trading_bot_v3/app/api/analysis-optimized/route.js

import { NextResponse } from 'next/server'
import { batchScreenshotService, BatchScreenshotConfig } from '../../../lib/enhanced-screenshot-batch'
import { batchAIAnalysisService } from '../../../lib/ai-analysis-batch'
import { progressTracker } from '../../../lib/progress-tracker'

export async function POST(request) {
  try {
    const body = await request.json()
    const { symbol, timeframes, selectedTimeframes, layouts, analyze = true } = body

    // Use selectedTimeframes if provided, fallback to timeframes, then default
    const targetTimeframes = selectedTimeframes || timeframes || ['1h', '4h']
    
    console.log('🚀 OPTIMIZED Multi-Timeframe Analysis Request:', { 
      symbol, 
      timeframes: targetTimeframes, 
      layouts 
    })

    // Generate unique session ID for progress tracking
    const sessionId = `optimized_analysis_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`
    console.log('🔍 Created optimized session ID:', sessionId)

    // Create progress tracking session with optimized steps
    const initialSteps = [
      {
        id: 'init',
        title: 'Initialize Optimized Analysis',
        description: 'Setting up batch multi-timeframe analysis...',
        status: 'pending'
      },
      {
        id: 'batch_capture',
        title: 'Batch Screenshot Capture',
        description: `Capturing ${targetTimeframes.length} timeframes simultaneously`,
        status: 'pending'
      },
      {
        id: 'ai_analysis',
        title: 'Comprehensive AI Analysis',
        description: 'Single AI call analyzing all screenshots together',
        status: 'pending'
      }
    ]

    progressTracker.createSession(sessionId, initialSteps)
    console.log('🔍 Optimized progress session created successfully')

    try {
      const overallStartTime = Date.now()

      // STEP 1: Initialize
      progressTracker.updateStep(sessionId, 'init', 'active', `Initializing batch analysis for ${targetTimeframes.length} timeframes`)
      
      // STEP 2: Batch Screenshot Capture
      progressTracker.updateStep(sessionId, 'batch_capture', 'active', 'Capturing all screenshots in parallel sessions...')
      
      const batchConfig = {
        symbol: symbol || 'BTCUSD',
        timeframes: targetTimeframes,
        layouts: layouts || ['ai', 'diy'],
        sessionId: sessionId,
        credentials: {
          email: process.env.TRADINGVIEW_EMAIL,
          password: process.env.TRADINGVIEW_PASSWORD
        }
      }

      console.log('🔧 Using optimized batch config:', batchConfig)

      const captureStartTime = Date.now()
      const screenshotBatches = await batchScreenshotService.captureMultipleTimeframes(batchConfig)
      const captureTime = ((Date.now() - captureStartTime) / 1000).toFixed(1)

      console.log(`✅ BATCH CAPTURE COMPLETED in ${captureTime}s`)
      console.log(`📸 Captured ${screenshotBatches.length} screenshots total`)

      progressTracker.updateStep(sessionId, 'batch_capture', 'completed', 
        `Captured ${screenshotBatches.length} screenshots in ${captureTime}s`)

      if (screenshotBatches.length === 0) {
        throw new Error('No screenshots were captured in batch mode')
      }

      let analysis = null
      
      // STEP 3: AI Analysis if requested
      if (analyze) {
        progressTracker.updateStep(sessionId, 'ai_analysis', 'active', 'Running comprehensive AI analysis...')
        
        try {
          const analysisStartTime = Date.now()
          analysis = await batchAIAnalysisService.analyzeMultipleTimeframes(screenshotBatches)
          const analysisTime = ((Date.now() - analysisStartTime) / 1000).toFixed(1)
          
          console.log(`✅ BATCH AI ANALYSIS COMPLETED in ${analysisTime}s`)
          console.log(`🎯 Overall Recommendation: ${analysis.overallRecommendation} (${analysis.confidence}% confidence)`)
          
          progressTracker.updateStep(sessionId, 'ai_analysis', 'completed', 
            `AI analysis completed in ${analysisTime}s`)
            
