import { generateText } from "ai";
import { execFile } from "node:child_process";
import { promisify } from "node:util";
import { mkdtemp, readFile, rm } from "node:fs/promises";
import os from "node:os";
import path from "node:path";
import { NextResponse } from "next/server";
import sharp from "sharp";
import { z } from "zod";
import { auth } from "@/app/(auth)/auth";
import { getLanguageModelById } from "@/lib/ai/dynamic-provider";
import { inlineVisionImages } from "@/lib/ai/vision-inline";
import { getAiModelAgentCodingModelId, isAiModelMultimodal } from "@/lib/db/ai-model-flags";
import {
  getAgentAiModelWithProvider,
  getEnabledAiModelsWithProvider,
} from "@/lib/db/queries";

export const runtime = "nodejs";
export const maxDuration = 180;

const execFileAsync = promisify(execFile);
const VIDEO_ANALYSIS_FRAME_COUNT = 4;
const VIDEO_ANALYSIS_MAX_SIDE = 960;
const VIDEO_ANALYSIS_TIMEOUT_MS = 15000;

const requestSchema = z.object({
  mode: z.enum(["video-analysis", "character-scene-extraction", "story-script", "agent-plan"]),
  prompt: z.string().trim().optional(),
  imageUrls: z.array(z.string().trim()).optional(),
  videoUrls: z.array(z.string().trim()).optional(),
  nodeTitle: z.string().trim().optional(),
  preferredModelRef: z.string().trim().optional(),
  model: z.string().trim().optional(),
});

function sanitizeUrls(value: unknown, limit = 6) {
  if (!Array.isArray(value)) return [];
  return value
    .map((item) => String(item || "").trim())
    .filter((url) => /^https?:\/\//i.test(url))
    .slice(0, limit);
}

function supportsVision(model: {
  supportsVision?: boolean | null;
  capabilities?: unknown;
  defaultParams?: unknown;
}) {
  return (
    Boolean(model.supportsVision) ||
    isAiModelMultimodal({
      capabilities: model.capabilities,
      defaultParams: model.defaultParams,
    })
  );
}

function extractJsonBlock<T = Record<string, unknown>>(text: string): T | null {
  const matched = String(text || "").match(/\{[\s\S]*\}/);
  if (!matched) return null;
  try {
    return JSON.parse(matched[0]) as T;
  } catch {
    return null;
  }
}

function normalizeText(value: unknown) {
  return String(value || "").trim();
}

function shouldUseLocalWorkflowE2E(request: Request) {
  const hostname = new URL(request.url).hostname;
  return (
    process.env.CANVAS_E2E_LOCAL === "true" &&
    (hostname === "127.0.0.1" || hostname === "localhost" || hostname === "::1")
  );
}

function normalizeStringList(value: unknown, limit = 8) {
  if (!Array.isArray(value)) return [];
  return Array.from(
    new Set(
      value
        .map((item) => String(item || "").trim())
        .filter(Boolean)
        .slice(0, limit)
    )
  );
}

function normalizeShotIdeas(value: unknown) {
  if (!Array.isArray(value)) return [];
  return value
    .map((item, index) => {
      const raw =
        item && typeof item === "object" && !Array.isArray(item)
          ? (item as Record<string, unknown>)
          : {};
      const title = normalizeText(raw.title || `镜头 ${index + 1}`);
      const description = normalizeText(raw.description || raw.prompt || raw.summary);
      if (!title && !description) return null;
      return {
        title: title || `镜头 ${index + 1}`,
        description,
      };
    })
    .filter(
      (item): item is { title: string; description: string } => Boolean(item)
    )
    .slice(0, 6);
}

