/**
 * 场景裂变 API
 * 
 * 将一张产品图批量融合到多个场景图中
 * 支持多种生图模型：阿里生图、RUXA、Gemini、既梦
 */

import { NextResponse } from "next/server";
import { z } from "zod";
import { auth } from "@/app/(auth)/auth";
import { db } from "@/lib/db";
import { generatedImage } from "@/lib/db/schema";
import {
  createMediaAssetRelation,
  getEnabledAiModelsWithProvider,
  getMediaAssetsByUserAndUrls,
  getSystemSetting,
  upsertMediaAsset,
} from "@/lib/db/queries";
import { uploadFile } from "@/lib/storage/service";
import sharp from "sharp";
import { createGeminiImage } from "@/lib/ai/providers/gemini-image";
import { createVolcanoVision } from "@/lib/ai/providers/volcano-vision";
import { createRuxaImage } from "@/lib/ai/providers/ruxa-image";
import { createFalImage } from "@/lib/ai/providers/fal-image";
import { createOpenAIImage } from "@/lib/ai/providers/openai-image";
import { callQwen3Vl } from "@/lib/ai/qwen3-vl";
import {
  canProcessSceneFusionProviderType,
  normalizeSceneFusionOutputParamsForCandidate,
  normalizeSceneFusionProviderType,
  type SceneFusionImageGenConfig,
} from "@/lib/scene-fusion/output-capabilities";

// 请求体 Schema
const sceneFusionRequestSchema = z.object({
  productImage: z.string().min(1, "产品图不能为空"),
  productName: z.string().optional(), // 产品名称（如"沙发"）
  sceneMode: z.enum(["scene-fission", "multi-product-scene", "random-bg"]).optional(),
  aspectRatio: z.string().optional(), // 图片比例（如"1:1", "4:3", "auto"）
  imageSize: z.string().optional(), // 图片分辨率（如"1K", "2K", "4K"）
  scenes: z.array(z.object({
    id: z.string(),
    image: z.string().min(1, "场景图不能为空"),
    targetName: z.string().optional(), // 背景中需要被替换的物体名称（如"椅子"）
    customPrompt: z.string().optional(),
  })).min(1, "至少需要一个场景图").max(10, "最多支持10个场景图"),
});

type SceneFusionMode = "scene-fission" | "multi-product-scene" | "random-bg";

// 默认提示词（当数据库未配置时使用）- 使用"参考"的表述方式
const FALLBACK_PROMPT = "请参考第一张图片中的产品，将其自然地融合到第二张图片的场景中。要求：1)保持产品的整体外观、颜色、形状和设计风格与第一张图一致；2)调整产品的大小和角度以适应场景；3)注意光影和透视关系使融合自然。";

const SCENE_FUSION_MODEL_COOLDOWN_MS = 2 * 60 * 1000;
const SCENE_FUSION_MAX_ITEMS = 10;
const SCENE_FUSION_REQUEST_STAGGER_MS = 350;
const SCENE_FUSION_AGENT_TIMEOUT_MS = 20000;
const SCENE_FUSION_AGENT_MAX_WORKERS = 1;
const SCENE_FUSION_PROCESS_MAX_WORKERS = 1;
const SCENE_FUSION_PRE_RMBG_TIMEOUT_MS = 60_000;
const SCENE_FUSION_ECHO_HASH_TIMEOUT_MS = 20_000;
const SCENE_FUSION_ECHO_HASH_THRESHOLD = 8;
const SCENE_FUSION_STRICT_PROMPT_TAIL =
  "硬性要求：严禁直接返回第一张参考图（产品图）原图，必须保留第二张图的场景与构图，仅替换目标主体；若识别不确定，请优先保持第二张图背景不变并完成主体替换。";
const SCENE_FUSION_RANDOM_BG_PROMPT_TAIL =
  "硬性要求：背景必须与原图明显不同（颜色、结构、元素至少两项显著变化），禁止保留原背景纹理或直接回传原图；仅保留产品主体并保证主体边缘完整自然。";
const sceneFusionModelCooldown = new Map<string, number>();
let sceneFusionRoundRobinCursor = 0;

interface SceneFusionModelCandidate {
  model: {
    id: string;
    modelId: string;
    displayName: string;
    supportsVision: boolean;
    imageGenConfig?: SceneFusionImageGenConfig | null;
  };
  provider: {
    type: string;
    baseUrl: string;
    apiKey: string;
  };
}

interface SceneFusionAgentMeta {
  productName?: string;
  productSource?: "user" | "agent";
  productConfidence?: number;
  productModel?: string;
  sceneTargets: Array<{
    id: string;
    targetName?: string;
    source?: "user" | "agent";
    confidence?: number;
    model?: string;
  }>;
}

function pruneSceneFusionCooldown(now: number = Date.now()) {
  for (const [key, expireAt] of sceneFusionModelCooldown.entries()) {
    if (expireAt <= now) {
      sceneFusionModelCooldown.delete(key);
    }
  }
}

function isSceneFusionCandidateCoolingDown(
  candidate: SceneFusionModelCandidate,
  now: number = Date.now()
): boolean {
  const expireAt = sceneFusionModelCooldown.get(candidate.model.id);
  if (!expireAt) return false;
  if (expireAt <= now) {
    sceneFusionModelCooldown.delete(candidate.model.id);
    return false;
  }
  return true;
}

function markSceneFusionCandidateFailed(candidate: SceneFusionModelCandidate, now: number = Date.now()) {
  sceneFusionModelCooldown.set(candidate.model.id, now + SCENE_FUSION_MODEL_COOLDOWN_MS);
}

function markSceneFusionCandidateHealthy(candidate: SceneFusionModelCandidate) {
  sceneFusionModelCooldown.delete(candidate.model.id);
}

function rotateCandidates<T>(items: T[], startOffset: number): T[] {
  if (items.length <= 1) {
    return [...items];
  }
  const normalizedOffset = ((startOffset % items.length) + items.length) % items.length;
  return [
    ...items.slice(normalizedOffset),
    ...items.slice(0, normalizedOffset),
  ];
}

function shuffleCandidates<T>(items: T[]): T[] {
  const shuffled = [...items];
  for (let i = shuffled.length - 1; i > 0; i -= 1) {
    const j = Math.floor(Math.random() * (i + 1));
    [shuffled[i], shuffled[j]] = [shuffled[j], shuffled[i]];
  }
  return shuffled;
}

function getNextSceneFusionRoundRobinOffset(poolSize: number): number {
  if (poolSize <= 0) return 0;
  const offset = sceneFusionRoundRobinCursor % poolSize;
  sceneFusionRoundRobinCursor = (sceneFusionRoundRobinCursor + 1) % Number.MAX_SAFE_INTEGER;
  return offset;
}

function buildPreRmbgCandidateUrls(request: Request): string[] {
  const urls: string[] = [];
  const requestUrl = new URL(request.url);
  const requestHost = requestUrl.hostname.toLowerCase();
  const requestProtocol = requestUrl.protocol.replace(":", "");
  if (!(requestProtocol === "https" && (requestHost === "localhost" || requestHost === "127.0.0.1"))) {
    urls.push(new URL("/api/rmbg", request.url).toString());
  }

  const host = request.headers.get("host")?.trim();
  const protoHint = request.headers
    .get("x-forwarded-proto")
    ?.split(",")[0]
    ?.trim()
    .toLowerCase();
  const protocol = protoHint === "https" || protoHint === "http" ? protoHint : requestUrl.protocol.replace(":", "");

  if (host) {
    const normalizedHost = host.split(":")[0]?.toLowerCase() || "";
    const isLocalHost = normalizedHost === "localhost" || normalizedHost === "127.0.0.1";
    if (!(protocol === "https" && isLocalHost)) {
      urls.push(`${protocol}://${host}/api/rmbg`);
    }
    if (isLocalHost) {
      urls.push(`http://${host}/api/rmbg`);
    }
  }

  const localPort = requestUrl.port || process.env.PORT || "3125";
  urls.push(`http://127.0.0.1:${localPort}/api/rmbg`);
  urls.push(`http://localhost:${localPort}/api/rmbg`);

  return Array.from(new Set(urls));
}

function normalizeUrlForCompare(input: string): string {
  try {
    const parsed = new URL(input);
    return `${parsed.origin}${parsed.pathname}`;
  } catch {
    return input;
  }
}

async function fetchImageBufferWithTimeout(url: string, timeoutMs: number): Promise<Buffer> {
  const controller = new AbortController();
  const timeoutId = setTimeout(() => controller.abort(), timeoutMs);
  try {
    const response = await fetch(url, { signal: controller.signal });
    if (!response.ok) {
      throw new Error(`fetch_failed_${response.status}`);
    }
    return Buffer.from(await response.arrayBuffer());
  } finally {
    clearTimeout(timeoutId);
  }
}