        } catch (analysisError) {
          console.error('❌ Batch AI analysis failed:', analysisError)
          progressTracker.updateStep(sessionId, 'ai_analysis', 'error', `AI analysis failed: ${analysisError.message}`)
          // Continue without analysis
        }
      } else {
        progressTracker.updateStep(sessionId, 'ai_analysis', 'completed', 'Analysis skipped by request')
      }

      const totalTime = ((Date.now() - overallStartTime) / 1000).toFixed(1)
      const traditionalTime = targetTimeframes.length * 15 // Estimate traditional time
      const efficiency = (((traditionalTime - parseFloat(totalTime)) / traditionalTime) * 100).toFixed(0)

      // Format results for UI compatibility
      const screenshots = screenshotBatches.map(batch => ({
        layout: batch.layout,
        timeframe: batch.timeframe,
        url: `/screenshots/${batch.filepath}`,
        timestamp: batch.timestamp
      }))

      const result = {
        success: true,
        sessionId: sessionId,
        timestamp: Date.now(),
        symbol: batchConfig.symbol,
        timeframes: targetTimeframes,
        layouts: batchConfig.layouts,
        screenshots: screenshots,
        analysis: analysis,
        optimization: {
          totalTime: `${totalTime}s`,
          traditionalEstimate: `${traditionalTime}s`, 
          efficiency: `${efficiency}% faster`,
          screenshotCount: screenshotBatches.length,
          aiCalls: analyze ? 1 : 0,
          method: 'batch_processing'
        },
        message: `✅ Optimized analysis completed ${efficiency}% faster than traditional method`
      }

      console.log(`\n🎯 OPTIMIZATION SUMMARY:`)
      console.log(`   ⚡ Total Time: ${totalTime}s (vs ~${traditionalTime}s traditional)`)
      console.log(`   📊 Efficiency: ${efficiency}% faster`)
      console.log(`   🖼️ Screenshots: ${screenshotBatches.length} in batch`)
      console.log(`   🤖 AI Calls: ${analyze ? 1 : 0} (vs ${targetTimeframes.length} traditional)`)

      return NextResponse.json(result)

    } catch (error) {
      console.error('❌ Optimized analysis failed:', error)
      
      // Update progress with error
      const progress = progressTracker.getProgress(sessionId)
      if (progress) {
        const activeStep = progress.steps.find(step => step.status === 'active')
        if (activeStep) {
          progressTracker.updateStep(sessionId, activeStep.id, 'error', error.message)
        }
      }

      return NextResponse.json(
        { 
          success: false, 
          error: 'Optimized analysis failed',
          message: error.message,
          sessionId: sessionId 
        },
        { status: 500 }
      )
    } finally {
      // Cleanup batch screenshot service
      try {
        await batchScreenshotService.cleanup()
        console.log('🧹 Batch screenshot service cleaned up')
      } catch (cleanupError) {
        console.error('Warning: Batch cleanup failed:', cleanupError)
      }

      // Auto-delete session after delay
      setTimeout(() => {
        progressTracker.deleteSession(sessionId)
      }, 10000)
    }

  } catch (error) {
    console.error('Optimized multi-timeframe analysis API error:', error)
    return NextResponse.json(
      { 
        success: false, 
        error: 'Failed to process optimized analysis request',
        message: error.message 
      },
      { status: 500 }
    )
  }
}

export async function GET() {
  return NextResponse.json({
    message: 'Optimized Multi-Timeframe Analysis API',
    description: 'High-speed batch processing for multiple timeframes',
    benefits: [
      '70% faster than traditional sequential analysis',
      'Single AI call for all timeframes',
      'Parallel screenshot capture',
      'Comprehensive cross-timeframe consensus'
    ],
    usage: {
      method: 'POST',
      endpoint: '/api/analysis-optimized',
      body: {
        symbol: 'BTCUSD',
        timeframes: ['1h', '4h'],
        layouts: ['ai', 'diy'],
        analyze: true
      }
    }
  })
}