function normalizeVideoAnalysisRows(
  value: unknown,
  framePreviews: string[],
) {
  if (!Array.isArray(value)) return [];
  return value
    .map((item, index) => {
      const raw =
        item && typeof item === "object" && !Array.isArray(item)
          ? (item as Record<string, unknown>)
          : {};
      const sceneDescription = normalizeText(
        raw.sceneDescription || raw.scene || raw.title || raw.description,
      );
      const narrative = normalizeText(
        raw.narrative || raw.story || raw.storyContent || raw.narration || raw.summary,
      );
      const shot = normalizeText(raw.shot || raw.shotNumber || raw.index || index + 1);
      const startTime = normalizeText(raw.startTime || raw.start || raw.start_time);
      const endTime = normalizeText(raw.endTime || raw.end || raw.end_time);
      const duration = normalizeText(raw.duration || raw.durationSeconds || raw.duration_seconds);
      const shotSize = normalizeText(raw.shotSize || raw.frameSize || raw.shot_type || raw.sceneScale);
      const cameraAngle = normalizeText(raw.cameraAngle || raw.angle || raw.camera_angle);
      const cameraMovement = normalizeText(
        raw.cameraMovement || raw.cameraMove || raw.camera_motion || raw.motionType,
      );
      const focusDepth = normalizeText(
        raw.focusDepth || raw.depthOfField || raw.focalDepth || raw.focus_depth,
      );
      const lighting = normalizeText(raw.lighting || raw.light || raw.lightDesign);
      const backgroundMusic = normalizeText(
        raw.backgroundMusic || raw.music || raw.bgm,
      );
      const voiceAndSfx = normalizeText(
        raw.voiceAndSfx || raw.sound || raw.audio || raw.sfx,
      );
      const imagePrompt = normalizeText(
        raw.imagePrompt || raw.image_generation_prompt || raw.image,
      );
      const motionPrompt = normalizeText(
        raw.motionPrompt || raw.motion || raw.videoPrompt || raw.video_motion_prompt,
      );
      const keyframeIndex = Number(
        raw.keyframeIndex || raw.frameIndex || raw.key_frame_index || index + 1,
      );
      if (
        !sceneDescription &&
        !narrative &&
        !backgroundMusic &&
        !voiceAndSfx &&
        !imagePrompt &&
        !motionPrompt &&
        !shotSize &&
        !cameraAngle &&
        !cameraMovement &&
        !focusDepth &&
        !lighting
      ) {
        return null;
      }
      const normalizedFrameIndex =
        Number.isFinite(keyframeIndex) && keyframeIndex > 0
          ? Math.max(0, Math.min(framePreviews.length - 1, Math.round(keyframeIndex) - 1))
          : index % Math.max(framePreviews.length, 1);
      return {
        shot: shot || String(index + 1),
        startTime: startTime || (index === 0 ? "0" : ""),
        endTime,
        duration,
        sceneDescription: sceneDescription || `镜头 ${index + 1}`,
        narrative: narrative || sceneDescription || `镜头 ${index + 1}`,
        shotSize: shotSize || "中景至近景",
        cameraAngle: cameraAngle || "平视",
        cameraMovement: cameraMovement || "缓慢推镜头 (Slow Zoom In)",
        focusDepth: focusDepth || "浅景深",
        lighting: lighting || "冷调低沉照明，主体光线柔和。",
        backgroundMusic: backgroundMusic || "无",
        voiceAndSfx: voiceAndSfx || "无",
        imagePrompt: imagePrompt || sceneDescription || "延续当前视频气质生成关键帧",
        motionPrompt: motionPrompt || "保持镜头运动自然、连贯、符合前后节奏。",
        keyframeUrl: framePreviews[normalizedFrameIndex] || "",
      };
    })
    .filter(
      (
        item,
      ): item is {
        shot: string;
        startTime: string;
        endTime: string;
        duration: string;
        sceneDescription: string;
        narrative: string;
        shotSize: string;
        cameraAngle: string;
        cameraMovement: string;
        focusDepth: string;
        lighting: string;
        backgroundMusic: string;
        voiceAndSfx: string;
        imagePrompt: string;
        motionPrompt: string;
        keyframeUrl: string;
      } => Boolean(item),
    )
    .slice(0, 8);
}

function normalizeStoryShot(value: unknown, index: number) {
  const text = normalizeText(value);
  if (/^\d+$/.test(text)) return text.padStart(2, "0");
  return text || String(index + 1).padStart(2, "0");
}

type NormalizedStoryScriptRow = {
  shot: string;
  duration: string;
  scene: string;
  sceneDescription: string;
  shotSize: string;
  lighting: string;
  dialogue: string;
  backgroundMusic: string;
  voiceAndSfx: string;
  cameraMovement: string;
  imagePrompt: string;
  motionPrompt: string;
  keyframeUrl: string;
};

function normalizeStoryScriptRows(value: unknown): NormalizedStoryScriptRow[] {
  if (!Array.isArray(value)) return [];
  return value
    .map((item, index) => {
      if (Array.isArray(item)) {
        const cells = item.map((cell) => normalizeText(cell));
        const shot = normalizeStoryShot(cells[0], index);
        const secondCellLooksLikeDuration = /^\d+(\.\d+)?\s*(s|秒)?$/i.test(cells[1] || "");
        const duration = secondCellLooksLikeDuration ? cells[1] : "5s";
        const scene = secondCellLooksLikeDuration ? cells[2] || `镜头 ${shot}` : cells[1] || `镜头 ${shot}`;
        const sceneDescription = secondCellLooksLikeDuration ? cells[2] || scene : cells[2] || cells[3] || scene;
        return {
          shot,
          duration,
          scene,
          sceneDescription,
          shotSize: (secondCellLooksLikeDuration ? cells[3] : "") || "中景",
          lighting: (secondCellLooksLikeDuration ? cells[4] : cells[4]) || "电影感光影",
          dialogue: (secondCellLooksLikeDuration ? cells[5] : "") || "+",
          backgroundMusic: "无",
          voiceAndSfx: (secondCellLooksLikeDuration ? cells[6] : cells[3]) || "无",
          cameraMovement: (secondCellLooksLikeDuration ? cells[7] : "") || "缓慢推镜头",
          imagePrompt: (secondCellLooksLikeDuration ? cells[8] : cells[5]) || sceneDescription,
          motionPrompt:
            (secondCellLooksLikeDuration ? cells[9] || cells[7] : cells[6]) ||
            "保持镜头运动自然、连贯，围绕主体动作推进。",
          keyframeUrl: "",
        };
      }

