/**
 * Agent Suggestion Service
 *
 * Proposes research/monitor agents from existing semantic clusters (no duplicate DBSCAN).
 * Runs after clustering; compares cluster themes vs existing agents.
 */

import prisma from '@/lib/prisma'
import { clusteringService } from './clustering.service'
import { getChatProvider } from '@/lib/ai/factory'
import { getSystemConfig } from '@/lib/config'
import { calculateNextRun } from '@/lib/agents/schedule'

const MIN_CLUSTER_NOTES = 3
const MAX_SUGGESTIONS_PER_RUN = 3

export interface AgentSuggestionPayload {
  topic: string
  reason: string
  suggestedRole: string
  suggestedType: string
  suggestedFrequency: string
  relatedNoteIds: string[]
  clusterId: number
}

function parseJsonArray(raw: string | null | undefined): string[] {
  if (!raw) return []
  try {
    const parsed = JSON.parse(raw)
    return Array.isArray(parsed) ? parsed.filter((x): x is string => typeof x === 'string') : []
  } catch {
    return []
  }
}

function clusterCoveredByAgent(
  topic: string,
  noteIds: string[],
  agent: { name: string; role: string; description: string | null; sourceNoteIds: string | null },
): boolean {
  const topicLower = topic.toLowerCase()
  const haystack = `${agent.name} ${agent.role} ${agent.description || ''}`.toLowerCase()
  const topicWords = topicLower.split(/\s+/).filter(w => w.length > 3)
  if (topicWords.some(w => haystack.includes(w))) return true

  const agentNoteIds = parseJsonArray(agent.sourceNoteIds)
  if (agentNoteIds.length === 0 || noteIds.length === 0) return false
  const overlap = agentNoteIds.filter(id => noteIds.includes(id)).length
  return overlap / noteIds.length >= 0.4
}

async function buildSuggestionWithLlm(
  topic: string,
  noteTitles: string[],
  noteCount: number,
): Promise<Pick<AgentSuggestionPayload, 'reason' | 'suggestedRole' | 'suggestedType' | 'suggestedFrequency'>> {
  const fallback = {
    reason: `${noteCount} notes récentes sur ce thème — aucun agent ne le couvre encore.`,
    suggestedRole: `Recherche et synthétise les dernières informations sur « ${topic} ». Croise avec les notes existantes de l'utilisateur et produis un résumé actionnable.`,
    suggestedType: 'researcher',
    suggestedFrequency: 'weekly',
  }

  try {
    const config = await getSystemConfig()
    const provider = getChatProvider(config)
    if (!provider) return fallback

    const prompt = `Tu proposes un agent IA pour un second cerveau (prise de notes).
Thème détecté: "${topic}"
Notes liées (titres): ${noteTitles.slice(0, 5).join(' | ') || 'sans titre'}
Nombre de notes: ${noteCount}

Retourne UNIQUEMENT du JSON valide:
{"reason":"1 phrase pourquoi un agent est utile","suggestedRole":"prompt système de l'agent (2-3 phrases, français)","suggestedType":"researcher|monitor","suggestedFrequency":"daily|weekly"}`

    const raw = await provider.generateText(prompt)
    const match = raw.match(/\{[\s\S]*\}/)
    if (!match) return fallback
    const parsed = JSON.parse(match[0])
    return {
      reason: String(parsed.reason || fallback.reason).slice(0, 500),
      suggestedRole: String(parsed.suggestedRole || fallback.suggestedRole).slice(0, 2000),
      suggestedType: parsed.suggestedType === 'monitor' ? 'monitor' : 'researcher',
      suggestedFrequency: ['daily', 'weekly'].includes(parsed.suggestedFrequency) ? parsed.suggestedFrequency : 'weekly',
    }
  } catch {
    return fallback
  }
}

export class AgentSuggestionService {
  async generateForUser(userId: string): Promise<number> {
    const stored = await clusteringService.getStoredClusters(userId)
    if (!stored || stored.clusters.length === 0) return 0

