vía Supabase Realtime
  }

  return Response.json({ ok: true });
}

// Generar todas las variantes de una campaña en paralelo
async function generateCampaignAssets(prompts: string[]): Promise<string[]> {
  // Enviar todos los jobs simultáneamente (sin esperar a que terminen)
  const jobs = await Promise.all(
    prompts.map(prompt =>
      fal.queue.submit("fal-ai/flux/schnell", {
        input: { prompt, num_images: 1, num_inference_steps: 4 }
      })
    )
  );

  // Recoger todos los resultados (se procesan en paralelo en fal.ai)
  const results = await Promise.all(
    jobs.map(({ request_id }) =>
      fal.queue.result("fal-ai/flux/schnell", { requestId: request_id })
    )
  );

  return results.map(r => r.data.images[0].url);
}

// Uso: generar 6 variantes de campaña de verano
const urls = await generateCampaignAssets([
  "Summer sale banner, beach lifestyle, vibrant colors, 16:9",
  "Summer sale banner, outdoor sports, energetic, 16:9",
  "Summer sale product shot, minimal white background, 1:1",
  "Summer sale Instagram story, vertical, gradient sunset",
  "Summer sale email header, tropical vibes, 600px wide",
  "Summer sale Facebook ad, family fun, warm tones",
]); // ~2s total para 6 imágenes en paralelo