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

export const dynamic = 'force-dynamic'

import { NextRequest, NextResponse } from 'next/server'
import { createClient } from '@/lib/supabase/server'
import { createDeepSeekClient } from '@/lib/claude/client'
import { getApiKey } from '@/lib/settings'

export async function POST(request: NextRequest) {
  const supabase = await createClient()
  const { data: { user }, error: authError } = await supabase.auth.getUser()
  if (authError || !user) return NextResponse.json({ error: 'Unauthorized' }, { status: 401 })

  const since = new Date(Date.now() - 60_000).toISOString()
  const { count: recentCount } = await supabase
    .from('audit_log')
    .select('id', { count: 'exact', head: true })
    .eq('changed_by', user.id)
    .eq('action', 'ai_quality_check')
    .gte('changed_at', since)
  if ((recentCount ?? 0) > 0)
    return NextResponse.json({ error: 'Rate limited — please wait 60 seconds' }, { status: 429 })

  const deepseekKey = await getApiKey(supabase, '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 })
  }

  await supabase.rpc('write_audit_log', {
    p_table_name: 'incidents',
    p_record_id: user.id,
    p_action: 'ai_quality_check',
    p_new_value: { score: input.score, passes: input.passes, model: 'deepseek-chat' } as never,
  })

  return NextResponse.json(input)
}