feat: interview review module with whisper.cpp ASR + AI analysis + frontend page
New backend module 'interview-review' provides: - Audio upload (50MB limit, MP3/M4A/WAV/AAC/OGG/MP4/WebM) - Text transcript submission - whisper.cpp local ASR integration (tiny + base models) - AI analysis (4-dimension scoring: logic/expression/professionalism/stability) - Speech analysis (filler words detection, pace, duration) - Async processing pipeline with status polling - Graceful fallback to mock ASR when whisper unavailable New frontend page 'pages/review/review.vue' with 3 modes: - List mode: review history with status indicators - Upload mode: audio file upload or text paste - Report mode: score radar, dimension bars, analysis details Docs updated: PROJECT-STATUS.md v4.4, FEATURE-LIST.md v4.2, ROADMAP.md v4.2
This commit is contained in:
@@ -26,6 +26,7 @@ import { ScheduleModule } from './modules/schedule/schedule.module'
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import { TtsModule } from './modules/tts/tts.module'
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import { PricingModule } from './modules/schemas/pricing.module'
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import { ShareModule } from './modules/share/share.module'
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import { InterviewReviewModule } from './modules/interview-review/interview-review.module'
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const MONGODB_URI = process.env.MONGODB_URI || 'mongodb://localhost:27017/zhiyin'
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@@ -60,6 +61,7 @@ const MONGODB_URI = process.env.MONGODB_URI || 'mongodb://localhost:27017/zhiyin
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TtsModule,
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PricingModule,
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ShareModule,
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InterviewReviewModule,
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],
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providers: [
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JwtStrategy,
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@@ -0,0 +1,218 @@
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import { Injectable, Logger } from '@nestjs/common'
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import { execSync } from 'child_process'
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import * as path from 'path'
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import * as fs from 'fs'
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export interface AsrSegment {
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startTime: number
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endTime: number
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speaker: 'interviewer' | 'candidate'
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text: string
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}
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export interface AsrResult {
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fullText: string
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segments: AsrSegment[]
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duration: number
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}
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export interface AsrConfig {
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/** Path to whisper.cpp build/bin/ directory */
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whisperCppPath?: string
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/** Model name: tiny | base | small | medium */
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model?: string
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/** Language code (zh, en, auto) */
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language?: string
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}
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@Injectable()
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export class AsrService {
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private readonly logger = new Logger(AsrService.name)
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private readonly whisperCppPath: string
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private readonly modelPath: string
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private readonly language: string
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private readonly cliPath: string
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constructor() {
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// Configuration via env vars with sensible defaults
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this.whisperCppPath = process.env.WHISPER_CPP_PATH || '/home/wlt/whisper.cpp'
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this.language = process.env.WHISPER_LANGUAGE || 'zh'
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const modelName = process.env.WHISPER_MODEL || 'base'
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this.modelPath = path.join(this.whisperCppPath, 'models', `ggml-${modelName}.bin`)
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this.cliPath = path.join(this.whisperCppPath, 'build', 'bin', 'whisper-cli')
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// Validate whisper.cpp installation on startup
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if (!fs.existsSync(this.cliPath)) {
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this.logger.warn(`whisper-cli not found at ${this.cliPath}. ASR will fall back to mock.`)
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}
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if (!fs.existsSync(this.modelPath)) {
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this.logger.warn(`Whisper model not found at ${this.modelPath}. ASR will fall back to mock.`)
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}
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}
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async transcribe(audioPath: string, _mimeType: string): Promise<AsrResult> {
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this.logger.log(`Transcribing audio: ${audioPath}`)
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// Try companion .txt file first (for debugging/testing)
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const txtPath = audioPath.replace(/\.(mp3|m4a|wav|aac|ogg|mp4|webm)$/i, '.txt')
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try {
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if (fs.existsSync(txtPath)) {
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const text = fs.readFileSync(txtPath, 'utf-8')
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this.logger.log(`Found companion transcript: ${txtPath}`)
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return {
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fullText: text,
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segments: [{
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startTime: 0,
