Momento/memento-note/app/api/briefing/sentiment/route.ts

import { NextResponse } from 'next/server'
import { auth } from '@/auth'
import prisma from '@/lib/prisma'
import { getChatProvider } from '@/lib/ai/factory'
import { getSystemConfig } from '@/lib/config'
import { redis } from '@/lib/redis'
import { detectUserLanguage } from '@/lib/i18n/detect-user-language'

const CACHE_TTL_SEC = 3600

/**
 * GET /api/briefing/sentiment
 * Analyzes the emotional tone of recent notes (last 7 days) using LLM.
 */
export async function GET() {
  const session = await auth()
  if (!session?.user?.id) {
    return NextResponse.json({ error: 'Unauthorized' }, { status: 401 })
  }

  const userId = session.user.id
  const locale = await detectUserLanguage()
  const cacheKey = `briefing:sentiment:${userId}:${locale}`

  try {
    const cached = await redis.get(cacheKey)
    if (cached) return NextResponse.json(JSON.parse(cached))
  } catch {}

  const weekAgo = new Date()
  weekAgo.setDate(weekAgo.getDate() - 7)

  const recentNotes = await prisma.note.findMany({
    where: {
      userId,
      trashedAt: null,
      isArchived: false,
      updatedAt: { gte: weekAgo },
    },
    select: { title: true, content: true },
    take: 20,
    orderBy: { updatedAt: 'desc' },
  })

  if (recentNotes.length < 3) {
    return NextResponse.json({
      available: false,
      reason: 'Not enough notes this week',
    })
  }

  const snippets = recentNotes.map(n => {
    const text = n.content.replace(/<[^>]+>/g, ' ').replace(/&nbsp;/g, ' ').replace(/\s+/g, ' ').trim()
    return `${n.title || ''}: ${text.slice(0, 200)}`
  }).join('\n---\n')

  try {
    const config = await getSystemConfig()
    const provider = getChatProvider(config)
    if (!provider) {
      return NextResponse.json({ available: false, reason: 'No AI provider' })
    }

    const localeLabel = locale === 'fr' ? 'French' : locale === 'en' ? 'English' : locale
    const prompt = `Analyze the emotional patterns in these notes from the past week. Return ONLY valid JSON (no markdown, no code fences).
Write "summary" and "topTopic" in ${localeLabel} (locale code: ${locale}). Keep dominantEmotion keys in English as listed.

{"dominantEmotion":"focused|curious|enthusiastic|frustrated|calm|anxious|creative|reflective","sentimentScore":number from -1 to 1,"emotions":{"focused":number,"curious":number,"enthusiastic":number,"frustrated":number,"calm":number,"anxious":number,"creative":number,"reflective":number},"summary":"one sentence describing the emotional pattern","topTopic":"most discussed topic"}

Notes:
${snippets.slice(0, 3000)}`

    const result = await provider.generateText(prompt)
    const jsonMatch = result.match(/\{[\s\S]*\}/)
    if (!jsonMatch) {
      return NextResponse.json({ available: false, reason: 'Parse error' })
    }

    const parsed = JSON.parse(jsonMatch[0])
    const payload = { available: true, ...parsed }
    try { await redis.setex(cacheKey, CACHE_TTL_SEC, JSON.stringify(payload)) } catch {}
    return NextResponse.json(payload)
  } catch (error) {
    console.error('[briefing/sentiment]', error)
    return NextResponse.json({ available: false, reason: 'Analysis failed' })
  }
}