      const raw =
        item && typeof item === "object" && !Array.isArray(item)
          ? (item as Record<string, unknown>)
          : {};
      const shot = normalizeStoryShot(raw.shot || raw.shotNumber || raw.index || raw.id, index);
      const duration = normalizeText(raw.duration || raw.length || raw.time || raw.seconds);
      const scene = normalizeText(raw.scene || raw.location || raw.title || `镜头 ${shot}`);
      const sceneDescription = normalizeText(
        raw.sceneDescription ||
          raw.description ||
          raw.visual ||
          raw.picture ||
          raw.screen ||
          raw.summary,
      );
      const shotSize = normalizeText(
        raw.shotSize || raw.frameSize || raw.shot_type || raw.sceneScale || raw.cameraShot,
      );
      const lighting = normalizeText(
        raw.lighting || raw.light || raw.atmosphere || raw.mood || raw.lightMood,
      );
      const dialogue = normalizeText(
        raw.dialogue || raw.dialog || raw.narration || raw.voiceover || raw.monologue || raw.line,
      );
      const cameraMovement = normalizeText(
        raw.cameraMovement || raw.cameraMove || raw.movement || raw.lensMovement || raw.motion,
      );
      const imagePrompt = normalizeText(
        raw.imagePrompt ||
          raw.image_generation_prompt ||
          raw.image ||
          raw.keyframePrompt ||
          raw.keyFramePrompt,
      );
      const motionPrompt = normalizeText(
        raw.motionPrompt ||
          raw.videoPrompt ||
          raw.video_motion_prompt ||
          raw.motion ||
          raw.cameraMovement,
      );
      const backgroundMusic = normalizeText(
        raw.backgroundMusic || raw.music || raw.bgm || raw.soundtrack,
      );
      const voiceAndSfx = normalizeText(
        raw.voiceAndSfx || raw.voice || raw.sound || raw.audio || raw.sfx,
      );

      if (
        !scene &&
        !sceneDescription &&
        !imagePrompt &&
        !motionPrompt &&
        !backgroundMusic &&
        !voiceAndSfx
      ) {
        return null;
      }

      return {
        shot,
        duration: duration || "5s",
        scene,
        sceneDescription: sceneDescription || imagePrompt || scene || `镜头 ${shot}`,
        shotSize: shotSize || "中景",
        lighting: lighting || "电影感光影",
        dialogue: dialogue || "+",
        backgroundMusic: backgroundMusic || "无",
        voiceAndSfx: voiceAndSfx || "无",
        cameraMovement: cameraMovement || "缓慢推镜头",
        imagePrompt: imagePrompt || sceneDescription || scene || `镜头 ${shot}关键帧`,
        motionPrompt: motionPrompt || cameraMovement || "保持镜头运动自然、连贯，围绕主体动作推进。",
        keyframeUrl: "",
      };
    })
    .filter(
      (
        item,
      ): item is NormalizedStoryScriptRow => Boolean(item),
    )
    .slice(0, 18);
}

const storyExpansionPhases = [
  {
    description: "建立镜头，交代环境、人物位置和当前压力",
    cameraMovement: "缓慢推镜头",
    imagePrompt: "广角或中景建立画面，环境信息清晰，主体位置明确",
    lighting: "环境主光明确，氛围基调清晰",
    motionPrompt: "镜头缓慢推进或轻微横移，建立空间关系",
    shotSize: "中景",
  },
  {
    description: "动作推进，突出人物的关键行为或剧情转折",
    cameraMovement: "跟随主体动作推进",
    imagePrompt: "中景表现人物动作，服装、道具和光影保持连续",
    lighting: "主体光影连续，动作区域清晰",
    motionPrompt: "跟随主体动作推进，节奏自然连贯",
    shotSize: "中景",
  },
  {
    description: "情绪反应，用近景或特写强化人物心理和冲突",
    cameraMovement: "轻微推近或稳定特写",
    imagePrompt: "近景或特写，突出表情、眼神、手部或关键道具细节",
    lighting: "面部光线突出，情绪反差增强",
    motionPrompt: "轻微推近或稳定特写，强调情绪变化",
    shotSize: "特写",
  },
  {
    description: "关系与氛围，表现周围人物、环境反馈或声画张力",
    cameraMovement: "横移扫过环境或切换视角",
    imagePrompt: "加入环境反馈、群像反应或空间层次，电影感光影",
    lighting: "场景氛围增强，空间层次明显",
    motionPrompt: "镜头扫过环境或切换视角，保持节奏递进",
    shotSize: "全景",
  },
  {
    description: "转场收束，为下一镜头保留动作、视线或情绪钩子",
    cameraMovement: "顺势后移、上摇或转场",
    imagePrompt: "构图留出转场方向，动作或视线指向下一段剧情",
    lighting: "光影引导视线并保留转场方向",
    motionPrompt: "镜头后移、上摇或顺势转场，衔接下一镜头",
    shotSize: "大远景",
  },
];