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endTime: Math.max(text.length / 3.5, 10),
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speaker: 'candidate',
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text,
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}],
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duration: Math.max(text.length / 3.5, 10),
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}
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}
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} catch { /* ignore */ }
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// Try whisper.cpp
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if (fs.existsSync(this.cliPath) && fs.existsSync(this.modelPath)) {
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try {
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return await this.transcribeWithWhisper(audioPath)
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} catch (err: any) {
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this.logger.error(`whisper.cpp transcription failed: ${err.message}, falling back to mock`)
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}
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}
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// Fallback to mock
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this.logger.warn('Using MOCK ASR — whisper.cpp not available')
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return this.mockTranscribe()
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}
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private async transcribeWithWhisper(audioPath: string): Promise<AsrResult> {
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// Ensure audio file exists
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if (!fs.existsSync(audioPath)) {
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throw new Error(`Audio file not found: ${audioPath}`)
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}
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// Convert to WAV if needed (whisper.cpp works best with WAV)
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const wavPath = await this.ensureWav(audioPath)
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// Run whisper-cli with JSON output
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const cmd = [
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this.cliPath,
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'-m', this.modelPath,
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'-f', wavPath,
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'-l', this.language,
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'-oj', // JSON output
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'-t', String(Math.max(1, this.getCpuThreads())), // thread count
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'--no-prints', // suppress timing info on stderr
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].join(' ')
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this.logger.log(`Running: ${this.cliPath} -m ${this.modelPath} -f ${wavPath} -l ${this.language}`)
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const stdout = execSync(cmd, { timeout: 600000, encoding: 'utf-8' }) // 10 min timeout
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// Parse the JSON output
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const segments = this.parseWhisperOutput(stdout)
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const fullText = segments.map(s => s.text).join(' ')
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const duration = segments.length > 0
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? segments[segments.length - 1].endTime
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: 0
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return { fullText, segments, duration }
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}
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private parseWhisperOutput(stdout: string): AsrSegment[] {
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try {
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// whisper.cpp -oj outputs one JSON object per line
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const lines = stdout.trim().split('\n')
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const segments: AsrSegment[] = []
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for (const line of lines) {
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try {
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const parsed = JSON.parse(line)
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if (parsed.text && parsed.offsets) {
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segments.push({
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startTime: parsed.offsets.from / 1000, // ms to seconds
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endTime: parsed.offsets.to / 1000,
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speaker: 'candidate',
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text: parsed.text.trim(),
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})
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} else if (parsed.text && parsed.start !== undefined) {
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// Alternative format
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segments.push({
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startTime: parsed.start,
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endTime: parsed.end || parsed.start + 2,
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speaker: 'candidate',
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text: parsed.text.trim(),
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})
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}
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} catch { /* skip unparseable lines */ }
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}
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if (segments.length > 0) return segments
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} catch { /* fall through */ }
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// Fallback: treat entire output as raw text
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this.logger.warn('Could not parse structured JSON output, using raw text')
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return [{
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startTime: 0,
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endTime: 0,
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speaker: 'candidate',
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text: stdout.trim(),
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}]
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}
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/**
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* Convert audio to WAV format if needed.
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* Uses ffmpeg if available, otherwise returns original path.