async function getAverageHashBits(
  imageUrl: string,
  hashCache: Map<string, string>
): Promise<string | null> {
  if (hashCache.has(imageUrl)) {
    return hashCache.get(imageUrl)!;
  }

  try {
    const buffer = await fetchImageBufferWithTimeout(imageUrl, SCENE_FUSION_ECHO_HASH_TIMEOUT_MS);
    const pixels = await sharp(buffer)
      .resize(8, 8, { fit: "fill" })
      .grayscale()
      .raw()
      .toBuffer();
    if (!pixels.length) return null;
    const total = pixels.reduce((sum, value) => sum + value, 0);
    const avg = total / pixels.length;
    const hashBits = Array.from(pixels)
      .map((value) => (value >= avg ? "1" : "0"))
      .join("");
    hashCache.set(imageUrl, hashBits);
    return hashBits;
  } catch (error) {
    console.warn("[SceneFusion] Echo hash compute skipped:", {
      imageUrl: imageUrl.slice(0, 120),
      error: error instanceof Error ? error.message : "unknown",
    });
    return null;
  }
}

function hammingDistanceBits(a: string, b: string): number {
  if (a.length !== b.length) {
    return Math.max(a.length, b.length);
  }
  let distance = 0;
  for (let index = 0; index < a.length; index += 1) {
    if (a[index] !== b[index]) {
      distance += 1;
    }
  }
  return distance;
}

async function detectEchoedProductResult(
  params: {
    requestId: string;
    sceneIndex: number;
    productImageUrl: string;
    generatedImageUrl: string;
    modelName: string;
    hashCache: Map<string, string>;
  }
): Promise<{
  tooSimilar: boolean;
  reason?: string;
  hammingDistance?: number;
}> {
  const normalizedProductUrl = normalizeUrlForCompare(params.productImageUrl);
  const normalizedGeneratedUrl = normalizeUrlForCompare(params.generatedImageUrl);

  if (normalizedProductUrl === normalizedGeneratedUrl) {
    return { tooSimilar: true, reason: "same_url" };
  }

  const [productHash, generatedHash] = await Promise.all([
    getAverageHashBits(params.productImageUrl, params.hashCache),
    getAverageHashBits(params.generatedImageUrl, params.hashCache),
  ]);

  if (!productHash || !generatedHash) {
    return { tooSimilar: false };
  }

  const distance = hammingDistanceBits(productHash, generatedHash);
  if (distance <= SCENE_FUSION_ECHO_HASH_THRESHOLD) {
    console.warn(
      `[SceneFusion][${params.requestId}] Echo-guard hit: scene#${params.sceneIndex + 1}, model=${params.modelName}, distance=${distance}`
    );
    return {
      tooSimilar: true,
      reason: "hash_too_close",
      hammingDistance: distance,
    };
  }

  return { tooSimilar: false, hammingDistance: distance };
}

function waitSceneFusion(ms: number): Promise<void> {
  return new Promise((resolve) => setTimeout(resolve, ms));
}

function normalizeSceneFusionName(value: unknown): string {
  const raw = String(value ?? "").trim();
  if (!raw) return "";
  const normalized = raw
    .replace(/[：:，,。.!！?？、；;（）()[\]{}"'`]/g, " ")
    .replace(/\s+/g, " ")
    .trim();
  return normalized.slice(0, 16);
}

function extractJsonObject(text: string): Record<string, unknown> | null {
  const content = String(text || "").trim();
  if (!content) return null;
  const match = content.match(/\{[\s\S]*\}/);
  if (!match) return null;
  try {
    const parsed = JSON.parse(match[0]);
    if (parsed && typeof parsed === "object" && !Array.isArray(parsed)) {
      return parsed as Record<string, unknown>;
    }
    return null;
  } catch {
    return null;
  }
}

async function detectProductNameWithAgent(imageUrl: string): Promise<{
  name: string;
  confidence: number;
  model: string;
} | null> {
  const prompt = [
    "你是电商图片分析助手。",
    "请识别图片中最核心、最主要的产品主体名称。",
    "要求：",
    "- 名称精简，2-8个字，尽量包含材质/品类（如：皮质沙发、木质餐椅、陶瓷花瓶）",
    "- 不要返回颜色描述句子，不要返回背景和场景词",
    "仅返回JSON：",
    '{\"productName\":\"名称\",\"confidence\":0.0}',
  ].join("\n");

  const { text, model } = await callQwen3Vl({
    imageUrl,
    prompt,
    temperature: 0.1,
    maxTokens: 220,
    timeoutMs: SCENE_FUSION_AGENT_TIMEOUT_MS,
    retries: 1,
  });

  const parsed = extractJsonObject(text);
  const name = normalizeSceneFusionName(parsed?.productName);
  const confidenceRaw = Number(parsed?.confidence);
  const confidence = Number.isFinite(confidenceRaw) ? Math.max(0, Math.min(1, confidenceRaw)) : 0.7;

  if (!name || confidence < 0.45) {
    return null;
  }

  return { name, confidence, model };
}

async function detectTargetNameWithAgent(
  sceneImageUrl: string,
  productNameHint: string
): Promise<{
  name: string;
  confidence: number;
  model: string;
} | null> {
  const prompt = [
    "你是场景替换识别助手。",
    `已知要替换进场景的产品是：${productNameHint || "该产品"}`,
    "请识别当前场景图里最应该被替换的目标物体名称（与该产品品类最接近的场景主体）。",
    "要求：",
    "- 返回“完整物体名”，不要返回局部部件名",
    "- 名称精简，2-8个字（如：餐椅、沙发、茶几、花瓶）",
    "- 如果无法确定，返回最主要主体物体名",
    "仅返回JSON：",
    '{\"targetName\":\"名称\",\"confidence\":0.0}',
  ].join("\n");

  const { text, model } = await callQwen3Vl({
    imageUrl: sceneImageUrl,
    prompt,
    temperature: 0.1,
    maxTokens: 220,
    timeoutMs: SCENE_FUSION_AGENT_TIMEOUT_MS,
    retries: 1,
  });

  const parsed = extractJsonObject(text);
  const name = normalizeSceneFusionName(parsed?.targetName);
  const confidenceRaw = Number(parsed?.confidence);
  const confidence = Number.isFinite(confidenceRaw) ? Math.max(0, Math.min(1, confidenceRaw)) : 0.7;

  if (!name || confidence < 0.4) {
    return null;
  }

  return { name, confidence, model };
}

function extractStyleHintFromPrompt(customPrompt: string | undefined): string | undefined {
  const content = String(customPrompt || "").trim();
  if (!content) return undefined;
  const quoted = content.match(/[“"]([^”"]+?)(?:风格|场景)/);
  if (quoted?.[1]) {
    return normalizeSceneFusionName(quoted[1]) || quoted[1].trim();
  }
  const styleOnly = content.match(/(?:替换背景为|背景为|风格为)\s*([^，。；]+)/);
  if (styleOnly?.[1]) {
    return normalizeSceneFusionName(styleOnly[1]) || styleOnly[1].trim();
  }
  return undefined;
}

function mergeCustomPrompt(basePrompt: string | undefined, addition: string | undefined): string | undefined {
  const base = String(basePrompt || "").trim();
  const extra = String(addition || "").trim();
  if (!base && !extra) return undefined;
  if (!base) return extra.slice(0, 280);
  if (!extra) return base.slice(0, 280);
  if (base.includes(extra)) return base.slice(0, 280);
  if (extra.includes(base)) return extra.slice(0, 280);
  return `${base} ${extra}`.slice(0, 280);
}

async function generateRandomBackgroundPromptWithAgent(params: {
  productImageUrl: string;
  productNameHint?: string;
  styleHint?: string;
  userPrompt?: string;
}): Promise<string | null> {
  const prompt = [
    "你是电商场景策划助手。",
    `产品名称：${params.productNameHint || "该产品"}`,
    params.styleHint ? `期望风格：${params.styleHint}` : "",
    params.userPrompt ? `用户补充要求：${params.userPrompt}` : "",
    "请基于产品图生成一条“仅描述背景场景”的中文提示词。",
    "要求：",
    "- 场景必须与产品品类匹配，适合商品展示",
    "- 只描述背景、灯光、材质氛围，不改动产品主体外观",
    "- 不要输出流程指令（例如“替换背景/保持主体”）",
    "- 20~60字，语义具体可执行",
    "仅返回 JSON：",
    "{\"backgroundPrompt\":\"...\"}",
  ]
    .filter(Boolean)
    .join("\n");