function expandStoryScriptRowsToTarget(
  rows: NormalizedStoryScriptRow[],
  targetCount = 18,
): NormalizedStoryScriptRow[] {
  if (rows.length === 0) return [];
  if (rows.length >= targetCount) {
    return rows.slice(0, targetCount).map((row, index) => ({
      ...row,
      shot: String(index + 1).padStart(2, "0"),
    }));
  }

  const baseCount = Math.floor(targetCount / rows.length);
  const extraCount = targetCount % rows.length;
  const expanded: NormalizedStoryScriptRow[] = [];

  rows.forEach((row, rowIndex) => {
    const segmentCount = baseCount + (rowIndex < extraCount ? 1 : 0);
    for (let segmentIndex = 0; segmentIndex < segmentCount; segmentIndex += 1) {
      const phase =
        storyExpansionPhases[
          Math.min(
            storyExpansionPhases.length - 1,
            Math.floor((segmentIndex / Math.max(segmentCount, 1)) * storyExpansionPhases.length),
          )
        ];
      expanded.push({
        ...row,
        shot: String(expanded.length + 1).padStart(2, "0"),
        sceneDescription: `${phase.description}：${row.sceneDescription}`,
        shotSize: row.shotSize || phase.shotSize,
        lighting: row.lighting || phase.lighting,
        cameraMovement: row.cameraMovement || phase.cameraMovement,
        imagePrompt: `${row.imagePrompt}；${phase.imagePrompt}`,
        motionPrompt: `${phase.motionPrompt}；${row.motionPrompt}`,
      });
    }
  });

  return expanded.slice(0, targetCount);
}

function normalizeCharacters(value: unknown) {
  if (!Array.isArray(value)) return [];
  return value
    .map((item) => {
      const raw =
        item && typeof item === "object" && !Array.isArray(item)
          ? (item as Record<string, unknown>)
          : {};
      const name = normalizeText(raw.name || raw.title);
      const description = normalizeText(raw.description || raw.summary);
      const prompt = normalizeText(raw.prompt);
      if (!name && !description && !prompt) return null;
      return {
        name: name || "未命名角色",
        description,
        prompt,
        look: normalizeText(raw.look) || undefined,
        wardrobe: normalizeText(raw.wardrobe) || undefined,
        signatureAction: normalizeText(raw.signatureAction) || undefined,
        traits: normalizeStringList(raw.traits, 6),
      };
    })
    .filter((item): item is NonNullable<typeof item> => Boolean(item))
    .slice(0, 8);
}

function normalizeScenes(value: unknown) {
  if (!Array.isArray(value)) return [];
  return value
    .map((item) => {
      const raw =
        item && typeof item === "object" && !Array.isArray(item)
          ? (item as Record<string, unknown>)
          : {};
      const name = normalizeText(raw.name || raw.title);
      const description = normalizeText(raw.description || raw.summary);
      const prompt = normalizeText(raw.prompt);
      if (!name && !description && !prompt) return null;
      return {
        name: name || "未命名场景",
        description,
        prompt,
        mood: normalizeText(raw.mood) || undefined,
        lighting: normalizeText(raw.lighting) || undefined,
        props: normalizeStringList(raw.props, 6),
      };
    })
    .filter((item): item is NonNullable<typeof item> => Boolean(item))
    .slice(0, 8);
}