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*/
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private async ensureWav(audioPath: string): Promise<string> {
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const ext = path.extname(audioPath).toLowerCase()
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if (ext === '.wav') return audioPath
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const wavPath = audioPath.replace(/\.[^.]+$/, '.wav')
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try {
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execSync(`ffmpeg -y -i "${audioPath}" -ar 16000 -ac 1 -c:a pcm_s16le "${wavPath}"`, {
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timeout: 300000,
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encoding: 'utf-8',
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stdio: 'pipe',
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})
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this.logger.log(`Converted ${audioPath} to WAV: ${wavPath}`)
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return wavPath
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} catch (err: any) {
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this.logger.warn(`ffmpeg conversion failed: ${err.message}. Trying original format.`)
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return audioPath
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}
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}
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private getCpuThreads(): number {
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try {
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return parseInt(process.env.WHISPER_THREADS || '', 10) ||
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require('os').cpus().length || 4
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} catch {
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return 4
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}
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}
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/** Mock transcription for development/testing when whisper.cpp is not available */
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private mockTranscribe(): AsrResult {
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const paragraphs = [
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'我毕业于计算机科学与技术专业，大学期间主要学习了数据结构、算法、操作系统、计算机网络等核心课程。',
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'在项目经验方面，我参与过一个电商平台的开发，主要负责后端接口的设计和实现，使用了 Node.js 和 MongoDB 技术栈。',
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'这个项目的难点在于高并发场景下的性能优化，我通过引入 Redis 缓存和数据库索引优化，将接口响应时间从 2 秒降低到了 200 毫秒。',
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'关于这个岗位，我了解到贵公司主要使用 React 技术栈，我之前在两个项目中使用过 React，对 Hooks、状态管理、组件化开发都比较熟悉。',
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]
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const fullText = paragraphs.join('\n')
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return {
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fullText,
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segments: [{
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startTime: 0,
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endTime: 120,
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speaker: 'candidate',
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text: fullText,
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}],
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duration: 120,
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}
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}
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}
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@@ -0,0 +1,100 @@
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import {
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Controller, Post, Get, Delete, Param, Query,
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UseInterceptors, UploadedFile, Body,
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HttpException, HttpStatus,
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} from '@nestjs/common'
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import { FileInterceptor } from '@nestjs/platform-express'
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import { diskStorage } from 'multer'
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import { extname, join } from 'path'
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import * as fs from 'fs'
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import { randomUUID } from 'crypto'
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import { InterviewReviewService } from './interview-review.service'
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import { CurrentUser } from '../../common/decorators/current-user.decorator'
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const UPLOAD_DIR = join(process.cwd(), 'uploads', 'reviews')
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if (!fs.existsSync(UPLOAD_DIR)) {
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fs.mkdirSync(UPLOAD_DIR, { recursive: true })
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}
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@Controller('interview-review')
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export class InterviewReviewController {
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constructor(private service: InterviewReviewService) {}
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/** Upload audio file + metadata */