  const { text } = await callQwen3Vl({
    imageUrl: params.productImageUrl,
    prompt,
    temperature: 0.2,
    maxTokens: 240,
    timeoutMs: SCENE_FUSION_AGENT_TIMEOUT_MS,
    retries: 1,
  });

  const parsed = extractJsonObject(text);
  const backgroundPromptRaw = String(parsed?.backgroundPrompt || "").replace(/\s+/g, " ").trim();
  if (!backgroundPromptRaw || backgroundPromptRaw.length < 6) {
    return null;
  }
  return backgroundPromptRaw.slice(0, 120);
}

async function enrichRandomBackgroundScenesWithAgent(params: {
  requestId: string;
  productImageUrl: string;
  productName?: string;
  scenes: Array<{
    id: string;
    image: string;
    targetName?: string;
    customPrompt?: string;
    imageUrl: string;
  }>;
}): Promise<Array<{
  id: string;
  image: string;
  targetName?: string;
  customPrompt?: string;
  imageUrl: string;
}>> {
  const nextScenes = params.scenes.map((scene) => ({ ...scene }));
  const workerTargets = nextScenes;
  if (workerTargets.length === 0) return nextScenes;

  let cursor = 0;
  const workerCount = Math.max(1, Math.min(SCENE_FUSION_AGENT_MAX_WORKERS, workerTargets.length));
  const workers = Array.from({ length: workerCount }, async () => {
    while (true) {
      const current = cursor;
      cursor += 1;
      if (current >= workerTargets.length) break;

      const scene = workerTargets[current];
      if (!scene) continue;

      try {
        if (!normalizeSceneFusionName(scene.targetName) && params.productName) {
          scene.targetName = params.productName;
        }

        const styleHint = extractStyleHintFromPrompt(scene.customPrompt);
        const generatedPrompt = await generateRandomBackgroundPromptWithAgent({
          productImageUrl: params.productImageUrl,
          productNameHint: params.productName,
          styleHint,
          userPrompt: scene.customPrompt,
        });

        if (generatedPrompt) {
          scene.customPrompt = mergeCustomPrompt(scene.customPrompt, `场景建议：${generatedPrompt}`);
        }
      } catch (error) {
        console.warn(
          `[SceneFusion][${params.requestId}] Random scene prompt enhance skipped: scene=${scene.id}`,
          error
        );
      }
    }
  });

  await Promise.all(workers);
  return nextScenes;
}

async function enrichSceneLabelsWithAgent(params: {
  requestId: string;
  productImageUrl: string;
  productName?: string;
  scenes: Array<{
    id: string;
    image: string;
    targetName?: string;
    customPrompt?: string;
    imageUrl: string;
  }>;
}): Promise<{
  productName?: string;
  scenes: Array<{
    id: string;
    image: string;
    targetName?: string;
    customPrompt?: string;
    imageUrl: string;
  }>;
  meta: SceneFusionAgentMeta;
}> {
  const clonedScenes = params.scenes.map((scene) => ({ ...scene }));
  const userProductName = normalizeSceneFusionName(params.productName);
  let resolvedProductName = userProductName;

  const meta: SceneFusionAgentMeta = {
    productName: userProductName || undefined,
    productSource: userProductName ? "user" : undefined,
    sceneTargets: clonedScenes.map((scene) => ({
      id: scene.id,
      targetName: normalizeSceneFusionName(scene.targetName) || undefined,
      source: normalizeSceneFusionName(scene.targetName) ? "user" : undefined,
    })),
  };

  try {
    const productDetected = await detectProductNameWithAgent(params.productImageUrl);
    if (productDetected?.name) {
      if (!resolvedProductName) {
        resolvedProductName = productDetected.name;
        meta.productSource = "agent";
      } else {
        meta.productSource = "user";
      }
      meta.productName = resolvedProductName || productDetected.name;
      meta.productConfidence = productDetected.confidence;
      meta.productModel = productDetected.model;
    }
  } catch (error) {
    console.warn(`[SceneFusion][${params.requestId}] Product detect skipped:`, error);
  }

  const unresolvedIndexes = clonedScenes
    .map((scene, index) => ({
      index,
      hasTarget: Boolean(normalizeSceneFusionName(scene.targetName)),
      sameAsProduct: scene.imageUrl === params.productImageUrl,
    }))
    .filter((item) => !item.hasTarget && !item.sameAsProduct)
    .map((item) => item.index);

  if (unresolvedIndexes.length === 0) {
    return {
      productName: resolvedProductName || undefined,
      scenes: clonedScenes,
      meta,
    };
  }

  let cursor = 0;
  const workerCount = Math.max(1, Math.min(SCENE_FUSION_AGENT_MAX_WORKERS, unresolvedIndexes.length));

  const workers = Array.from({ length: workerCount }, async () => {
    while (true) {
      const current = cursor;
      cursor += 1;
      if (current >= unresolvedIndexes.length) break;

      const sceneIndex = unresolvedIndexes[current]!;
      const scene = clonedScenes[sceneIndex]!;
      try {
        const targetDetected = await detectTargetNameWithAgent(
          scene.imageUrl,
          resolvedProductName || "产品"
        );
        if (!targetDetected?.name) continue;
        scene.targetName = targetDetected.name;
        const metaScene = meta.sceneTargets.find((item) => item.id === scene.id);
        if (metaScene) {
          metaScene.targetName = targetDetected.name;
          metaScene.source = "agent";
          metaScene.confidence = targetDetected.confidence;
          metaScene.model = targetDetected.model;
        }
      } catch (error) {
        console.warn(
          `[SceneFusion][${params.requestId}] Scene target detect skipped: scene=${scene.id}`,
          error
        );
      }
    }
  });

  await Promise.all(workers);

  return {
    productName: resolvedProductName || undefined,
    scenes: clonedScenes,
    meta,
  };
}

function buildSceneFusionCandidatePool(
  allModels: Awaited<ReturnType<typeof getEnabledAiModelsWithProvider>>,
  preferredModelId: string
): SceneFusionModelCandidate[] {
  const backendConfiguredImageModels = allModels
    .filter((entry) => {
      if (!entry?.model?.isEnabled) return false;
      if (!entry?.model?.supportsImageGen) return false;
      if (entry?.model?.isSubModel) return false;
      return true;
    })
    .map((entry) => ({
      model: {
        id: String(entry.model.id),
        modelId: String(entry.model.modelId || ""),
        displayName: String(entry.model.displayName || entry.model.modelId || entry.model.id),
        supportsVision: Boolean(entry.model.supportsVision),
        imageGenConfig: (entry.model.imageGenConfig || null) as SceneFusionImageGenConfig | null,
      },
      provider: {
        type: normalizeSceneFusionProviderType(String(entry.provider.type || "")),
        baseUrl: String(entry.provider.baseUrl || ""),
        apiKey: String(entry.provider.apiKey || ""),
      },
    }))
    .filter(
      (candidate) =>
        Boolean(candidate.model.modelId) &&
        Boolean(candidate.provider.type) &&
        Boolean(candidate.provider.apiKey)
    );

  if (backendConfiguredImageModels.length <= 1) {
    return backendConfiguredImageModels;
  }

  const preferred = preferredModelId
    ? backendConfiguredImageModels.find((candidate) => candidate.model.id === preferredModelId)
    : undefined;

  if (!preferred) {
    return shuffleCandidates(backendConfiguredImageModels);
  }

  const rest = backendConfiguredImageModels.filter(
    (candidate) => candidate.model.id !== preferred.model.id
  );
  return [preferred, ...shuffleCandidates(rest)];
}

