Remove nsfwjs

Signed-off-by: eternal-flame-AD <yume@yumechi.jp>

package.json

@@ -51,18 +51,18 @@
 		"lodash": "4.17.21"
 	},
 	"dependencies": {
+		"cross-env": "7.0.3",
 		"cssnano": "6.1.2",
+		"esbuild": "0.24.0",
 		"execa": "9.5.1",
 		"fast-glob": "3.3.2",
+		"glob": "11.0.0",
 		"ignore-walk": "6.0.5",
 		"js-yaml": "4.1.0",
 		"postcss": "8.4.49",
 		"tar": "6.2.1",
 		"terser": "5.36.0",
-		"typescript": "5.6.3",
-		"esbuild": "0.24.0",
-		"glob": "11.0.0",
-		"cross-env": "7.0.3"
+		"typescript": "5.6.3"
 	},
 	"devDependencies": {
 		"@misskey-dev/eslint-plugin": "2.0.3",
@@ -74,8 +74,5 @@
 		"globals": "15.12.0",
 		"ncp": "2.0.0",
 		"start-server-and-test": "2.0.8"
-	},
-	"optionalDependencies": {
-		"@tensorflow/tfjs-core": "4.4.0"
 	}
 }

packages/backend/package.json

@@ -48,8 +48,6 @@
 		"@swc/core-win32-arm64-msvc": "1.3.56",
 		"@swc/core-win32-ia32-msvc": "1.3.56",
 		"@swc/core-win32-x64-msvc": "1.3.56",
-		"@tensorflow/tfjs": "4.4.0",
-		"@tensorflow/tfjs-node": "4.4.0",
 		"bufferutil": "4.0.7",
 		"slacc-android-arm-eabi": "0.0.10",
 		"slacc-android-arm64": "0.0.10",
@@ -146,7 +144,6 @@
 		"nested-property": "4.0.0",
 		"node-fetch": "3.3.2",
 		"nodemailer": "6.9.16",
-		"nsfwjs": "2.4.2",
 		"oauth": "0.10.0",
 		"oauth2orize": "1.12.0",
 		"oauth2orize-pkce": "0.1.2",

packages/backend/src/core/AiService.ts

@@ -1,72 +0,0 @@
-/*
- * SPDX-FileCopyrightText: syuilo and misskey-project
- * SPDX-License-Identifier: AGPL-3.0-only
- */
-
-import * as fs from 'node:fs';
-import { fileURLToPath } from 'node:url';
-import { dirname } from 'node:path';
-import { Injectable } from '@nestjs/common';
-import * as nsfw from 'nsfwjs';
-import si from 'systeminformation';
-import { Mutex } from 'async-mutex';
-import { bindThis } from '@/decorators.js';
-
-const _filename = fileURLToPath(import.meta.url);
-const _dirname = dirname(_filename);
-
-const REQUIRED_CPU_FLAGS = ['avx2', 'fma'];
-let isSupportedCpu: undefined | boolean = undefined;
-
-@Injectable()
-export class AiService {
-	private model: nsfw.NSFWJS;
-	private modelLoadMutex: Mutex = new Mutex();
-
-	constructor(
-	) {
-	}
-
-	@bindThis
-	public async detectSensitive(path: string): Promise<nsfw.predictionType[] | null> {
-		try {
-			if (isSupportedCpu === undefined) {
-				const cpuFlags = await this.getCpuFlags();
-				isSupportedCpu = REQUIRED_CPU_FLAGS.every(required => cpuFlags.includes(required));
-			}
-
-			if (!isSupportedCpu) {
-				console.error('This CPU cannot use TensorFlow.');
-				return null;
-			}
-
-			const tf = await import('@tensorflow/tfjs-node');
-
-			if (this.model == null) {
-				await this.modelLoadMutex.runExclusive(async () => {
-					if (this.model == null) {
-						this.model = await nsfw.load(`file://${_dirname}/../../nsfw-model/`, { size: 299 });
-					}
-				});
-			}
-
-			const buffer = await fs.promises.readFile(path);
-			const image = await tf.node.decodeImage(buffer, 3) as any;
-			try {
-				const predictions = await this.model.classify(image);
-				return predictions;
-			} finally {
-				image.dispose();
-			}
-		} catch (err) {
-			console.error(err);
-			return null;
-		}
-	}
-
-	@bindThis
-	private async getCpuFlags(): Promise<string[]> {
-		const str = await si.cpuFlags();
-		return str.split(/\s+/);
-	}
-}

packages/backend/src/core/FileInfoService.ts

@@ -13,11 +13,8 @@ import * as fileType from 'file-type';
 import FFmpeg from 'fluent-ffmpeg';
 import isSvg from 'is-svg';
 import probeImageSize from 'probe-image-size';
-import { type predictionType } from 'nsfwjs';
 import { sharpBmp } from '@misskey-dev/sharp-read-bmp';
 import * as blurhash from 'blurhash';
-import { createTempDir } from '@/misc/create-temp.js';
-import { AiService } from '@/core/AiService.js';
 import { LoggerService } from '@/core/LoggerService.js';
 import type Logger from '@/logger.js';
 import { bindThis } from '@/decorators.js';
@@ -53,7 +50,6 @@ export class FileInfoService {
 	private logger: Logger;
 
 	constructor(
-		private aiService: AiService,
 		private loggerService: LoggerService,
 	) {
 		this.logger = this.loggerService.getLogger('file-info');
@@ -167,102 +163,7 @@ export class FileInfoService {
 