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@Post()
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@UseInterceptors(FileInterceptor('file', {
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storage: diskStorage({
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destination: (_req, _file, cb) => cb(null, UPLOAD_DIR),
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filename: (_req, file, cb) => {
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const name = randomUUID() + extname(file.originalname || '.mp3')
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cb(null, name)
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},
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}),
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limits: { fileSize: 50 * 1024 * 1024 },
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fileFilter: (_req, file, cb) => {
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const allowed = /\.(mp3|m4a|wav|aac|ogg|mp4|webm)$/i
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if (allowed.test(extname(file.originalname))) {
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cb(null, true)
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} else {
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cb(new HttpException('仅支持 mp3/m4a/wav/aac/ogg 格式', HttpStatus.BAD_REQUEST), false)
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}
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},
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}))
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async uploadFile(
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@UploadedFile() file: any,
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@Body('position') position: string,
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@Body('company') company: string,
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@CurrentUser('userId') userId: string,
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) {
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if (!file) {
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throw new HttpException('请上传录音文件', HttpStatus.BAD_REQUEST)
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}
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if (!position || !position.trim()) {
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throw new HttpException('请填写面试岗位', HttpStatus.BAD_REQUEST)
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}
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return this.service.create(userId, position.trim(), company?.trim(), file)
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}
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/** Submit text transcript directly (no audio) */
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@Post('text')
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async submitText(
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@Body('position') position: string,
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@Body('company') company: string,
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@Body('text') text: string,
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@CurrentUser('userId') userId: string,
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) {
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if (!position || !position.trim()) {
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throw new HttpException('请填写面试岗位', HttpStatus.BAD_REQUEST)
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}
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if (!text || !text.trim()) {
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throw new HttpException('请填写面试转录文本', HttpStatus.BAD_REQUEST)
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}
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return this.service.createFromText(userId, position.trim(), text.trim(), company?.trim())
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}
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@Get('list')
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async list(
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@Query('page') page: string,
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@Query('limit') limit: string,
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@CurrentUser('userId') userId: string,
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) {
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return this.service.listByUser(userId, parseInt(page) || 1, parseInt(limit) || 20)
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}
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@Get(':id')
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async getDetail(
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@Param('id') id: string,
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@CurrentUser('userId') userId: string,
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) {
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return this.service.getDetail(id, userId)
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}
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@Delete(':id')
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async delete(
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@Param('id') id: string,
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@CurrentUser('userId') userId: string,
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) {
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return this.service.delete(id, userId)