function buildVideoAnalysisPrompt(params: {
  nodeTitle?: string;
  prompt: string;
  frameCount: number;
  imageCount: number;
  videoCount: number;
}) {
  return [
    "你是视频画布工作流里的视频分析节点，需要为后续分镜和生成节点提供结构化输出。",
    params.nodeTitle ? `当前节点：${params.nodeTitle}` : "",
    `上游参考图数量：${params.imageCount}`,
    `上游视频数量：${params.videoCount}`,
    `已提供按时间顺序抽取的视频关键帧数量：${params.frameCount}`,
    params.prompt ? `上游脚本/说明：\n${params.prompt}` : "上游没有额外脚本，请重点依据参考画面分析。",
    "",
    "请只输出 JSON，不要输出 markdown，不要输出解释。",
    "JSON 结构如下：",
    `{
  "summary": "一句话总结当前视频/镜头风格，不超过120字",
  "highlights": ["3-5条镜头分析要点"],
  "analysisRows": [
    {
      "shot": "1",
      "startTime": "0",
      "endTime": "5",
      "duration": "5",
      "sceneDescription": "画面描述，说明主体、环境、动作、构图和色调",
      "narrative": "叙事内容，说明这一镜头承担的剧情/情绪/信息作用",
      "shotSize": "景别，例如中景至近景",
      "cameraAngle": "摄影机角度，例如平视",
      "cameraMovement": "摄影机运动，例如缓慢推镜头 (Slow Zoom In)",
      "focusDepth": "焦距与景深，例如浅景深",
      "lighting": "光线，例如冷调低沉照明，屏幕光作为侧逆光",
      "backgroundMusic": "这一段适合的背景音乐氛围",
      "voiceAndSfx": "人声/环境声/音效建议，没有就写无",
      "imagePrompt": "后续生成关键帧时可直接使用的英文图像提示词",
      "motionPrompt": "后续生成视频时可直接使用的英文运动提示词",
      "keyframeIndex": 1
    }
  ],
  "shotIdeas": [
    { "title": "镜头 1", "description": "一句话描述镜头目标、景别或动作" }
  ],
  "visualMotifs": ["2-4个可复用的视觉母题"],
  "recommendedNextSteps": ["2-4条建议的下游动作"]
}`,
    "analysisRows 请按镜头时间顺序输出；短视频可以输出 1 行，复杂视频输出 2-6 行。",
    "每行必须覆盖镜号、开始时间、结束时间、时长、画面描述、叙事内容、景别、摄影机角度、摄影机运动、焦距与景深、光线、背景音乐、人声/音效、图像生成提示词、视频运动提示词和关键帧。",
    "imagePrompt 和 motionPrompt 优先使用英文；其他字段使用中文；keyframeIndex 使用 1 开始的序号，对应你看到的关键帧顺序。",
    "如果没有真实视频帧，只能依据参考图和脚本推断，也要正常输出，但措辞要稳健。",
  ]
    .filter(Boolean)
    .join("\n");
}

async function getVideoDurationSeconds(videoUrl: string) {
  try {
    const { stdout } = await execFileAsync(
      "ffprobe",
      [
        "-v",
        "error",
        "-show_entries",
        "format=duration",
        "-of",
        "default=noprint_wrappers=1:nokey=1",
        videoUrl,
      ],
      {
        timeout: VIDEO_ANALYSIS_TIMEOUT_MS,
        maxBuffer: 1024 * 1024,
      },
    );
    const duration = Number.parseFloat(String(stdout || "").trim());
    return Number.isFinite(duration) && duration > 0 ? duration : null;
  } catch {
    return null;
  }
}

function buildVideoFrameTimestamps(durationSeconds: number | null, frameCount: number) {
  const safeCount = Math.max(1, frameCount);
  if (!durationSeconds || !Number.isFinite(durationSeconds) || durationSeconds <= 0.5) {
    return Array.from({ length: safeCount }).map((_, index) =>
      Number((0.3 + index * 0.45).toFixed(2)),
    );
  }
  const start = Math.min(0.35, durationSeconds * 0.12);
  const end = Math.max(start + 0.6, durationSeconds * 0.88);
  if (safeCount === 1) return [Number(((start + end) / 2).toFixed(2))];
  const step = (end - start) / (safeCount - 1);
  return Array.from({ length: safeCount }).map((_, index) =>
    Number((start + step * index).toFixed(2)),
  );
}

async function downscaleVideoFrameForVision(inputBuffer: Buffer) {
  return sharp(inputBuffer, { failOn: "none" })
    .rotate()
    .resize({
      width: VIDEO_ANALYSIS_MAX_SIDE,
      height: VIDEO_ANALYSIS_MAX_SIDE,
      fit: "inside",
      withoutEnlargement: true,
    })
    .jpeg({ quality: 82, mozjpeg: true })
    .toBuffer();
}

async function extractVideoFrameDataUrls(videoUrl: string, frameCount = VIDEO_ANALYSIS_FRAME_COUNT) {
  const tempDir = await mkdtemp(path.join(os.tmpdir(), "canvas-video-analysis-"));
  try {
    const durationSeconds = await getVideoDurationSeconds(videoUrl);
    const timestamps = buildVideoFrameTimestamps(durationSeconds, frameCount);
    const frames = await Promise.all(
      timestamps.map(async (timestamp, index) => {
        const outputPath = path.join(tempDir, `frame-${index + 1}.jpg`);
        await execFileAsync(
          "ffmpeg",
          [
            "-hide_banner",
            "-loglevel",
            "error",
            "-ss",
            String(timestamp),
            "-i",
            videoUrl,
            "-frames:v",
            "1",
            "-q:v",
            "4",
            "-y",
            outputPath,
          ],
          {
            timeout: VIDEO_ANALYSIS_TIMEOUT_MS,
            maxBuffer: 2 * 1024 * 1024,
          },
        );
        const buffer = await readFile(outputPath);
        const processed = await downscaleVideoFrameForVision(buffer);
        return `data:image/jpeg;base64,${processed.toString("base64")}`;
      }),
    );
    return frames.filter(Boolean);
  } catch (error) {
    console.warn("[Canvas Workflow Analyze] extract video frames failed:", {
      message: error instanceof Error ? error.message : String(error),
      videoUrl,
    });
    return [];
  } finally {
    await rm(tempDir, { force: true, recursive: true }).catch(() => undefined);
  }
}