/**
 * 获取场景裂变配置
 */
async function getSceneFusionConfig() {
  try {
    const setting = await getSystemSetting({ key: "scene_fusion_config" });
    if (setting?.value) {
      const config = setting.value as {
        enabled?: boolean;
        defaultPrompt?: string;
        maxScenes?: number;
        modelId?: string;
        preRmbgEnabled?: boolean;
      };
      return {
        enabled: config.enabled ?? true,
        defaultPrompt: config.defaultPrompt || FALLBACK_PROMPT,
        maxScenes: Math.min(config.maxScenes ?? SCENE_FUSION_MAX_ITEMS, SCENE_FUSION_MAX_ITEMS),
        modelId: config.modelId || "",
        preRmbgEnabled: config.preRmbgEnabled ?? true,
      };
    }
  } catch (error) {
    console.error("[SceneFusion] Failed to load config:", error);
  }
  
  return {
    enabled: true,
    defaultPrompt: FALLBACK_PROMPT,
    maxScenes: SCENE_FUSION_MAX_ITEMS,
    modelId: "",
    preRmbgEnabled: true,
  };
}

async function preprocessProductImageWithRmbg(
  request: Request,
  requestId: string,
  productImageUrl: string,
  preRmbgEnabled: boolean
): Promise<{
  productImageUrl: string;
  usedPreRmbg: boolean;
  provider?: string;
  reason?: string;
}> {
  if (!preRmbgEnabled) {
    return {
      productImageUrl,
      usedPreRmbg: false,
      reason: "disabled_by_config",
    };
  }

  const cookie = request.headers.get("cookie");
  if (!cookie) {
    return {
      productImageUrl,
      usedPreRmbg: false,
      reason: "missing_cookie",
    };
  }

  const candidateUrls = buildPreRmbgCandidateUrls(request);
  let lastReason = "no_candidate_url";

  console.log(
    `[SceneFusion][${requestId}] Pre-RMBG start: ${productImageUrl.substring(0, 120)}..., candidates=${candidateUrls.join(" | ")}`
  );

  for (const rmbgUrl of candidateUrls) {
    const controller = new AbortController();
    const timeoutId = setTimeout(() => controller.abort(), SCENE_FUSION_PRE_RMBG_TIMEOUT_MS);
    try {
      const response = await fetch(rmbgUrl, {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
          cookie,
        },
        body: JSON.stringify({
          imageUrl: productImageUrl,
          prompt: "场景裂变预处理-抠图",
          skipGallerySave: true,
        }),
        signal: controller.signal,
      });

      const payload = await response.json().catch(() => null) as Record<string, unknown> | null;

      if (!response.ok) {
        lastReason = `request_failed_${response.status}`;
        console.warn(`[SceneFusion][${requestId}] Pre-RMBG candidate failed: ${rmbgUrl}`, {
          status: response.status,
          detail: payload?.detail || payload?.error || "unknown",
        });
        continue;
      }

      const cleanedUrl = String(payload?.original_url || payload?.thumbnail_url || "").trim();
      if (!cleanedUrl || cleanedUrl === productImageUrl) {
        lastReason = cleanedUrl ? "same_as_source" : "empty_result";
        console.warn(`[SceneFusion][${requestId}] Pre-RMBG candidate invalid result: ${rmbgUrl}`, {
          reason: lastReason,
        });
        continue;
      }

      return {
        productImageUrl: cleanedUrl,
        usedPreRmbg: true,
        provider: String(payload?.provider || "unknown"),
      };
    } catch (error) {
      lastReason = error instanceof Error ? error.message : "exception";
      console.warn(`[SceneFusion][${requestId}] Pre-RMBG candidate exception: ${rmbgUrl}`, error);
    } finally {
      clearTimeout(timeoutId);
    }
  }

  return {
    productImageUrl,
    usedPreRmbg: false,
    reason: lastReason,
  };
}

/**
 * 创建缩略图
 */
async function createThumbnail(buffer: Buffer): Promise<Buffer> {
  try {
    const thumbnailBuffer = await sharp(buffer)
      .resize(400, 400, { fit: "inside", withoutEnlargement: true })
      .jpeg({ quality: 80 })
      .toBuffer();
    return thumbnailBuffer;
  } catch (error) {
    console.error("[SceneFusion] Failed to create thumbnail:", error);
    return buffer;
  }
}

/**
 * 生成唯一文件名
 */
function generateFilename(prefix: string, extension: string): string {
  const timestamp = Date.now();
  const random = Math.random().toString(36).substring(2, 8);
  return `${prefix}-${timestamp}-${random}.${extension}`;
}

/**
 * 将 Base64 图片上传到 S3 获取 URL
 */
async function uploadBase64ToS3(base64Data: string, prefix: string = "scene-fusion"): Promise<string> {
  try {
    // 移除 data URL 前缀
    const base64Clean = base64Data.replace(/^data:image\/\w+;base64,/, "");
    const buffer = Buffer.from(base64Clean, "base64");
    
    const filename = generateFilename(`${prefix}-ref`, "png");
    const result = await uploadFile(filename, buffer.buffer.slice(buffer.byteOffset, buffer.byteOffset + buffer.byteLength));
    console.log(`[SceneFusion] Uploaded reference image to: ${result.url}`);
    return result.url;
  } catch (error) {
    console.error("[SceneFusion] Failed to upload reference image:", error);
    throw error;
  }
}

/**
 * 下载图片并上传到 S3
 */
async function downloadAndUploadToS3(
  imageUrl: string,
  prefix: string = "scene-fusion"
): Promise<{ thumbnail: string; original: string }> {
  try {
    console.log("[SceneFusion] Downloading image from:", imageUrl.substring(0, 100) + "...");
    
    const response = await fetch(imageUrl);
    if (!response.ok) {
      throw new Error(`Failed to download image: ${response.status}`);
    }
    
    const buffer = Buffer.from(await response.arrayBuffer());
    console.log(`[SceneFusion] Downloaded image, size: ${buffer.length} bytes`);
    
    // 创建缩略图
    const thumbnailBuffer = await createThumbnail(buffer);
    
    // 上传原图
    const originalFilename = generateFilename(`${prefix}-orig`, "png");
    const originalResult = await uploadFile(originalFilename, buffer.buffer.slice(buffer.byteOffset, buffer.byteOffset + buffer.byteLength));
    console.log(`[SceneFusion] Uploaded original to: ${originalResult.url}`);
    
    // 上传缩略图
    const thumbnailFilename = generateFilename(`${prefix}-thumb`, "jpg");
    const thumbnailArrayBuffer = new Uint8Array(thumbnailBuffer).buffer;
    const thumbnailResult = await uploadFile(thumbnailFilename, thumbnailArrayBuffer);
    console.log(`[SceneFusion] Uploaded thumbnail to: ${thumbnailResult.url}`);
    
    return {
      original: originalResult.url,
      thumbnail: thumbnailResult.url,
    };
  } catch (error) {
    console.error("[SceneFusion] Failed to download/upload image:", error);
    throw error;
  }
}

async function saveSceneFusionImageRecord(params: {
  userId: string;
  thumbnailUrl: string;
  originalUrl: string;
  imageSize: string;
  prompt: string;
  modelId: string;
  sourceImageUrls?: string[];
}): Promise<void> {
  let lastError: unknown = null;
  let savedImage:
    | {
        id: string;
      }
    | null = null;
  for (let attempt = 1; attempt <= 3; attempt += 1) {
    try {
      ([savedImage] = await db.insert(generatedImage).values({
        userId: params.userId,
        chatId: null,
        messageId: null,
        thumbnailUrl: params.thumbnailUrl,
        originalUrl: params.originalUrl,
        imageSize: params.imageSize,
        prompt: params.prompt,
        modelId: params.modelId,
      }).returning());
      break;
    } catch (error) {
      lastError = error;
      if (attempt < 3) {
        await waitSceneFusion(120 * attempt);
      }
    }
  }

  if (!savedImage) {
    throw lastError instanceof Error ? lastError : new Error("保存到相册失败");
  }

  try {
    const sourceAssets =
      Array.isArray(params.sourceImageUrls) && params.sourceImageUrls.length > 0
        ? await getMediaAssetsByUserAndUrls({
            userId: params.userId,
            urls: params.sourceImageUrls,
          })
        : [];
    const media = await upsertMediaAsset({
      userId: params.userId,
      mediaType: "image",
      sourceType: "edited",
      status: "ready",
      originalUrl: params.originalUrl,
      previewUrl: params.originalUrl,
      thumbnailUrl: params.thumbnailUrl,
      displayName: params.prompt.slice(0, 80),
      prompt: params.prompt,
      modelId: params.modelId,
      sourceTable: "GeneratedImage",
      sourceRowId: savedImage.id,
      primaryParentAssetId: sourceAssets[0]?.id ?? null,
      metadata: {
        imageSize: params.imageSize,
        sourceImageUrls: params.sourceImageUrls ?? [],
      },
    });
    if (media) {
      for (const sourceAsset of sourceAssets) {
        await createMediaAssetRelation({
          userId: params.userId,
          fromAssetId: sourceAsset.id,
          toAssetId: media.id,
          relationType: "edited_from",
          metadata: {
            prompt: params.prompt,
          },
        });
      }
    }
  } catch (assetError) {
    console.error("[SceneFusion] ⚠️ MediaAsset 注册失败:", assetError);
  }
}

/**
 * 根据 aspectRatio 和 imageSize 计算实际尺寸
 * @param aspectRatio - 宽高比，如 "1:1", "4:3", "16:9", "auto"
 * @param imageSize - 分辨率等级，如 "1K", "2K", "4K"
 * @returns 尺寸字符串，如 "1024*1024"，或 undefined（auto 模式）
 */
function calculateSize(aspectRatio: string, imageSize: string): string | undefined {
  // auto 模式：不设置尺寸，让 API 根据输入图片自动决定
  if (aspectRatio === "auto") {
    return undefined;
  }
  
