import { randomUUID } from "node:crypto";
import { removeBackground } from "@snapotter/ai";
import { getBundleForTool, TOOL_BUNDLE_MAP } from "@snapotter/shared";
import type { FastifyInstance, FastifyReply, FastifyRequest } from "fastify";
import sharp from "sharp";
import { z } from "zod";
import { registerAiJobHandler } from "../../jobs/ai-handlers.js";
import { enqueueToolJob } from "../../jobs/enqueue.js";
import { autoOrient } from "../../lib/auto-orient.js";
import { compositeOnColor, createGradientBackground } from "../../lib/bg-effects.js";
import { formatZodErrors, stripInternalPaths } from "../../lib/errors.js";
import { isToolInstalled } from "../../lib/feature-status.js";
import { validateImageBuffer } from "../../lib/file-validation.js";
import { decodeToSharpCompat, needsCliDecode } from "../../lib/format-decoders.js";
import { decodeHeic } from "../../lib/heic-converter.js";
import { receiveUpload } from "../../lib/upload-stream.js";

const HEX_RE = /^#[0-9a-fA-F]{6}$/;

const settingsSchema = z.object({
  backgroundType: z.enum(["color", "gradient"]).default("color"),
  color: z.string().regex(HEX_RE).default("#ffffff"),
  gradientColor1: z.string().regex(HEX_RE).optional(),
  gradientColor2: z.string().regex(HEX_RE).optional(),
  gradientAngle: z.number().int().min(0).max(360).default(180),
  feather: z.number().int().min(0).max(20).default(0),
  format: z.enum(["png", "webp"]).default("png"),
});

/**
 * Soften the alpha edges of a subject PNG by blurring its alpha channel.
 * Keeps RGB intact; only the transparency boundary gets smoothed.
 */
async function featherEdges(subjectBuffer: Buffer, radius: number): Promise<Buffer> {
  // Read the subject as raw RGBA plus a separately-blurred copy of its alpha,
  // then overwrite the alpha channel in place. joinChannel does not reliably
  // re-tag the merged channel as alpha, so we splice the raw bytes directly.
  const { data: rgba, info } = await sharp(subjectBuffer)
    .ensureAlpha()
    .raw()
    .toBuffer({ resolveWithObject: true });
  const { data: blurredAlpha } = await sharp(subjectBuffer)
    .extractChannel(3)
    .blur(radius)
    .raw()
    .toBuffer({ resolveWithObject: true });
  for (let p = 3, a = 0; p < rgba.length; p += 4, a++) {
    rgba[p] = blurredAlpha[a];
  }
  return sharp(rgba, { raw: { width: info.width, height: info.height, channels: 4 } })
    .png()
    .toBuffer();
}

// -- AI job handler (runs inside the BullMQ worker) --
registerAiJobHandler("background-replace", async (input, data, ctx) => {
  const settings = settingsSchema.parse(data.settings);

  ctx.report(5, "Removing background");

  const subjectPng = await removeBackground(input, ctx.scratchDir, {}, (percent, stage) => {
    // Scale rembg progress into 5..80 range
    const scaled = 5 + Math.round(percent * 0.75);
    ctx.report(Math.min(scaled, 80), stage);
  });

  // Feather alpha edges before compositing
  let subject = subjectPng;
  if (settings.feather > 0) {
    ctx.report(82, "Feathering edges");
    subject = await featherEdges(subjectPng, settings.feather);
  }

  ctx.report(85, "Compositing background");

  let composited: Buffer;
  if (settings.backgroundType === "gradient") {
    const meta = await sharp(subject).metadata();
    if (!meta.width || !meta.height) throw new Error("Cannot read subject dimensions");
    const gradBg = await createGradientBackground(
      meta.width,
      meta.height,
      settings.gradientColor1 ?? "#ffffff",
      settings.gradientColor2 ?? "#000000",
      settings.gradientAngle,
    );
    composited = await sharp(gradBg)
      .composite([{ input: subject, blend: "over" }])
      .png()
      .toBuffer();
  } else {
    composited = await compositeOnColor(subject, settings.color);
  }

