type GenerateMode = "text-to-image" | "image-to-image" | "inpaint" | "outpaint";

type ComfyGenerateRequest = {
  mode: GenerateMode;
  model: string;
  prompt: string;
  negativePrompt?: string;
  strength?: number;
  steps?: number;
  cfg?: number;
  seed?: number;
  sampler?: string;
  scheduler?: string;
  width?: number;
  height?: number;
  outpaint?: {
    left?: number;
    top?: number;
    right?: number;
    bottom?: number;
    feathering?: number;
  };
  inputImage?: string;
  maskImage?: string;
};

const comfyBaseUrl = process.env.COMFYUI_URL ?? "http://127.0.0.1:8188";

export async function handleComfyApi(request: Request) {
  const url = new URL(request.url);
  if (url.pathname === "/api/comfy/models" && request.method === "GET") return json(await listGenerationOptions());
  if (url.pathname === "/api/comfy/generate" && request.method === "POST") return json(await generate(await request.json() as ComfyGenerateRequest));
  return new Response("Not found", { status: 404 });
}

async function listGenerationOptions() {
  const response = await fetch(`${comfyBaseUrl}/object_info`);
  if (!response.ok) throw new Error(`ComfyUI option lookup failed: ${response.status}`);
  const info = await response.json() as {
    CheckpointLoaderSimple?: { input?: { required?: { ckpt_name?: [string[]] } } };
    KSampler?: { input?: { required?: { sampler_name?: [string[]]; scheduler?: [string[]] } } };
  };
  return {
    models: info.CheckpointLoaderSimple?.input?.required?.ckpt_name?.[0] ?? [],
    samplers: info.KSampler?.input?.required?.sampler_name?.[0] ?? [],
    schedulers: info.KSampler?.input?.required?.scheduler?.[0] ?? [],
  };
}

async function listCheckpointModels() {
  return (await listGenerationOptions()).models;
}

async function generate(request: ComfyGenerateRequest) {
  if (!request.prompt?.trim()) throw new Error("Prompt is required");
  if (!request.model || request.model === "auto") {
    const models = await listCheckpointModels();
    request.model = models[0] ?? "sd_xl_base_1.0.safetensors";
  }

  const clientId = crypto.randomUUID();
  const uploaded = request.inputImage ? await uploadDataUrl(request.inputImage, `image-studio-${crypto.randomUUID()}.png`) : undefined;
  const mask = request.maskImage ? await uploadDataUrl(request.maskImage, `image-studio-mask-${crypto.randomUUID()}.png`) : undefined;
  const prompt = buildSdxlWorkflow({ ...request, inputImage: uploaded, maskImage: mask });

  const queued = await fetch(`${comfyBaseUrl}/prompt`, {
    method: "POST",
    headers: { "content-type": "application/json" },
    body: JSON.stringify({ client_id: clientId, prompt }),
  });
  if (!queued.ok) throw new Error(`ComfyUI prompt failed: ${queued.status} ${await queued.text()}`);
  const { prompt_id } = await queued.json() as { prompt_id: string };
  const history = await waitForHistory(prompt_id);
  const image = firstOutputImage(history);
  if (!image) throw new Error("ComfyUI did not return an image");

  const imageResponse = await fetch(`${comfyBaseUrl}/view?${new URLSearchParams({ filename: image.filename, subfolder: image.subfolder ?? "", type: image.type ?? "output" })}`);
  if (!imageResponse.ok) throw new Error(`ComfyUI image fetch failed: ${imageResponse.status}`);
  const bytes = Buffer.from(await imageResponse.arrayBuffer());
  return { source: `data:image/png;base64,${bytes.toString("base64")}`, mimeType: "image/png" };
}

async function uploadDataUrl(dataUrl: string, filename: string) {
  const match = /^data:([^;]+);base64,(.+)$/.exec(dataUrl);
  if (!match) return undefined;
  const mimeType = match[1] ?? "image/png";
  const base64 = match[2] ?? "";
  const form = new FormData();
  form.append("image", new File([new Uint8Array(Buffer.from(base64, "base64"))], filename, { type: mimeType }));
  form.append("overwrite", "true");
  const response = await fetch(`${comfyBaseUrl}/upload/image`, { method: "POST", body: form });
  if (!response.ok) throw new Error(`ComfyUI upload failed: ${response.status}`);
  const uploaded = await response.json() as { name: string };
  return uploaded.name;
}

