// AI Model Lazy Loading Implementation
import * as tf from '@tensorflow/tfjs'
import type { StudentRequest, MatchResult } from './types'

export class OptimizedStudentAssistanceAI {
  private static models: Map<string, any> = new Map()
  private static modelUrls: Record<string, string> = {
    'text-vectorization': '/models/text-vectorizer.json',
    'matching-engine': '/models/matcher.json',
    'priority-classifier': '/models/priority.json'
  }

  // Lazy load models on demand
  private static async loadModel(modelType: string) {
    if (this.models.has(modelType)) {
      return this.models.get(modelType)
    }

    try {
      console.log(`🤖 Loading AI model: ${modelType}`)
      const model = await tf.loadLayersModel(this.modelUrls[modelType])
      this.models.set(modelType, model)
      console.log(`✅ Model ${modelType} loaded successfully`)
      return model
    } catch (error) {
      console.error(`❌ Failed to load model ${modelType}:`, error)
      // Fallback to rule-based system
      return null
    }
  }

  // Preload critical models in background
  static async preloadCriticalModels() {
    try {
      // Load text vectorization model first (most commonly used)
      await this.loadModel('text-vectorization')
      
      // Load others in background
      setTimeout(() => this.loadModel('matching-engine'), 2000)
      setTimeout(() => this.loadModel('priority-classifier'), 4000)
    } catch (error) {
      console.warn('Background model preloading failed:', error)
    }
  }

  async processRequest(request: StudentRequest): Promise<MatchResult[]> {
    // Load models as needed
    const textModel = await OptimizedStudentAssistanceAI.loadModel('text-vectorization')
    const matchingModel = await OptimizedStudentAssistanceAI.loadModel('matching-engine')
    
    // Process with loaded models or fallback to rule-based
    if (textModel && matchingModel) {
      return this.aiBasedMatching(request, textModel, matchingModel)
    } else {
      return this.ruleBasedMatching(request)
    }
  }

  private async aiBasedMatching(request: StudentRequest, textModel: any, matchingModel: any): Promise<MatchResult[]> {
    // AI-powered matching logic
    console.log('🤖 Using AI-powered matching', { request: request.id, textModel: !!textModel, matchingModel: !!matchingModel })
    // Mock implementation for now
    return [{
      resourceId: 'resource-1',
      resourceName: 'School Supply Kit',
      resourceType: 'supplies',
      confidenceScore: 0.85,
      estimatedImpact: 8.5,
      logisticalComplexity: 2.1,
      estimatedCost: 50,
      fulfillmentTimeline: '2-3 days',
      reasoningFactors: ['AI-based match using TensorFlow models', 'High confidence score'],
      riskFactors: ['Low risk - standard supplies']
    }]
  }

  private async ruleBasedMatching(request: StudentRequest): Promise<MatchResult[]> {
    // Fallback rule-based matching
    console.log('📏 Using rule-based matching (fallback)', { request: request.id })
    // Mock implementation for now
    return [{
      resourceId: 'resource-1',
      resourceName: 'Basic Supply Kit',
      resourceType: 'supplies',
      confidenceScore: 0.65,
      estimatedImpact: 6.5,
      logisticalComplexity: 3.2,
      estimatedCost: 50,
      fulfillmentTimeline: '3-5 days',
      reasoningFactors: ['Rule-based fallback matching', 'Basic category match'],
      riskFactors: ['Medium risk - manual verification needed']
    }]
  }
}