  // 根据分辨率等级确定基准像素
  const baseSizeMap: Record<string, number> = {
    "1K": 1024,
    "2K": 1536,
    "4K": 2048,
  };
  const baseSize = baseSizeMap[imageSize] || 1536;
  
  // 根据比例计算宽高
  const ratioMap: Record<string, [number, number]> = {
    "1:1": [1, 1],
    "4:3": [4, 3],
    "3:4": [3, 4],
    "16:9": [16, 9],
    "9:16": [9, 16],
  };
  
  const ratio = ratioMap[aspectRatio];
  if (!ratio) {
    return undefined;
  }
  
  const [w, h] = ratio;
  // 保持总像素接近 baseSize * baseSize，同时维持比例
  const scale = Math.sqrt((baseSize * baseSize) / (w * h));
  let width = Math.round(w * scale);
  let height = Math.round(h * scale);
  
  // 确保宽高在 512-2048 范围内
  const clamp = (v: number) => Math.max(512, Math.min(2048, v));
  width = clamp(width);
  height = clamp(height);
  
  return `${width}*${height}`;
}

/**
 * 调用阿里生图 API
 */
async function callAlibabaImageAPI(
  baseURL: string,
  apiKey: string,
  modelId: string,
  productImageUrl: string,
  sceneImageUrl: string,
  prompt: string,
  size?: string
): Promise<string> {
  // 构建消息内容：产品图 + 场景图 + 提示词
  const content = [
    { image: productImageUrl },
    { image: sceneImageUrl },
    { text: prompt },
  ];
  
  const requestBody = {
    model: modelId,
    input: {
      messages: [
        {
          role: "user",
          content,
        },
      ],
    },
    parameters: {
      n: 1,
      ...(size && { size }),
      prompt_extend: true,
      watermark: false,
    },
  };
  
  console.log(`[SceneFusion][${modelId}] Request Debug:
    - Index: Scene ${sceneImageUrl.substring(sceneImageUrl.length-15)}
    - Product URL: ${productImageUrl}
    - Scene URL: ${sceneImageUrl}
    - Size: ${size || 'auto'}
    - Prompt: ${prompt}
  `);
  
  const response = await fetch(baseURL, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer ${apiKey}`,
    },
    body: JSON.stringify(requestBody),
  });
  
  const data = await response.json();
  
  // 打印完整响应用于调试
  console.log(`[SceneFusion][${modelId}] Full API Response:`, JSON.stringify(data, null, 2));
  
  if (!response.ok || data.code) {
    console.error("[SceneFusion] API Error Response:", JSON.stringify(data, null, 2));
    throw new Error(data.message || (data.code ? `Error ${data.code}: ${data.message}` : `API request failed: ${response.status}`));
  }
  
  // 提取生成的图片 URL
  const choices = data.output?.choices;
  if (!choices || choices.length === 0) {
    console.error("[SceneFusion] No choices in response, output:", data.output);
    throw new Error("No images in response");
  }
  
  const imageContent = choices[0]?.message?.content;
  console.log(`[SceneFusion] Image content array:`, JSON.stringify(imageContent, null, 2));
  
  if (!imageContent || imageContent.length === 0) {
    throw new Error("No image content in response");
  }
  
  // 遍历所有内容项，找到 image 类型的
  let generatedImageUrl: string | undefined;
  for (const item of imageContent) {
    if (item.image) {
      generatedImageUrl = item.image;
      console.log(`[SceneFusion] Found image URL in content item`);
      break;
    }
  }
  
  if (!generatedImageUrl) {
    console.error("[SceneFusion] No image URL found in content:", imageContent);
    throw new Error("No image URL in response");
  }
  
  // 检查返回的图片 URL 是否与输入相同（可能是 API 返回了原图）
  if (generatedImageUrl === productImageUrl || generatedImageUrl === sceneImageUrl) {
    console.warn(`[SceneFusion] ⚠️ WARNING: API returned same URL as input!`);
    console.warn(`[SceneFusion] - Generated URL: ${generatedImageUrl}`);
    console.warn(`[SceneFusion] - Product URL: ${productImageUrl}`);
    console.warn(`[SceneFusion] - Scene URL: ${sceneImageUrl}`);
  }
  
  console.log("[SceneFusion] Generated image URL:", generatedImageUrl.substring(0, 100) + "...");
  return generatedImageUrl;
}

/**
 * 构建智能提示词
 * @param defaultPrompt - 默认提示词模板
 * @param productName - 产品名称（如"沙发"）
 * @param targetName - 背景中需要被替换的物体名称（如"椅子"）
 * @param customPrompt - 用户补充的关键词
 */
function buildPrompt(
  defaultPrompt: string,
  productName?: string,
  targetName?: string,
  customPrompt?: string,
  sceneMode: SceneFusionMode = "scene-fission"
): string {
  let prompt = defaultPrompt;

  if (sceneMode === "random-bg") {
    const subject = productName || "产品";
    prompt = `请以第一张图片中的${subject}为唯一主体，对第二张图片进行背景重绘。要求：1)${subject}外观、材质、颜色与细节保持一致；2)允许轻微调整主体位置与角度以匹配新背景；3)背景需要完整重建并与原图明显不同；4)光影透视自然，输出商业摄影质感。`;
  } else if (productName && targetName) {
    // 如果用户提供了具体的产品名称和被替换物体名称，构建更精确的提示词
    // 使用"参考图1"的表述方式，并强调产品的视觉特征
    prompt = `请参考第一张图片中的${productName}，将其自然地融合到第二张图片的场景中，替换掉场景中的${targetName}。要求：1)保持${productName}的整体外观、颜色、形状和设计风格与第一张图一致；2)调整${productName}的大小和角度以适应场景；3)注意光影和透视关系使融合自然。`;
  } else if (productName) {
    prompt = `请参考第一张图片中的${productName}，将其自然地融合到第二张图片的场景中。要求：1)保持${productName}的整体外观、颜色、形状和设计风格与第一张图一致；2)调整${productName}的大小和角度以适应场景；3)注意光影和透视关系使融合自然。`;
  } else if (targetName) {
    prompt = `请参考第一张图片中的产品，将其自然地融合到第二张图片的场景中，替换掉场景中的${targetName}。要求：1)保持产品的整体外观、颜色、形状和设计风格与第一张图一致；2)调整产品的大小和角度以适应场景；3)注意光影和透视关系使融合自然。`;
  }
  
  // 添加用户补充的关键词
  if (customPrompt) {
    prompt = `${prompt} 补充要求：${customPrompt}`;
  }

  if (sceneMode === "random-bg") {
    if (!prompt.includes("背景必须与原图明显不同")) {
      prompt = `${prompt} ${SCENE_FUSION_RANDOM_BG_PROMPT_TAIL}`;
    }
  } else if (!prompt.includes("严禁直接返回第一张参考图")) {
    prompt = `${prompt} ${SCENE_FUSION_STRICT_PROMPT_TAIL}`;
  }
  
  return prompt;
}

/**
 * 通用场景处理函数 - 根据 provider 类型调用不同的实现
 */
async function processSceneWithProvider(
  productImageUrl: string,
  sceneImageUrl: string,
  productName: string | undefined,
  targetName: string | undefined,
  customPrompt: string | undefined,
  defaultPrompt: string,
  sceneMode: SceneFusionMode,
  provider: { type: string; baseUrl: string; apiKey: string },
  modelId: string,
  aspectRatio?: string,
  imageSize?: string
): Promise<{ thumbnail: string; original: string; prompt: string }> {
  // 构建智能提示词
  const prompt = buildPrompt(defaultPrompt, productName, targetName, customPrompt, sceneMode);
  
  console.log(`[SceneFusion] Built prompt: ${prompt}`);
  console.log(`[SceneFusion] Provider type: ${provider.type}`);
  