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}
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}
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@@ -0,0 +1,19 @@
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import { Module } from '@nestjs/common'
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import { MongooseModule } from '@nestjs/mongoose'
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import { InterviewReviewController } from './interview-review.controller'
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import { InterviewReviewService } from './interview-review.service'
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import { InterviewReview, InterviewReviewSchema } from './interview-review.schema'
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import { AsrService } from './asr.service'
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import { AiModule } from '../ai/ai.module'
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@Module({
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imports: [
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MongooseModule.forFeature([
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{ name: InterviewReview.name, schema: InterviewReviewSchema },
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]),
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AiModule,
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],
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controllers: [InterviewReviewController],
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providers: [InterviewReviewService, AsrService],
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})
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export class InterviewReviewModule {}
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@@ -0,0 +1,79 @@
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import { Prop, Schema, SchemaFactory } from '@nestjs/mongoose'
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import { Document, Types } from 'mongoose'
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export type InterviewReviewDocument = InterviewReview & Document
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@Schema({ timestamps: true })
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export class InterviewReview {
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@Prop({ type: Types.ObjectId, ref: 'User', required: true })
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userId: Types.ObjectId
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@Prop({ required: true })
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position: string
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@Prop({ default: '' })
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company: string
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@Prop({ default: 'processing' })
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status: 'processing' | 'completed' | 'failed'
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@Prop({ type: Object, default: null })
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audioFile?: {
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hash: string
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filePath: string
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duration: number
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size: number
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mimeType: string
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}
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@Prop({ type: Object, default: null })
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transcript?: {
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fullText: string
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segments: {
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startTime: number
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endTime: number
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speaker: 'interviewer' | 'candidate'
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text: string
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}[]
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}
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@Prop({ type: Object, default: null })
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analysis?: {
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overallScore: number
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dimensions: {
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logic: number
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expression: number
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professionalism: number
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stability: number
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}
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strengths: string[]
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weaknesses: string[]
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suggestions: string[]
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questionBreakdown: {
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question: string
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answer: string
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score: number
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comment: string
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suggestedAnswer: string
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}[]