async function waitForHistory(promptId: string) {
  for (let attempt = 0; attempt < 240; attempt++) {
    const response = await fetch(`${comfyBaseUrl}/history/${promptId}`);
    if (response.ok) {
      const history = await response.json() as Record<string, unknown>;
      if (history[promptId]) return history[promptId];
    }
    await Bun.sleep(500);
  }
  throw new Error("Timed out waiting for ComfyUI");
}

function firstOutputImage(history: unknown): { filename: string; subfolder?: string; type?: string } | undefined {
  const outputs = (history as { outputs?: Record<string, { images?: { filename: string; subfolder?: string; type?: string }[] }> }).outputs ?? {};
  for (const output of Object.values(outputs)) {
    const image = output.images?.[0];
    if (image) return image;
  }
  return undefined;
}

function buildSdxlWorkflow(request: ComfyGenerateRequest) {
  if (!request.width || !request.height) throw new Error("Generation width and height are required");
  const width = Math.max(64, Math.round(request.width));
  const height = Math.max(64, Math.round(request.height));
  const denoise = Math.max(0, Math.min(1, (request.strength ?? 75) / 100));
  const seed = request.seed === undefined || request.seed < 0 ? Math.floor(Math.random() * 2 ** 32) : Math.round(request.seed);
  const steps = Math.max(1, Math.round(request.steps ?? 30));
  const cfg = Math.max(0, request.cfg ?? 7);
  const sampler = request.sampler ?? "euler";
  const scheduler = request.scheduler ?? "normal";
  const positive = request.prompt;
  const negative = request.negativePrompt ?? "";
  const workflow: Record<string, unknown> = {
    "1": { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: request.model } },
    "2": { class_type: "CLIPTextEncode", inputs: { text: positive, clip: ["1", 1] } },
    "3": { class_type: "CLIPTextEncode", inputs: { text: negative, clip: ["1", 1] } },
    "6": { class_type: "KSampler", inputs: { seed, steps, cfg, sampler_name: sampler, scheduler, denoise, model: ["1", 0], positive: ["2", 0], negative: ["3", 0], latent_image: ["5", 0] } },
    "7": { class_type: "VAEDecode", inputs: { samples: ["6", 0], vae: ["1", 2] } },
    "8": { class_type: "SaveImage", inputs: { filename_prefix: `image-studio-${request.mode}`, images: ["7", 0] } },
  };

  if (request.mode === "text-to-image" || !request.inputImage) {
    workflow["5"] = { class_type: "EmptyLatentImage", inputs: { width, height, batch_size: 1 } };
    return workflow;
  }

  workflow["4"] = { class_type: "LoadImage", inputs: { image: request.inputImage } };

  if (request.mode === "inpaint" && request.maskImage) {
    workflow["9"] = { class_type: "LoadImage", inputs: { image: request.maskImage } };
    workflow["5"] = { class_type: "VAEEncodeForInpaint", inputs: { pixels: ["4", 0], vae: ["1", 2], mask: ["9", 1], grow_mask_by: 6 } };
    return workflow;
  }

  if (request.mode === "outpaint") {
    workflow["10"] = { class_type: "ImagePadForOutpaint", inputs: { image: ["4", 0], left: Math.round(request.outpaint?.left ?? 0), top: Math.round(request.outpaint?.top ?? 0), right: Math.round(request.outpaint?.right ?? 0), bottom: Math.round(request.outpaint?.bottom ?? 0), feathering: Math.round(request.outpaint?.feathering ?? 0) } };
    workflow["5"] = { class_type: "VAEEncodeForInpaint", inputs: { pixels: ["10", 0], vae: ["1", 2], mask: ["10", 1], grow_mask_by: 6 } };
    return workflow;
  }

  workflow["5"] = { class_type: "VAEEncode", inputs: { pixels: ["4", 0], vae: ["1", 2] } };
  return workflow;
}

function json(value: unknown) {
  return new Response(JSON.stringify(value), { headers: { "content-type": "application/json" } });
}