  // 根据 provider 类型选择不同的处理逻辑
  switch (provider.type) {
    case "alibaba-image":
      return processWithAlibabaImage(productImageUrl, sceneImageUrl, prompt, provider, modelId, aspectRatio, imageSize);
    
    case "ruxa-image":
      return processWithRuxaImage(productImageUrl, sceneImageUrl, prompt, provider, modelId, aspectRatio, imageSize);

    case "fal-image":
      return processWithFalImage(productImageUrl, sceneImageUrl, prompt, provider, modelId, aspectRatio, imageSize);

    case "openai-image":
      return processWithOpenAIImage(productImageUrl, sceneImageUrl, prompt, provider, modelId, aspectRatio, imageSize);

    case "gemini":
    case "gemini-image":
      return processWithGemini(productImageUrl, sceneImageUrl, prompt, provider, modelId, aspectRatio, imageSize);
    
    case "volcano-vision":
      return processWithVolcanoVision(productImageUrl, sceneImageUrl, prompt, provider, modelId, aspectRatio, imageSize);
    
    default:
      throw new Error(`不支持的模型类型: ${provider.type}`);
  }
}

/**
 * 使用阿里生图模型处理
 */
async function processWithAlibabaImage(
  productImageUrl: string,
  sceneImageUrl: string,
  prompt: string,
  provider: { baseUrl: string; apiKey: string },
  modelId: string,
  aspectRatio?: string,
  imageSize?: string
): Promise<{ thumbnail: string; original: string; prompt: string }> {
  // 计算尺寸
  const size = calculateSize(aspectRatio || "auto", imageSize || "2K");
  
  // 调用阿里生图 API
  const generatedImageUrl = await callAlibabaImageAPI(
    provider.baseUrl,
    provider.apiKey,
    modelId,
    productImageUrl,
    sceneImageUrl,
    prompt,
    size
  );
  
  // 下载并上传到 S3
  const uploaded = await downloadAndUploadToS3(generatedImageUrl, "scene-fusion-alibaba");
  
  return { ...uploaded, prompt };
}

/**
 * 使用 Gemini 模型处理
 */
async function processWithGemini(
  productImageUrl: string,
  sceneImageUrl: string,
  prompt: string,
  provider: { baseUrl: string; apiKey: string },
  modelId: string,
  aspectRatio?: string,
  imageSize?: string
): Promise<{ thumbnail: string; original: string; prompt: string }> {
  console.log(`[SceneFusion][Gemini] Processing with model: ${modelId}`);
  
  // 创建 Gemini provider
  const geminiProvider = createGeminiImage({
    baseURL: provider.baseUrl,
    apiKey: provider.apiKey,
  });
  
  // 构建消息格式（Gemini 使用 OpenAI 兼容格式）
  const messages = [
    {
      role: "user",
      content: [
        { type: "image_url", image_url: { url: productImageUrl } },
        { type: "image_url", image_url: { url: sceneImageUrl } },
        { type: "text", text: prompt },
      ],
    },
  ];
  
  // 调用 Gemini API
  const result = await geminiProvider.chat(modelId).doGenerate({
    messages,
    imageParams: {
      aspectRatio: aspectRatio || "auto",
      imageSize: imageSize || "2K",
      imageCount: 1,
    },
  });
  
  // 解析返回的 Markdown 格式图片
  const { thumbnail, original } = parseImageFromMarkdown(result.text);
  
  return { thumbnail, original, prompt };
}

/**
 * 使用 RUXA 模型处理
 */
async function processWithRuxaImage(
  productImageUrl: string,
  sceneImageUrl: string,
  prompt: string,
  provider: { baseUrl: string; apiKey: string },
  modelId: string,
  aspectRatio?: string,
  imageSize?: string
): Promise<{ thumbnail: string; original: string; prompt: string }> {
  console.log(`[SceneFusion][RUXA] Processing with model: ${modelId}`);

  const ruxaProvider = createRuxaImage({
    baseURL: provider.baseUrl,
    apiKey: provider.apiKey,
  });

  const messages = [
    {
      role: "user",
      content: [
        { type: "image_url", image_url: { url: productImageUrl } },
        { type: "image_url", image_url: { url: sceneImageUrl } },
        { type: "text", text: prompt },
      ],
    },
  ];

  const result = await ruxaProvider.chat(modelId).doGenerate({
    messages,
    imageParams: {
      aspectRatio: aspectRatio || "auto",
      imageSize: imageSize || "2K",
      imageCount: 1,
    },
  });

  const { thumbnail, original } = parseImageFromMarkdown(result.text);
  return { thumbnail, original, prompt };
}

async function processWithFalImage(
  productImageUrl: string,
  sceneImageUrl: string,
  prompt: string,
  provider: { baseUrl: string; apiKey: string },
  modelId: string,
  aspectRatio?: string,
  imageSize?: string
): Promise<{ thumbnail: string; original: string; prompt: string }> {
  console.log(`[SceneFusion][FAL] Processing with model: ${modelId}`);

  const falProvider = createFalImage({
    baseURL: provider.baseUrl,
    apiKey: provider.apiKey,
  });

  const messages = [
    {
      role: "user",
      content: [
        { type: "image_url", image_url: { url: productImageUrl } },
        { type: "image_url", image_url: { url: sceneImageUrl } },
        { type: "text", text: prompt },
      ],
    },
  ];

  const result = await falProvider.chat(modelId).doGenerate({
    messages,
    imageParams: {
      aspectRatio: aspectRatio || "auto",
      imageSize: imageSize || "2K",
      imageCount: 1,
    },
  });

  const { thumbnail, original } = parseImageFromMarkdown(result.text);
  return { thumbnail, original, prompt };
}

async function processWithOpenAIImage(
  productImageUrl: string,
  sceneImageUrl: string,
  prompt: string,
  provider: { baseUrl: string; apiKey: string },
  modelId: string,
  aspectRatio?: string,
  imageSize?: string
): Promise<{ thumbnail: string; original: string; prompt: string }> {
  console.log(`[SceneFusion][OpenAI Image] Processing with model: ${modelId}`);

  const openAIImageProvider = createOpenAIImage({
    baseURL: provider.baseUrl,
    apiKey: provider.apiKey,
  });

  const messages = [
    {
      role: "user",
      content: [
        { type: "image_url", image_url: { url: productImageUrl } },
        { type: "image_url", image_url: { url: sceneImageUrl } },
        { type: "text", text: prompt },
      ],
    },
  ];

  const result = await openAIImageProvider.chat(modelId).doGenerate({
    messages,
    imageParams: {
      aspectRatio: aspectRatio || "auto",
      imageSize: imageSize || "2K",
      imageCount: 1,
    },
  });

  const { thumbnail, original } = parseImageFromMarkdown(result.text);
  return { thumbnail, original, prompt };
}

/**
 * 使用既梦/火山模型处理
 */
async function processWithVolcanoVision(
  productImageUrl: string,
  sceneImageUrl: string,
  prompt: string,
  provider: { baseUrl: string; apiKey: string },
  modelId: string,
  aspectRatio?: string,
  imageSize?: string
): Promise<{ thumbnail: string; original: string; prompt: string }> {
  console.log(`[SceneFusion][Volcano] Processing with model: ${modelId}`);
  
  // 创建 Volcano Vision provider
  const volcanoProvider = createVolcanoVision({
    baseURL: provider.baseUrl,
    apiKey: provider.apiKey,
  });
  
  // 构建消息格式
  const messages = [
    {
      role: "user",
      content: [
        { type: "image_url", image_url: { url: productImageUrl } },
        { type: "image_url", image_url: { url: sceneImageUrl } },
        { type: "text", text: prompt },
      ],
    },
  ];
  
  // 调用 Volcano Vision API
  const result = await volcanoProvider.chat(modelId).doGenerate({
    messages,
    imageParams: {
      aspectRatio: aspectRatio || "auto",
      imageSize: imageSize || "2K",
      imageCount: 1,
    },
  });
  
  // 解析返回的 Markdown 格式图片
  const { thumbnail, original } = parseImageFromMarkdown(result.text);
  
  return { thumbnail, original, prompt };
}

/**
 * 从 Markdown 格式解析图片 URL
 * 格式: ![thumb|||originalUrl|||size](thumbnailUrl)
 */
function parseImageFromMarkdown(markdown: string): { thumbnail: string; original: string } {
  console.log(`[SceneFusion] Parsing markdown: ${markdown.substring(0, 200)}...`);
  
  // 匹配 ![thumb|||originalUrl|||size](thumbnailUrl) 格式
  const match = markdown.match(/!\[thumb\|\|\|([^|]+)\|\|\|[^\]]*\]\(([^)]+)\)/);
  if (match) {
    return {
      original: match[1],
      thumbnail: match[2],
    };
  }
  
  // 尝试匹配普通图片格式 ![alt](url)
  const simpleMatch = markdown.match(/!\[[^\]]*\]\(([^)]+)\)/);
  if (simpleMatch) {
    return {
      original: simpleMatch[1],
      thumbnail: simpleMatch[1],
    };
  }
  
  throw new Error("无法从响应中解析图片 URL");
}

/**
 * 处理单个场景 (保留旧函数用于兼容)
 */
async function processScene(
  productImageUrl: string,
  sceneImageUrl: string,
  productName: string | undefined,
  targetName: string | undefined,
  customPrompt: string | undefined,
  defaultPrompt: string,
  sceneMode: SceneFusionMode,
  baseURL: string,
  apiKey: string,
  modelId: string,
  size?: string
): Promise<{ thumbnail: string; original: string; prompt: string }> {
  // 构建智能提示词
  const prompt = buildPrompt(defaultPrompt, productName, targetName, customPrompt, sceneMode);
  
  console.log(`[SceneFusion] Built prompt: ${prompt}`);
  