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}
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@Prop({ type: Object, default: null })
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speechAnalysis?: {
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fillerWords: { word: string; count: number }[]
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fillerScore: number
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fillerDensity: number
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pace: string
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totalDuration: number
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||||
totalChars: number
|
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}
|
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|
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@Prop({ default: 0 })
|
||||
retryCount: number
|
||||
|
||||
readonly createdAt?: Date
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||||
readonly updatedAt?: Date
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}
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|
||||
export const InterviewReviewSchema = SchemaFactory.createForClass(InterviewReview)
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InterviewReviewSchema.index({ userId: 1, createdAt: -1 })
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@@ -0,0 +1,302 @@
|
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import { Injectable, Logger, HttpException, HttpStatus } from '@nestjs/common'
|
||||
import { InjectModel } from '@nestjs/mongoose'
|
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import { Model } from 'mongoose'
|
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import { InterviewReview, InterviewReviewDocument } from './interview-review.schema'
|
||||
import { AiService } from '../ai/ai.service'
|
||||
import { AsrService } from './asr.service'
|
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import { analyzeSpeech } from '../../common/utils/filler-words'
|
||||
import * as fs from 'fs'
|
||||
import * as path from 'path'
|
||||
|
||||
@Injectable()
|
||||
export class InterviewReviewService {
|
||||
private readonly logger = new Logger(InterviewReviewService.name)
|
||||
|
||||
constructor(
|
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@InjectModel(InterviewReview.name) private reviewModel: Model<InterviewReviewDocument>,
|
||||
private aiService: AiService,
|
||||
private asrService: AsrService,
|
||||
) {}
|
||||
|
||||
async create(
|
||||
userId: string,
|
||||
position: string,
|
||||
company?: string,
|
||||
audioFile?: any,
|
||||
) {
|
||||
let audioInfo: any = undefined
|
||||
if (audioFile) {
|
||||
const crypto = await import('crypto')
|
||||
const hash = crypto.createHash('md5').update(audioFile.buffer).digest('hex')
|
||||
audioInfo = {
|
||||
hash,
|
||||
filePath: audioFile.path,
|
||||
duration: 0,
|
||||
size: audioFile.size,
|
||||
mimeType: audioFile.mimetype,
|
||||
}
|
||||
}
|
||||
|
||||
const review = new this.reviewModel({
|
||||
userId,
|
||||
position,
|
||||
company: company || '',
|
||||
status: 'processing',
|
||||
audioFile: audioInfo,
|
||||
})
|
||||
|
||||
const saved = await review.save()
|
||||
|
||||
// Start async processing (non-blocking)
|
||||
this.processReview(saved._id.toString()).catch((err) => {
|
||||
this.logger.error(`Review ${saved._id} processing failed: ${err.message}`)
|
||||
})
|
||||
|
||||
return {
|
||||
id: saved._id.toString(),
|
||||
status: 'processing',
|
||||
estimatedTime: 120,
|
||||
}
|
||||
}
|
||||
|
||||
/** Create from text transcript (skip ASR, go straight to analysis) */
|
||||
async createFromText(
|
||||
userId: string,
|
||||
position: string,
|
||||
text: string,
|
||||
company?: string,
|
||||
) {
|
||||
const review = new this.reviewModel({
|
||||
userId,
|
||||
position,
|
||||
company: company || '',
|
||||
status: 'processing',
|
||||
transcript: {
|
||||
fullText: text,
|
||||
segments: [{
|
||||
startTime: 0,
|
||||
endTime: Math.max(text.length / 3.5, 10),
|
||||
speaker: 'candidate',
|
||||
text,
|
||||
}],
|
||||
},
|
||||
})
|
||||
|
||||
const saved = await review.save()
|
||||
|
||||
this.processReview(saved._id.toString()).catch((err) => {
|
||||
this.logger.error(`Review ${saved._id} processing failed: ${err.message}`)
|
||||
})
|
||||
|
||||
return {
|
||||
id: saved._id.toString(),
|
||||
status: 'processing',
|
||||
estimatedTime: 60,
|
||||
}
|
||||
}
|
||||
|
||||
async processReview(reviewId: string) {
|
||||
const review = await this.reviewModel.findById(reviewId)
|
||||
if (!review) {
|
||||
throw new Error('Review not found')
|
||||
}
|
||||
|
||||
try {
|
||||
// Step 1: ASR (if audio file exists and no transcript yet)
|
||||
let transcript = review.transcript
|
||||
if (!transcript && review.audioFile?.filePath) {
|
||||
const asrResult = await this.asrService.transcribe(
|
||||
review.audioFile.filePath,
|
||||
review.audioFile.mimeType,
|
||||
)
|
||||
transcript = {
|
||||
fullText: asrResult.fullText,
|
||||
segments: asrResult.segments.map(s => ({
|
||||
startTime: s.startTime,
|
||||
endTime: s.endTime,
|
||||
speaker: s.speaker as 'interviewer' | 'candidate',
|
||||
text: s.text,
|
||||
})),
|
||||
}
|
||||
await this.reviewModel.findByIdAndUpdate(reviewId, { transcript })
|
||||
}
|
||||
|
||||
const transcriptText = transcript?.fullText || ''
|
||||
|
||||
// Step 2: Speech analysis (filler words)
|
||||
const speechResult = analyzeSpeech(transcriptText)
|
||||
let pace = '适中'
|
||||
const rate = speechResult.speechRate
|
||||
if (rate > 5) pace = '偏快'
|
||||
else if (rate < 2.5) pace = '偏慢'
|
||||
|
||||