 	@bindThis
 	private async detectSensitivity(source: string, mime: string, sensitiveThreshold: number, sensitiveThresholdForPorn: number, analyzeVideo: boolean): Promise<[sensitive: boolean, porn: boolean]> {
-		let sensitive = false;
-		let porn = false;
-
-		function judgePrediction(result: readonly predictionType[]): [sensitive: boolean, porn: boolean] {
-			let sensitive = false;
-			let porn = false;
-
-			if ((result.find(x => x.className === 'Sexy')?.probability ?? 0) > sensitiveThreshold) sensitive = true;
-			if ((result.find(x => x.className === 'Hentai')?.probability ?? 0) > sensitiveThreshold) sensitive = true;
-			if ((result.find(x => x.className === 'Porn')?.probability ?? 0) > sensitiveThreshold) sensitive = true;
-
-			if ((result.find(x => x.className === 'Porn')?.probability ?? 0) > sensitiveThresholdForPorn) porn = true;
-
-			return [sensitive, porn];
-		}
-
-		if ([
-			'image/jpeg',
-			'image/png',
-			'image/webp',
-		].includes(mime)) {
-			const result = await this.aiService.detectSensitive(source);
-			if (result) {
-				[sensitive, porn] = judgePrediction(result);
-			}
-		} else if (analyzeVideo && (mime === 'image/apng' || mime.startsWith('video/'))) {
-			const [outDir, disposeOutDir] = await createTempDir();
-			try {
-				const command = FFmpeg()
-					.input(source)
-					.inputOptions([
-						'-skip_frame', 'nokey', // 可能ならキーフレームのみを取得してほしいとする（そうなるとは限らない）
-						'-lowres', '3', // 元の画質でデコードする必要はないので 1/8 画質でデコードしてもよいとする（そうなるとは限らない）
-					])
-					.noAudio()
-					.videoFilters([
-						{
-							filter: 'select', // フレームのフィルタリング
-							options: {
-								e: 'eq(pict_type,PICT_TYPE_I)', // I-Frame のみをフィルタする（VP9 とかはデコードしてみないとわからないっぽい）
-							},
-						},
-						{
-							filter: 'blackframe', // 暗いフレームの検出
-							options: {
-								amount: '0', // 暗さに関わらず全てのフレームで測定値を取る
-							},
-						},
-						{
-							filter: 'metadata',
-							options: {
-								mode: 'select', // フレーム選択モード
-								key: 'lavfi.blackframe.pblack', // フレームにおける暗部の百分率（前のフィルタからのメタデータを参照する）
-								value: '50',
-								function: 'less', // 50% 未満のフレームを選択する（50% 以上暗部があるフレームだと誤検知を招くかもしれないので）
-							},
-						},
-						{
-							filter: 'scale',
-							options: {
-								w: 299,
-								h: 299,
-							},
-						},
-					])
-					.format('image2')
-					.output(join(outDir, '%d.png'))
-					.outputOptions(['-vsync', '0']); // 可変フレームレートにすることで穴埋めをさせない
-				const results: ReturnType<typeof judgePrediction>[] = [];
-				let frameIndex = 0;
-				let targetIndex = 0;
-				let nextIndex = 1;
-				for await (const path of this.asyncIterateFrames(outDir, command)) {
-					try {
-						const index = frameIndex++;
-						if (index !== targetIndex) {
-							continue;
-						}
-						targetIndex = nextIndex;
-						nextIndex += index; // fibonacci sequence によってフレーム数制限を掛ける
-						const result = await this.aiService.detectSensitive(path);
-						if (result) {
-							results.push(judgePrediction(result));
-						}
-					} finally {
-						fs.promises.unlink(path);
-					}
-				}
-				sensitive = results.filter(x => x[0]).length >= Math.ceil(results.length * sensitiveThreshold);
-				porn = results.filter(x => x[1]).length >= Math.ceil(results.length * sensitiveThresholdForPorn);
-			} finally {
-				disposeOutDir();
-			}
-		}
-
-		return [sensitive, porn];
+		return [false, false];
 	}
 
 	private async *asyncIterateFrames(cwd: string, command: FFmpeg.FfmpegCommand): AsyncGenerator<string, void> {