  // 调用阿里生图 API
  const generatedImageUrl = await callAlibabaImageAPI(
    baseURL,
    apiKey,
    modelId,
    productImageUrl,
    sceneImageUrl,
    prompt,
    size
  );
  
  // 下载并上传到 S3
  const uploaded = await downloadAndUploadToS3(generatedImageUrl, "scene-fusion");
  
  return { ...uploaded, prompt };
}

async function processSceneWithDiversity(
  params: {
    productImageUrl: string;
    sceneImageUrl: string;
    productName?: string;
    targetName?: string;
    customPrompt?: string;
    defaultPrompt: string;
    sceneMode: SceneFusionMode;
    aspectRatio?: string;
    imageSize?: string;
    sceneIndex: number;
    requestOffset: number;
    requestId: string;
    candidates: SceneFusionModelCandidate[];
    imageHashCache: Map<string, string>;
  }
): Promise<{
  result: { thumbnail: string; original: string; prompt: string };
  candidate: SceneFusionModelCandidate;
  attempts: number;
}> {
  const supportedCandidates = params.candidates.filter((candidate) =>
    canProcessSceneFusionProviderType(candidate.provider.type)
  );
  if (supportedCandidates.length === 0) {
    throw new Error("后台已配置生图模型中暂无可用于场景裂变的供应商，请检查模型供应商类型");
  }

  const rotatedCandidates = rotateCandidates(
    supportedCandidates,
    params.requestOffset + params.sceneIndex
  );
  pruneSceneFusionCooldown();

  const healthyCandidates = rotatedCandidates.filter(
    (candidate) => !isSceneFusionCandidateCoolingDown(candidate)
  );
  const attemptCandidates =
    healthyCandidates.length > 0 ? healthyCandidates : rotatedCandidates;

  console.log(
    `[SceneFusion][${params.requestId}] Scene#${params.sceneIndex + 1} candidate order: ${attemptCandidates
      .map((candidate) => `${candidate.model.displayName}/${candidate.provider.type}`)
      .join(" -> ")}`
  );

  const failureReasons: string[] = [];

  for (let attemptIndex = 0; attemptIndex < attemptCandidates.length; attemptIndex += 1) {
    const candidate = attemptCandidates[attemptIndex]!;
    try {
      const normalizedOutputParams = normalizeSceneFusionOutputParamsForCandidate(candidate, {
        aspectRatio: params.aspectRatio,
        imageSize: params.imageSize,
      });
      if (normalizedOutputParams.changed) {
        console.log(
          `[SceneFusion][${params.requestId}] Candidate ${candidate.model.displayName} output params normalized: ${normalizedOutputParams.changes.join(", ")}`
        );
      }

      const result = await processSceneWithProvider(
        params.productImageUrl,
        params.sceneImageUrl,
        params.productName,
        params.targetName,
        params.customPrompt,
        params.defaultPrompt,
        params.sceneMode,
        {
          type: candidate.provider.type,
          baseUrl: candidate.provider.baseUrl,
          apiKey: candidate.provider.apiKey,
        },
        candidate.model.modelId,
        normalizedOutputParams.aspectRatio,
        normalizedOutputParams.imageSize
      );

      const echoCheck = await detectEchoedProductResult({
        requestId: params.requestId,
        sceneIndex: params.sceneIndex,
        productImageUrl: params.productImageUrl,
        generatedImageUrl: result.original,
        modelName: candidate.model.displayName,
        hashCache: params.imageHashCache,
      });
      if (echoCheck.tooSimilar) {
        const detail =
          typeof echoCheck.hammingDistance === "number"
            ? `hamming=${echoCheck.hammingDistance}`
            : echoCheck.reason || "unknown";
        throw new Error(`生成结果疑似回传产品图（${detail}）`);
      }

      markSceneFusionCandidateHealthy(candidate);
      return {
        result,
        candidate,
        attempts: attemptIndex + 1,
      };
    } catch (error) {
      const errorMessage = error instanceof Error ? error.message : "未知错误";
      failureReasons.push(`${candidate.model.displayName}: ${errorMessage}`);
      markSceneFusionCandidateFailed(candidate);
      console.error("[SceneFusion] Candidate failed, try next:", {
        requestId: params.requestId,
        sceneIndex: params.sceneIndex,
        attempt: attemptIndex + 1,
        model: candidate.model.modelId,
        provider: candidate.provider.type,
        error: errorMessage,
      });
    }
  }

  const reason = failureReasons.join("；");
  throw new Error(
    reason
      ? `多样性模式下所有可用模型均失败：${reason}`
      : "多样性模式下所有可用模型均失败"
  );
}

export async function POST(request: Request) {
  try {
    // 1. 认证
    const session = await auth();
    if (!session?.user?.id) {
      return NextResponse.json(
        { error: "未登录" },
        { status: 401 }
      );
    }
    
    // 2. 获取场景裂变配置
    const config = await getSceneFusionConfig();
    
    if (!config.enabled) {
      return NextResponse.json(
        { error: "场景裂变功能已被管理员禁用" },
        { status: 403 }
      );
    }
    
    // 3. 解析请求体
    const json = await request.json();
    const parseResult = sceneFusionRequestSchema.safeParse(json);
    
    if (!parseResult.success) {
      return NextResponse.json(
        { error: parseResult.error.errors[0].message },
        { status: 400 }
      );
    }
    
    const { productImage, productName, sceneMode, aspectRatio, imageSize, scenes } = parseResult.data;
    const requestId = `sf-${Date.now()}-${Math.random().toString(36).slice(2, 8)}`;
    const resolvedSceneMode = sceneMode || "scene-fission";
    
    // 计算输出尺寸
    const size = calculateSize(aspectRatio || "auto", imageSize || "2K");
    console.log(`[SceneFusion] Output settings: aspectRatio=${aspectRatio || 'auto'}, imageSize=${imageSize || '2K'}, calculated size=${size || 'auto'}`);
    
    // 检查场景数量限制
    const effectiveMaxScenes = Math.min(config.maxScenes, SCENE_FUSION_MAX_ITEMS);
    if (scenes.length > effectiveMaxScenes) {
      return NextResponse.json(
        { error: `最多只能添加 ${effectiveMaxScenes} 个场景` },
        { status: 400 }
      );
    }
    
    console.log(`[SceneFusion] Processing ${scenes.length} scenes for user ${session.user.id}`);
    console.log(`[SceneFusion] Product name: ${productName || '(not specified)'}`);
    console.log(`[SceneFusion] Scene mode: ${resolvedSceneMode}`);
    console.log(`[SceneFusion] Using default prompt: ${config.defaultPrompt.substring(0, 50)}...`);
    console.log(`[SceneFusion] Preferred model ID: ${config.modelId || '(none)'}`);
    
    // 4. 获取生图模型池（固定多样性模式）
    const allModels = await getEnabledAiModelsWithProvider();
    const candidateModels = buildSceneFusionCandidatePool(allModels, config.modelId);
    if (config.modelId && !candidateModels.some((candidate) => candidate.model.id === config.modelId)) {
      console.warn(
        `[SceneFusion] Preferred model ${config.modelId} not available, fallback to full diversity pool`
      );
    }

    if (candidateModels.length === 0) {
      return NextResponse.json(
        { error: "未找到可用模型，请在后台启用至少一个支持生图的模型并配置供应商 API Key" },
        { status: 500 }
      );
    }

    console.log(
      `[SceneFusion] Diversity candidates: ${candidateModels
        .map((candidate) => `${candidate.model.displayName}(${candidate.provider.type})`)
        .join(", ")}`
    );
    const unsupportedCandidates = candidateModels.filter(
      (candidate) => !canProcessSceneFusionProviderType(candidate.provider.type)
    );
    if (unsupportedCandidates.length > 0) {
      console.warn(
        `[SceneFusion] Skip unsupported providers for scene fusion: ${unsupportedCandidates
          .map((candidate) => `${candidate.model.displayName}(${candidate.provider.type})`)
          .join(", ")}`
      );
    }
    const nonVisionCandidates = candidateModels.filter(
      (candidate) => !candidate.model.supportsVision
    );
    if (nonVisionCandidates.length > 0) {
      console.warn(
        `[SceneFusion] Models without supportsVision are included for failover diversity: ${nonVisionCandidates
          .map((candidate) => `${candidate.model.displayName}(${candidate.provider.type})`)
          .join(", ")}`
      );
    }
    const requestOffset = getNextSceneFusionRoundRobinOffset(
      Math.max(1, candidateModels.length)
    );
    console.log(
      `[SceneFusion][${requestId}] Round-robin offset=${requestOffset}, scenes=${scenes.length}, candidates=${candidateModels.length}`
    );
    