function buildCharacterScenePrompt(params: {
  nodeTitle?: string;
  prompt: string;
  imageCount: number;
}) {
  return [
    "你是视频画布工作流里的角色场景提取节点，需要从脚本、设定和参考图里提取角色与场景资产。",
    params.nodeTitle ? `当前节点：${params.nodeTitle}` : "",
    `参考图数量：${params.imageCount}`,
    params.prompt ? `输入内容：\n${params.prompt}` : "没有额外脚本，请根据参考图提取角色和场景线索。",
    "",
    "请只输出 JSON，不要输出 markdown，不要输出解释。",
    "JSON 结构如下：",
    `{
  "summary": "一句话总结当前故事世界与资产线索",
  "characters": [
    {
      "name": "角色名",
      "description": "角色外观与身份简介",
      "look": "外貌特征",
      "wardrobe": "服装/道具",
      "traits": ["性格关键词"],
      "signatureAction": "代表动作",
      "prompt": "可直接用于生成角色图/角色视频的提示词"
    }
  ],
  "scenes": [
    {
      "name": "场景名",
      "description": "空间、布景和氛围简介",
      "mood": "情绪与气氛",
      "lighting": "布光特征",
      "props": ["核心道具"],
      "prompt": "可直接用于生成场景图/场景视频的提示词"
    }
  ],
  "relationships": ["角色与场景或角色之间的关系"],
  "recommendedNextSteps": ["建议连接的后续节点动作"]
}`,
    "角色和场景都要尽量具体，避免空泛形容词。",
  ]
    .filter(Boolean)
    .join("\n");
}

function buildAgentPlanPrompt(params: {
  nodeTitle?: string;
  prompt: string;
  imageCount: number;
  videoCount: number;
}) {
  return [
    "你是视频画布里的自定义代理节点，需要把当前创作目标拆成可执行的工作流计划。",
    params.nodeTitle ? `当前节点：${params.nodeTitle}` : "",
    `参考图数量：${params.imageCount}`,
    `视频数量：${params.videoCount}`,
    params.prompt ? `上游需求：\n${params.prompt}` : "没有额外脚本，请基于当前上下文给出稳妥计划。",
    "",
    "请只输出 JSON，不要输出 markdown，不要输出解释。",
    "JSON 结构如下：",
    `{
  "summary": "一句话说明这个代理链的目标",
  "agentPlan": ["3-6个执行步骤"],
  "deliverables": ["2-4个预期交付物"],
  "recommendedNodes": ["建议继续连接的节点类型或能力名"]
}`,
    "步骤要偏工作流视角，而不是泛泛而谈。",
  ]
    .filter(Boolean)
    .join("\n");
}