    const agents = await prisma.agent.findMany({
      where: { userId, isEnabled: true },
      select: { name: true, role: true, description: true, sourceNoteIds: true },
    })

    const candidates = stored.clusters
      .filter(c => c.noteIds.length >= MIN_CLUSTER_NOTES)
      .sort((a, b) => b.noteIds.length - a.noteIds.length)

    let created = 0
    for (const cluster of candidates) {
      if (created >= MAX_SUGGESTIONS_PER_RUN) break

      const topic = cluster.name || `Thème ${cluster.clusterId + 1}`
      const covered = agents.some(a => clusterCoveredByAgent(topic, cluster.noteIds, a))
      if (covered) continue

      const existing = await prisma.agentSuggestion.findUnique({
        where: { userId_clusterId: { userId, clusterId: cluster.clusterId } },
      })
      if (existing && existing.status !== 'pending') continue

      const notes = await prisma.note.findMany({
        where: { id: { in: cluster.noteIds.slice(0, 8) }, userId },
        select: { title: true },
      })
      const llm = await buildSuggestionWithLlm(
        topic,
        notes.map(n => n.title || 'Sans titre'),
        cluster.noteIds.length,
      )

      const payload = {
        topic,
        reason: llm.reason,
        suggestedRole: llm.suggestedRole,
        suggestedType: llm.suggestedType,
        suggestedFrequency: llm.suggestedFrequency,
        relatedNoteIds: JSON.stringify(cluster.noteIds.slice(0, 20)),
        status: 'pending' as const,
      }

      if (existing) {
        await prisma.agentSuggestion.update({
          where: { id: existing.id },
          data: payload,
        })
      } else {
        await prisma.agentSuggestion.create({
          data: { userId, clusterId: cluster.clusterId, ...payload },
        })
      }
      created++
    }

    return created
  }

  async getPending(userId: string, limit = 3) {
    return prisma.agentSuggestion.findMany({
      where: { userId, status: 'pending' },
      orderBy: { updatedAt: 'desc' },
      take: limit,
    })
  }

  async dismiss(userId: string, id: string) {
    const row = await prisma.agentSuggestion.findFirst({ where: { id, userId } })
    if (!row) return null
    return prisma.agentSuggestion.update({
      where: { id },
      data: { status: 'dismissed' },
    })
  }

  async accept(userId: string, id: string): Promise<{ agentId: string } | null> {
    const row = await prisma.agentSuggestion.findFirst({ where: { id, userId, status: 'pending' } })
    if (!row) return null

    const noteIds = parseJsonArray(row.relatedNoteIds)
    const agent = await prisma.agent.create({
      data: {
        userId,
        name: `Recherche — ${row.topic}`.slice(0, 80),
        description: row.reason,
        type: row.suggestedType,
        role: row.suggestedRole,
        frequency: row.suggestedFrequency,
        sourceNoteIds: noteIds.length > 0 ? JSON.stringify(noteIds) : null,
        tools: JSON.stringify(['web_search', 'read_url']),
        isEnabled: true,
        scheduledTime: '08:00',
      },
    })

    if (row.suggestedFrequency !== 'manual') {
      const nextRun = calculateNextRun({
        frequency: row.suggestedFrequency,
        scheduledTime: '08:00',
      })
      if (nextRun) {
        await prisma.agent.update({ where: { id: agent.id }, data: { nextRun } })
      }
    }

    await prisma.agentSuggestion.update({
      where: { id: row.id },
      data: { status: 'accepted' },
    })

    import('@/lib/ai/services/agent-executor.service')
      .then(({ executeAgent }) => executeAgent(agent.id, userId))
      .catch(err => console.error('[AgentSuggestion] execute failed:', err))

    return { agentId: agent.id }
  }
}

export const agentSuggestionService = new AgentSuggestionService()