    // 5. 预处理：将 Base64 图片上传到 S3
    let productImageUrl = productImage;
    if (productImage.startsWith("data:")) {
      productImageUrl = await uploadBase64ToS3(productImage, "scene-fusion-product");
    }
    const shouldForcePreRmbg = resolvedSceneMode === "random-bg";
    const preprocessedProduct = await preprocessProductImageWithRmbg(
      request,
      requestId,
      productImageUrl,
      shouldForcePreRmbg ? true : config.preRmbgEnabled
    );
    const fusionProductImageUrl = preprocessedProduct.productImageUrl;
    console.log(
      `[SceneFusion][${requestId}] Pre-RMBG result: used=${preprocessedProduct.usedPreRmbg}, provider=${preprocessedProduct.provider || "-"}, reason=${preprocessedProduct.reason || "-"}`
    );
    
    const processedScenes = await Promise.all(
      scenes.map(async (scene) => {
        let sceneImageUrl = scene.image;
        if (scene.image.startsWith("data:")) {
          sceneImageUrl = await uploadBase64ToS3(scene.image, "scene-fusion-scene");
        }
        return {
          ...scene,
          imageUrl: sceneImageUrl,
        };
      })
    );

    const enriched = await enrichSceneLabelsWithAgent({
      requestId,
      productImageUrl: fusionProductImageUrl,
      productName,
      scenes: processedScenes,
    });
    const resolvedProductName = enriched.productName;
    let resolvedScenes = enriched.scenes;
    if (resolvedSceneMode === "random-bg") {
      resolvedScenes = await enrichRandomBackgroundScenesWithAgent({
        requestId,
        productImageUrl: fusionProductImageUrl,
        productName: resolvedProductName,
        scenes: resolvedScenes,
      });
    }
    const resolvedSceneMap = new Map(resolvedScenes.map((scene) => [scene.id, scene]));
    const imageHashCache = new Map<string, string>();

    console.log(
      `[SceneFusion][${requestId}] Agent labels: product=${resolvedProductName || "(none)"}; targets=${resolvedScenes
        .map((scene) => `${scene.id}:${scene.targetName || "-"}`)
        .join(", ")}`
    );
    if (resolvedSceneMode === "random-bg") {
      console.log(
        `[SceneFusion][${requestId}] Random-bg prompts: ${resolvedScenes
          .map((scene) => `${scene.id}:${(scene.customPrompt || "").slice(0, 80) || "-"}`)
          .join(" | ")}`
      );
    }

    // 6. 创建流式响应，每完成一张图就立即发送给前端
    const stream = new ReadableStream({
      async start(controller) {
        const encoder = new TextEncoder();
        let streamClosed = false;
        const safeClose = () => {
          if (streamClosed) return;
          streamClosed = true;
          try {
            controller.close();
          } catch (closeError) {
            console.warn("[SceneFusion] Stream close skipped:", closeError);
          }
        };
        const enqueue = (payload: Record<string, unknown>) => {
          if (streamClosed) return false;
          try {
            controller.enqueue(encoder.encode(`${JSON.stringify(payload)}\n`));
            return true;
          } catch (enqueueError) {
            streamClosed = true;
            console.warn("[SceneFusion] Stream enqueue skipped:", enqueueError);
            return false;
          }
        };
        
        try {
          // 发送初始消息
          if (!enqueue({ type: "start", total: processedScenes.length })) {
            return;
          }
          enqueue({
            type: "agent_meta",
            data: {
              productName: enriched.meta.productName || resolvedProductName || "",
              productSource: enriched.meta.productSource || (resolvedProductName ? "agent" : "none"),
              sceneTargets: enriched.meta.sceneTargets,
            },
          });
          
          let successCount = 0;
          let failCount = 0;

          let nextSceneIndex = 0;
          const workerCount = Math.max(
            1,
            Math.min(
              SCENE_FUSION_PROCESS_MAX_WORKERS,
              candidateModels.length,
              processedScenes.length
            )
          );

          const workers = Array.from({ length: workerCount }, async () => {
            while (true) {
              if (streamClosed) break;
              const currentIndex = nextSceneIndex;
              nextSceneIndex += 1;
              if (currentIndex >= processedScenes.length) break;

              const staggerDelay = (currentIndex % workerCount) * SCENE_FUSION_REQUEST_STAGGER_MS;
              if (staggerDelay > 0) {
                await waitSceneFusion(staggerDelay);
              }

              const scene = processedScenes[currentIndex];
              if (!scene) break;
              const resolvedScene = resolvedSceneMap.get(scene.id) || scene;

              console.log(
                `[SceneFusion] ========== Processing scene ${currentIndex + 1}/${processedScenes.length} ==========`
              );
              console.log(`[SceneFusion] Scene ID: ${scene.id}`);
              console.log(
                `[SceneFusion] Product Image URL: ${fusionProductImageUrl.substring(0, 80)}...`
              );
              console.log(
                `[SceneFusion] Scene Image URL: ${scene.imageUrl.substring(0, 80)}...`
              );
              console.log(
                `[SceneFusion] Target Name: ${resolvedScene.targetName || "(not specified)"}`
              );
              console.log(
                `[SceneFusion] Custom Prompt: ${resolvedScene.customPrompt || "(none)"}`
              );

              try {
                const { result, candidate, attempts } = await processSceneWithDiversity({
                  productImageUrl: fusionProductImageUrl,
                  sceneImageUrl: resolvedScene.imageUrl,
                  productName: resolvedProductName,
                  targetName: resolvedScene.targetName,
                  customPrompt: resolvedScene.customPrompt,
                  defaultPrompt: config.defaultPrompt,
                  sceneMode: resolvedSceneMode,
                  aspectRatio,
                  imageSize,
                  sceneIndex: currentIndex,
                  requestOffset,
                  requestId,
                  candidates: candidateModels,
                  imageHashCache,
                });

                const dbImageSize = imageSize || "2K";

                await saveSceneFusionImageRecord({
                  userId: session.user.id,
                  thumbnailUrl: result.thumbnail,
                  originalUrl: result.original,
                  imageSize: dbImageSize,
                  prompt: `[场景裂变] ${result.prompt}`,
                  modelId: candidate.model.modelId,
                  sourceImageUrls: [fusionProductImageUrl, resolvedScene.imageUrl],
                });
                console.log(
                  `[SceneFusion] ✅ Saved image to gallery: ${result.original.substring(0, 80)}...`
                );

                successCount += 1;
                if (!enqueue({
                  type: "result",
                  data: {
                    id: scene.id,
                    status: "completed" as const,
                    result: result.thumbnail,
                    originalUrl: result.original,
                    model: candidate.model.displayName,
                    modelId: candidate.model.modelId,
                    providerType: candidate.provider.type,
                    attempts,
                    recognizedProductName: resolvedProductName,
                    recognizedTargetName: resolvedScene.targetName,
                  },
                })) {
                  break;
                }
              } catch (error) {
                console.error(`[SceneFusion] Failed to process scene ${scene.id}:`, error);
                failCount += 1;
                if (!enqueue({
                  type: "result",
                  data: {
                    id: scene.id,
                    status: "failed" as const,
                    error: error instanceof Error ? error.message : "处理失败",
                  },
                })) {
                  break;
                }
              }
            }
          });

          await Promise.all(workers);
          
          // 发送完成消息
          console.log(`[SceneFusion] Completed: ${successCount} success, ${failCount} failed`);
          enqueue({
            type: "done",
            summary: {
              total: processedScenes.length,
              success: successCount,
              failed: failCount,
            },
          });
          
        } catch (error) {
          console.error("[SceneFusion] Stream error:", error);
          // 发送错误消息
          if (!streamClosed) {
            enqueue({
              type: "error",
              error: error instanceof Error ? error.message : "处理失败",
            });
          }
        } finally {
          safeClose();
        }
      },
    });
    
    return new Response(stream, {
      headers: {
        "Content-Type": "text/event-stream",
        "Cache-Control": "no-cache",
        "Connection": "keep-alive",
      },
    });
  } catch (error) {
    console.error("[SceneFusion] Unexpected error:", error);
    return NextResponse.json(
      { error: error instanceof Error ? error.message : "处理失败" },
      { status: 500 }
    );
  }
}