ims/app/api/incidents/ai/quality-check/route.ts

export const dynamic = 'force-dynamic'

import { NextRequest, NextResponse } from 'next/server'
import { getSession } from '@/lib/auth/get-session'
import { withUser, asAdmin } from '@/lib/db/with-user'
import { writeAuditLog } from '@/lib/db/audit'
import { auditLog } from '@/lib/db/schema'
import { eq, and, gte, sql } from 'drizzle-orm'
import { createDeepSeekClient } from '@/lib/claude/client'
import { getApiKey } from '@/lib/settings'

export async function POST(request: NextRequest) {
  const session = await getSession()
  if (!session) return NextResponse.json({ error: 'Unauthorized' }, { status: 401 })

  // Rate limit
  const since = new Date(Date.now() - 60_000)
  const [rateRow] = await asAdmin(db =>
    db.select({ cnt: sql<number>`count(*)` }).from(auditLog)
      .where(and(
        eq(auditLog.changedBy, session.sub),
        eq(auditLog.action, 'ai_quality_check'),
        gte(auditLog.changedAt, since),
      ))
  )
  if (Number(rateRow?.cnt ?? 0) > 0)
    return NextResponse.json({ error: 'Rate limited — please wait 60 seconds' }, { status: 429 })

  const deepseekKey = await getApiKey('DEEPSEEK_API_KEY')
  const client = createDeepSeekClient(deepseekKey)

  let body: { description?: string; incident_type?: string }
  try {
    body = await request.json()
  } catch {
    return NextResponse.json({ error: 'Invalid JSON body' }, { status: 400 })
  }
  if (!body.description || !body.incident_type) {
    return NextResponse.json({ error: 'description and incident_type required' }, { status: 422 })
  }

  let res: Awaited<ReturnType<typeof client.chat.completions.create>>
  try {
    res = await client.chat.completions.create({
      model: 'deepseek-chat',
      max_tokens: 1024,
      tools: [{
        type: 'function',
        function: {
          name: 'assess_quality',
          description: 'Assess HSE incident report description quality',
          parameters: {
            type: 'object',
            properties: {
              score: { type: 'number', description: '1-10 quality score' },
              passes: { type: 'boolean', description: 'True when score is 6 or above' },
              feedback: { type: 'string', description: 'One-sentence quality summary' },
              suggestions: {
                type: 'array',
                items: { type: 'string' },
                description: 'Up to 3 concrete suggestions to improve the description',
              },
            },
            required: ['score', 'passes', 'feedback', 'suggestions'],
          },
        },
      }],
      tool_choice: { type: 'function', function: { name: 'assess_quality' } },
      messages: [{
        role: 'user',
        content: `You are an HSE reporting assistant for a Malaysian 3PL warehouse. Assess this incident report description.

Incident type: ${body.incident_type}
Description: ${body.description}

Score 1–10 based on: specificity (location, time, persons involved), completeness (what happened + immediate actions), and clarity. Score 6 or above passes. If score is below 6, give up to 3 actionable suggestions.`,
      }],
    })
  } catch {
    return NextResponse.json({ error: 'AI service unavailable' }, { status: 503 })
  }

  const call = res.choices[0]?.message?.tool_calls?.[0]
  if (!call || call.type !== 'function') return NextResponse.json({ error: 'AI assessment failed' }, { status: 500 })

  let input: { score?: unknown; passes?: unknown; feedback?: unknown; suggestions?: unknown }
  try { input = JSON.parse(call.function.arguments) }
  catch { return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 }) }

  if (
    typeof input.score !== 'number' ||
    typeof input.passes !== 'boolean' ||
    typeof input.feedback !== 'string' ||
    !Array.isArray(input.suggestions)
  ) {
    return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 })
  }

  // audit write uses session.sub as a pseudo record-id (no incident_id at this stage)
  await withUser(session.sub, async tx => {
    await writeAuditLog(tx, 'incidents', session.sub, 'ai_quality_check', {
      score: input.score, passes: input.passes, model: 'deepseek-chat',
    })
  })

  return NextResponse.json(input)
}