  // Encode to requested output format
  const fmt = settings.format;
  const result =
    fmt === "webp" ? await sharp(composited).webp({ lossless: true }).toBuffer() : composited;

  const base = data.filename.replace(/\.[^.]+$/, "");
  const outName = `${base}_bg.${fmt}`;

  return {
    buffer: result,
    filename: outName,
    contentType: fmt === "webp" ? "image/webp" : "image/png",
  };
});

export function registerBackgroundReplace(app: FastifyInstance) {
  app.post(
    "/api/v1/tools/background-replace",
    async (request: FastifyRequest, reply: FastifyReply) => {
      const toolId = "background-replace";
      if (!isToolInstalled(toolId)) {
        const bundle = getBundleForTool(toolId);
        return reply.status(501).send({
          error: "Feature not installed",
          code: "FEATURE_NOT_INSTALLED",
          feature: TOOL_BUNDLE_MAP[toolId],
          featureName: bundle?.name ?? toolId,
          estimatedSize: bundle?.estimatedSize ?? "unknown",
        });
      }

      const jobId = randomUUID();
      let fileBuffer: Buffer | null = null;
      let filename = "image";
      let settingsRaw: string | null = null;
      let clientJobId: string | null = null;
      let inputKey: string | null = null;

      try {
        const parts = request.parts();
        for await (const part of parts) {
          if (part.type === "file") {
            const upload = await receiveUpload(part, jobId);
            inputKey = upload.key;
            filename = upload.filename;
          } else if (part.fieldname === "settings") {
            settingsRaw = part.value as string;
          } else if (part.fieldname === "clientJobId") {
            const raw = part.value as string;
            if (/^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i.test(raw)) {
              clientJobId = raw;
            }
          }
        }
      } catch (err) {
        return reply.status(400).send({
          error: "Failed to parse multipart request",
          details: stripInternalPaths(err instanceof Error ? err.message : String(err)),
        });
      }

      if (!inputKey) {
        return reply.status(400).send({ error: "No image file provided" });
      }

      const { getObjectBuffer, putObject } = await import("../../lib/object-storage.js");
      fileBuffer = await getObjectBuffer(inputKey);

      if (!fileBuffer || fileBuffer.length === 0) {
        return reply.status(400).send({ error: "No image file provided" });
      }

      const validation = await validateImageBuffer(fileBuffer, filename);
      if (!validation.valid) {
        return reply.status(400).send({ error: `Invalid image: ${validation.reason}` });
      }

      let settings: z.infer<typeof settingsSchema>;
      try {
        const parsed = settingsRaw ? JSON.parse(settingsRaw) : {};
        const result = settingsSchema.safeParse(parsed);
        if (!result.success) {
          return reply
            .status(400)
            .send({ error: "Invalid settings", details: formatZodErrors(result.error.issues) });
        }
        settings = result.data;
      } catch {
        return reply.status(400).send({ error: "Settings must be valid JSON" });
      }

      try {
        if (validation.format === "heif") {
          fileBuffer = await decodeHeic(fileBuffer);
          const ext = filename.match(/\.[^.]+$/)?.[0];
          if (ext) filename = `${filename.slice(0, -ext.length)}.png`;
        }
        if (needsCliDecode(validation.format)) {
          fileBuffer = await decodeToSharpCompat(fileBuffer, validation.format);
          const ext = filename.match(/\.[^.]+$/)?.[0];
          if (ext) filename = `${filename.slice(0, -ext.length)}.png`;
        }
        fileBuffer = await autoOrient(fileBuffer);
      } catch (err) {
        request.log.error({ err, toolId }, "Input decoding failed");
        return reply.status(422).send({
          error: "Processing failed",
          details: stripInternalPaths(err instanceof Error ? err.message : "Unknown error"),
        });
      }

      const decodedKey = `uploads/${jobId}/${filename}`;
      if (decodedKey !== inputKey) {
        await putObject(decodedKey, fileBuffer);
        inputKey = decodedKey;
      } else {
        await putObject(inputKey, fileBuffer);
      }

      const progressJobId = clientJobId || jobId;

      await enqueueToolJob({
        jobId,
        toolId,
        userId: null,
        pool: "ai",
        inputRefs: [inputKey],
        filename,
        settings,
        clientJobId: clientJobId ?? undefined,
        kind: "ai-tool",
      });

      return reply.status(202).send({ jobId: progressJobId, async: true });
    },
  );
}