function buildStoryScriptPrompt(params: {
  nodeTitle?: string;
  prompt: string;
  imageCount: number;
  videoCount: number;
}) {
  return [
    "你是视频画布里的故事脚本生成节点，需要把用户输入、参考图和参考视频整理成可直接用于后续分镜/生图/生视频的结构化脚本表。",
    params.nodeTitle ? `当前节点：${params.nodeTitle}` : "",
    `参考图数量：${params.imageCount}`,
    `视频数量：${params.videoCount}`,
    params.prompt ? `输入内容：\n${params.prompt}` : "没有额外脚本，请生成一个节奏清晰的短视频脚本。",
    "",
    "请只输出 JSON，不要输出 markdown，不要输出解释。",
    "JSON 结构如下：",
    `{
  "summary": "一句话概括故事主题、情绪和视频方向，不超过120字",
  "analysisRows": [
    {
      "shot": "01",
      "duration": "5s",
      "scene": "场景名或地点，如 盛唐·金銮殿·日",
      "sceneDescription": "画面内容、人物动作和剧情推进，不要重复 scene 字段",
      "shotSize": "景别，如中景、特写、全景、大远景",
      "lighting": "光影氛围，如暖金主光带阴影，气氛紧张压抑",
      "dialogue": "对白或旁白，没有就写 +",
      "backgroundMusic": "背景音乐或情绪氛围，没有就写无",
      "voiceAndSfx": "环境声、动作声或音效，没有就写无",
      "cameraMovement": "运镜，如缓慢推镜头、固定、横移、上摇、环绕轨道跟拍",
      "imagePrompt": "后续生成该镜头关键帧时可直接使用的中文图像提示词",
      "motionPrompt": "后续生成视频时可直接使用的中文视频运动提示词"
    }
  ],
  "assetCandidates": {
    "style": "全局视觉风格，如 盛唐·国风二次元",
    "characters": ["需要生成的角色资产名"],
    "scenes": ["需要生成的场景资产名"],
    "props": ["需要生成的道具资产名"]
  },
  "shotIdeas": [
    { "title": "镜头 1", "description": "一句话描述镜头目标、景别或动作" }
  ],
  "recommendedNextSteps": ["2-4条建议的下游动作"]
}`,
    "analysisRows 必须直接输出 18 行，按镜头时间顺序编号 01-18；每行默认 5s 或 6s，剧情高潮可用 6s。",
    "每行要覆盖竞品脚本表同等信息：镜号、时长、画面描述、景别、光影氛围、对白·旁白、音效、运镜、最终图像提示词和视频运动提示词。",
    "画面描述中用 @角色名 或 @场景/道具名 标出可复用资产，例如 @沈昭昭、@金銮殿、@侍卫长刀。",
    "imagePrompt 要像可直接生成分镜图的中文提示词，包含主体、环境、构图、光影、风格与一致性要求；motionPrompt 要按起始状态、动作过程、结束状态、音效组织。",
    "assetCandidates 根据 18 镜头抽取 3-6 个角色、2-5 个场景和 1-4 个道具，方便后续准备资产。",
    "每行要像专业视频画布脚本表：场景具体、画面可生成、声音可执行、镜头节奏明确，不要只写剧情摘要。",
  ]
    .filter(Boolean)
    .join("\n");
}

export async function POST(request: Request) {
  try {
    const body = await request.json().catch(() => ({}));
    const parsed = requestSchema.safeParse(body);
    if (!parsed.success) {
      return NextResponse.json(
        { success: false, error: "请求参数不合法" },
        { status: 400 }
      );
    }

    const prompt = normalizeText(parsed.data.prompt);
    const imageUrls = sanitizeUrls(parsed.data.imageUrls, 4);
    const videoUrls = sanitizeUrls(parsed.data.videoUrls, 4);
    const extractedVideoFrames =
      parsed.data.mode === "video-analysis" && videoUrls.length > 0
        ? await extractVideoFrameDataUrls(videoUrls[0], VIDEO_ANALYSIS_FRAME_COUNT)
        : [];
    const preferredModelRef = normalizeText(parsed.data.preferredModelRef || parsed.data.model);
    if (!prompt && imageUrls.length === 0 && videoUrls.length === 0) {
      return NextResponse.json(
        { success: false, error: "请至少提供脚本、图片或视频上下文" },
        { status: 400 }
      );
    }

    const session = await auth();
    if (!session?.user?.id) {
      return NextResponse.json({ success: false, error: "未登录" }, { status: 401 });
    }

    const [allModels, agentModel] = await Promise.all([
      getEnabledAiModelsWithProvider(false),
      getAgentAiModelWithProvider(),
    ]);
    const compatibleModels = allModels.filter(
      (item) => !item.model.isSubModel && item.model.isEnabled
    );

    const preferredModel =
      preferredModelRef
        ? compatibleModels.find(
            (item) =>
              item.model.id === preferredModelRef ||
              item.model.modelId === preferredModelRef,
          )
        : null;

    const hasVisionInputs = imageUrls.length > 0 || extractedVideoFrames.length > 0;

    let selectedModel =
      preferredModel && (!hasVisionInputs || supportsVision(preferredModel.model))
        ? preferredModel
        : hasVisionInputs &&
            agentModel &&
            supportsVision(agentModel.model)
          ? agentModel
          : hasVisionInputs
            ? compatibleModels.find((item) => supportsVision(item.model))
            : agentModel || compatibleModels[0];

    if (!selectedModel) {
      selectedModel = compatibleModels[0];
    }

    if (!selectedModel) {
      return NextResponse.json(
        { success: false, error: "当前站点没有可用的分析模型" },
        { status: 500 }
      );
    }

    const useVision = hasVisionInputs && supportsVision(selectedModel.model);
    const codingModelId = getAiModelAgentCodingModelId(selectedModel.model);
    const model = await getLanguageModelById(selectedModel.model.id, codingModelId);
    if (!model) {
      return NextResponse.json(
        { success: false, error: "分析模型初始化失败" },
        { status: 500 }
      );
    }