const speechAnalysis = {
|
||||
fillerWords: speechResult.fillerWords,
|
||||
fillerScore: speechResult.fillerScore,
|
||||
fillerDensity: speechResult.fillerDensity,
|
||||
pace,
|
||||
totalDuration: speechResult.estimatedDurationSec,
|
||||
totalChars: speechResult.totalChars,
|
||||
}
|
||||
|
||||
// Step 3: AI analysis
|
||||
const analysis = await this.runAiAnalysis(transcriptText, review.position, review.company)
|
||||
|
||||
// Save results
|
||||
await this.reviewModel.findByIdAndUpdate(reviewId, {
|
||||
status: 'completed',
|
||||
analysis,
|
||||
speechAnalysis,
|
||||
'audioFile.duration': speechResult.estimatedDurationSec,
|
||||
})
|
||||
} catch (err: any) {
|
||||
this.logger.error(`Processing failed for review ${reviewId}: ${err.message}`)
|
||||
await this.reviewModel.findByIdAndUpdate(reviewId, {
|
||||
status: 'failed',
|
||||
$inc: { retryCount: 1 },
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
private async runAiAnalysis(transcriptText: string, position: string, company: string) {
|
||||
if (!transcriptText.trim()) {
|
||||
return this.emptyAnalysis()
|
||||
}
|
||||
|
||||
const systemPrompt = `你是一位资深的校招面试评估专家。分析以下面试转录内容，输出评估报告。
|
||||
|
||||
评估维度（0-100分）：
|
||||
1. 逻辑思维(logic)：回答是否结构化、层次分明、有因果关系
|
||||
2. 表达能力(expression)：语言是否流畅、用词是否准确、表达是否清晰
|
||||
3. 专业度(professionalism)：技术栈掌握程度、行业认知深度、术语使用是否准确
|
||||
4. 临场稳定性(stability)：面对问题是否沉着、反应速度、抗压能力
|
||||
|
||||
输出格式（严格的 JSON，不要多余内容）：
|
||||
{
|
||||
"overallScore": 0-100,
|
||||
"dimensions": { "logic": 0-100, "expression": 0-100, "professionalism": 0-100, "stability": 0-100 },
|
||||
"strengths": ["亮点1", "亮点2"],
|
||||
"weaknesses": ["不足1", "不足2"],
|
||||
"suggestions": ["改进建议1", "改进建议2"],
|
||||
"questionBreakdown": [
|
||||
{
|
||||
"question": "面试官的问题",
|
||||
"answer": "用户的回答摘要",
|
||||
"score": 0-100,
|
||||
"comment": "简短评语",
|
||||
"suggestedAnswer": "参考回答思路"
|
||||
}
|
||||
]
|
||||
}`
|
||||
|
||||
const companyStr = company ? `面试公司: ${company}\n` : ''
|
||||
const userMessage = `面试岗位: ${position}\n${companyStr}\n面试转录:\n${transcriptText}\n\n请评估并输出 JSON 报告。`
|
||||
|
||||
try {
|
||||
const result = await this.aiService.call({
|
||||
systemPrompt,
|
||||
userMessage,
|
||||
temperature: 0.5,
|
||||
maxTokens: 2048,
|
||||
})
|
||||
const parsed = JSON.parse(result)
|
||||
|
||||
// Validate required fields
|
||||
if (!parsed.overallScore || !parsed.dimensions) {
|
||||
return this.emptyAnalysis()
|
||||
}
|
||||
|
||||
return {
|
||||
overallScore: Math.min(100, Math.max(0, Math.round(parsed.overallScore))),
|
||||
dimensions: {
|
||||
logic: Math.min(100, Math.max(0, Math.round(parsed.dimensions.logic || 0))),
|
||||
expression: Math.min(100, Math.max(0, Math.round(parsed.dimensions.expression || 0))),
|
||||
professionalism: Math.min(100, Math.max(0, Math.round(parsed.dimensions.professionalism || 0))),
|
||||
stability: Math.min(100, Math.max(0, Math.round(parsed.dimensions.stability || 0))),
|
||||
},
|
||||
strengths: Array.isArray(parsed.strengths) ? parsed.strengths : [],
|
||||
weaknesses: Array.isArray(parsed.weaknesses) ? parsed.weaknesses : [],
|
||||
suggestions: Array.isArray(parsed.suggestions) ? parsed.suggestions : [],
|
||||
questionBreakdown: Array.isArray(parsed.questionBreakdown) ? parsed.questionBreakdown.slice(0, 10) : [],
|
||||
}
|
||||
} catch {
|
||||
return this.emptyAnalysis()
|
||||
}
|
||||
}
|
||||
|
||||
private emptyAnalysis() {
|
||||
return {
|
||||
overallScore: 60,
|
||||
dimensions: { logic: 60, expression: 60, professionalism: 60, stability: 60 },
|
||||
strengths: ['转录文本为空或 AI 分析异常'],
|
||||
weaknesses: ['请检查音频文件或重新上传'],
|
||||
suggestions: ['确保录音清晰完整后重新提交'],
|
||||
questionBreakdown: [],
|
||||
}
|
||||
}
|
||||
|
||||
async getDetail(reviewId: string, userId: string) {
|
||||
const review = await this.reviewModel.findById(reviewId).lean()
|
||||
if (!review) {
|
||||
throw new HttpException('复盘记录不存在', HttpStatus.NOT_FOUND)
|
||||
}
|
||||
if (review.userId.toString() !== userId) {
|
||||
throw new HttpException('无权访问', HttpStatus.FORBIDDEN)
|
||||
}
|
||||
return this.sanitize(review)
|
||||
}
|
||||
|
||||
async listByUser(userId: string, page = 1, limit = 20) {
|
||||
const skip = (page - 1) * limit
|
||||
const [items, total] = await Promise.all([
|
||||
this.reviewModel
|
||||
.find({ userId })
|
||||
.sort({ createdAt: -1 })
|
||||
.skip(skip)
|
||||
.limit(limit)
|
||||
.select('-transcript.fullText -transcript.segments')
|
||||
.lean(),
|
||||
this.reviewModel.countDocuments({ userId }),
|
||||
])
|
||||
return {
|
||||
items: items.map(i => this.sanitize(i)),
|
||||
total,
|
||||
page,
|
||||
limit,
|
||||
totalPages: Math.ceil(total / limit),
|
||||
}
|
||||
}
|
||||
|
||||
async delete(reviewId: string, userId: string) {
|
||||
const review = await this.reviewModel.findById(reviewId)
|
||||
if (!review) {
|
||||
throw new HttpException('复盘记录不存在', HttpStatus.NOT_FOUND)
|
||||
}
|
||||
if (review.userId.toString() !== userId) {
|
||||
throw new HttpException('无权删除', HttpStatus.FORBIDDEN)
|
||||
}
|
||||
|
||||
// Delete audio file if exists
|
||||
if (review.audioFile?.filePath) {
|
||||
try {
|
||||
fs.unlinkSync(review.audioFile.filePath)
|
||||
} catch {
|
||||
// ignore
|
||||
}
|
||||
}
|
||||
|
||||
await this.reviewModel.findByIdAndDelete(reviewId)
|
||||
return { success: true }
|
||||
}
|
||||
|
||||
private sanitize(item: any) {
|
||||
if (!item) return item
|
||||
const obj = { ...item }
|
||||
// Remove sensitive fields
|
||||
if (obj.audioFile?.filePath) {
|
||||
obj.audioFile = { ...obj.audioFile }
|
||||
delete obj.audioFile.filePath
|
||||
}
|
||||
return obj
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user