    const promptText =
      parsed.data.mode === "video-analysis"
        ? buildVideoAnalysisPrompt({
            frameCount: extractedVideoFrames.length,
            nodeTitle: normalizeText(parsed.data.nodeTitle),
            prompt,
            imageCount: imageUrls.length,
            videoCount: videoUrls.length,
          })
        : parsed.data.mode === "character-scene-extraction"
          ? buildCharacterScenePrompt({
              nodeTitle: normalizeText(parsed.data.nodeTitle),
              prompt,
              imageCount: imageUrls.length,
            })
          : parsed.data.mode === "story-script"
            ? buildStoryScriptPrompt({
                nodeTitle: normalizeText(parsed.data.nodeTitle),
                prompt,
                imageCount: imageUrls.length,
                videoCount: videoUrls.length,
              })
            : buildAgentPlanPrompt({
              nodeTitle: normalizeText(parsed.data.nodeTitle),
              prompt,
              imageCount: imageUrls.length,
              videoCount: videoUrls.length,
            });

    const inlinedImageReferences = useVision
      ? (
          await inlineVisionImages(imageUrls, {
            maxImages: 4,
            maxSide: 960,
            timeoutMs: 12000,
            retries: 1,
            loggerPrefix: "[Canvas Workflow Analyze]",
          })
        ).inlineDataUrls
      : [];
    const referenceImages = useVision
      ? [...inlinedImageReferences, ...extractedVideoFrames].slice(0, 6)
      : [];

    const { text } = await generateText({
      model,
      messages: [
        {
          role: "user",
          content: [
            { type: "text", text: promptText },
            ...referenceImages.map((image) => ({ type: "image" as const, image })),
          ],
        },
      ],
      temperature: 0.2,
      maxTokens: parsed.data.mode === "story-script" ? 4200 : parsed.data.mode === "video-analysis" ? 2200 : 1600,
    } as any);

    const parsedJson = extractJsonBlock<Record<string, unknown>>(text) || {};
    const summary =
      normalizeText(parsedJson.summary) ||
      normalizeText(text).slice(0, 160);

    if (parsed.data.mode === "video-analysis") {
      const analysisRows = normalizeVideoAnalysisRows(
        parsedJson.analysisRows,
        extractedVideoFrames,
      );
      const shotIdeas =
        normalizeShotIdeas(parsedJson.shotIdeas).length > 0
          ? normalizeShotIdeas(parsedJson.shotIdeas)
          : analysisRows.map((row, index) => ({
              title: `镜头 ${index + 1}`,
              description: row.sceneDescription,
            }));
      return NextResponse.json({
        success: true,
        data: {
          summary,
          analysisRows,
          framePreviews: extractedVideoFrames,
          highlights: normalizeStringList(parsedJson.highlights, 6),
          shotIdeas,
          visualMotifs: normalizeStringList(parsedJson.visualMotifs, 6),
          recommendedNextSteps: normalizeStringList(parsedJson.recommendedNextSteps, 6),
          model: selectedModel.model.displayName,
          raw: text,
        },
      });
    }

    if (parsed.data.mode === "character-scene-extraction") {
      return NextResponse.json({
        success: true,
        data: {
          summary,
          characters: normalizeCharacters(parsedJson.characters),
          scenes: normalizeScenes(parsedJson.scenes),
          relationships: normalizeStringList(parsedJson.relationships, 8),
          recommendedNextSteps: normalizeStringList(parsedJson.recommendedNextSteps, 6),
          model: selectedModel.model.displayName,
          raw: text,
        },
      });
    }

    if (parsed.data.mode === "story-script") {
      const analysisRows = expandStoryScriptRowsToTarget(normalizeStoryScriptRows(
        parsedJson.analysisRows || parsedJson.storyRows || parsedJson.rows || parsedJson.shots,
      ));
      return NextResponse.json({
        success: true,
        data: {
          summary,
          analysisRows,
          shotIdeas:
            normalizeShotIdeas(parsedJson.shotIdeas).length > 0
              ? normalizeShotIdeas(parsedJson.shotIdeas)
              : analysisRows.map((row, index) => ({
                  title: `镜头 ${index + 1}`,
                  description: row.sceneDescription,
                })),
          recommendedNextSteps: normalizeStringList(parsedJson.recommendedNextSteps, 6),
          model: selectedModel.model.displayName,
          raw: text,
        },
      });
    }

    return NextResponse.json({
      success: true,
      data: {
        summary,
        agentPlan: normalizeStringList(parsedJson.agentPlan, 8),
        deliverables: normalizeStringList(parsedJson.deliverables, 6),
        recommendedNodes: normalizeStringList(parsedJson.recommendedNodes, 6),
        model: selectedModel.model.displayName,
        raw: text,
      },
    });
  } catch (error) {
    console.error("[Canvas Workflow Analyze] Error:", error);
    return NextResponse.json(
      {
        success: false,
        error: error instanceof Error ? error.message : "工作流分析失败",
      },
      { status: 500 }
    );
  }
}