f263cac55f
- Fixed ai-analytics API: Created missing endpoint and corrected model names - Fixed ai-learning-status.ts: Updated to use ai_learning_data and trades models - Fixed batch-analysis route: Corrected ai_learning_data model references - Fixed analysis-details route: Updated automation_sessions and trades models - Fixed test scripts: Updated model names in check-learning-data.js and others - Disabled conflicting route files to prevent Next.js confusion All APIs now use correct snake_case model names matching Prisma schema: - ai_learning_data (not aILearningData) - automation_sessions (not automationSession) - trades (not trade) This resolves 'Unable to load REAL AI analytics' frontend errors.
318 lines
11 KiB
JavaScript
318 lines
11 KiB
JavaScript
import { NextResponse } from 'next/server'
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import { enhancedScreenshotService } from '../../../lib/enhanced-screenshot'
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import { aiAnalysisService } from '../../../lib/ai-analysis'
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import { progressTracker } from '../../../lib/progress-tracker'
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import { PrismaClient } from '@prisma/client';
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const prisma = new PrismaClient();
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// Store analysis results for AI learning
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async function storeAnalysisForLearning(symbol, analysis) {
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try {
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console.log('💾 Storing analysis for AI learning...')
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// Extract market conditions for learning
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const marketConditions = {
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marketSentiment: analysis.marketSentiment || 'NEUTRAL',
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keyLevels: analysis.keyLevels || {},
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trends: analysis.trends || {},
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timeframes: ['5m', '15m', '30m'], // Multi-timeframe analysis
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timestamp: new Date().toISOString()
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}
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await prisma.ai_learning_data.create({
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data: {
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userId: 'default-user', // Use same default user as ai-learning-status
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symbol: symbol,
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timeframe: 'MULTI', // Indicates multi-timeframe batch analysis
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analysisData: JSON.stringify(analysis),
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marketConditions: JSON.stringify(marketConditions),
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confidenceScore: Math.round(analysis.confidence || 50),
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createdAt: new Date()
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}
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})
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console.log(`✅ Analysis stored for learning: ${symbol} - ${analysis.recommendation || 'HOLD'} (${analysis.confidence || 50}% confidence)`)
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} catch (error) {
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console.error('❌ Failed to store analysis for learning:', error)
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}
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}
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export async function POST(request) {
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try {
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const body = await request.json()
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const { symbol, layouts, timeframes, selectedLayouts, analyze = true } = body
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console.log('📊 Batch analysis request:', { symbol, layouts, timeframes, selectedLayouts, analyze })
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// Validate inputs
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if (!symbol || !timeframes || !Array.isArray(timeframes) || timeframes.length === 0) {
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return NextResponse.json(
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{ success: false, error: 'Invalid request: symbol and timeframes array required' },
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{ status: 400 }
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)
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}
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// Generate unique session ID for progress tracking
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const sessionId = `batch_analysis_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`
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console.log('🔍 Created batch analysis session ID:', sessionId)
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// Create progress tracking session with initial steps
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const initialSteps = [
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{
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id: 'init',
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title: 'Initializing Batch Analysis',
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description: 'Starting multi-timeframe analysis...',
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status: 'pending'
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},
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{
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id: 'auth',
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title: 'TradingView Authentication',
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description: 'Logging into TradingView accounts',
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status: 'pending'
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},
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{
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id: 'navigation',
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title: 'Chart Navigation',
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description: 'Navigating to chart layouts',
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status: 'pending'
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},
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{
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id: 'loading',
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title: 'Chart Data Loading',
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description: 'Waiting for chart data and indicators',
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status: 'pending'
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},
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{
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id: 'capture',
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title: 'Screenshot Capture',
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description: `Capturing screenshots for ${timeframes.length} timeframes`,
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status: 'pending'
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},
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{
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id: 'analysis',
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title: 'AI Analysis',
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description: 'Analyzing all screenshots with AI',
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status: 'pending'
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}
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]
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// Create the progress session
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progressTracker.createSession(sessionId, initialSteps)
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// Prepare base configuration
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const baseConfig = {
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symbol: symbol || 'BTCUSD',
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layouts: layouts || selectedLayouts || ['ai', 'diy'],
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sessionId,
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credentials: {
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email: process.env.TRADINGVIEW_EMAIL,
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password: process.env.TRADINGVIEW_PASSWORD
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}
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}
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console.log('🔧 Base config:', baseConfig)
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let allScreenshots = []
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const screenshotResults = []
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try {
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// STEP 1: Collect ALL screenshots from ALL timeframes FIRST
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console.log(`🔄 Starting batch screenshot collection for ${timeframes.length} timeframes...`)
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progressTracker.updateStep(sessionId, 'init', 'active', 'Starting batch screenshot collection...')
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for (let i = 0; i < timeframes.length; i++) {
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const timeframe = timeframes[i]
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const timeframeLabel = getTimeframeLabel(timeframe)
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console.log(`📸 Collecting screenshots for ${symbol} ${timeframeLabel} (${i + 1}/${timeframes.length})`)
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// Update progress for current timeframe
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progressTracker.updateStep(sessionId, 'capture', 'active',
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`Capturing ${timeframeLabel} screenshots (${i + 1}/${timeframes.length})`
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)
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try {
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const config = {
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...baseConfig,
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timeframe: timeframe,
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sessionId: i === 0 ? sessionId : undefined // Only track progress for first timeframe
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}
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// Capture screenshots WITHOUT analysis
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const screenshots = await enhancedScreenshotService.captureWithLogin(config)
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if (screenshots && screenshots.length > 0) {
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console.log(`✅ Captured ${screenshots.length} screenshots for ${timeframeLabel}`)
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// Store screenshots with metadata
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const screenshotData = {
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timeframe: timeframe,
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timeframeLabel: timeframeLabel,
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screenshots: screenshots,
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success: true
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}
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screenshotResults.push(screenshotData)
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allScreenshots.push(...screenshots)
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} else {
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console.warn(`⚠️ No screenshots captured for ${timeframeLabel}`)
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screenshotResults.push({
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timeframe: timeframe,
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timeframeLabel: timeframeLabel,
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screenshots: [],
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success: false,
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error: 'No screenshots captured'
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})
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}
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} catch (timeframeError) {
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console.error(`❌ Error capturing ${timeframeLabel}:`, timeframeError)
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screenshotResults.push({
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timeframe: timeframe,
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timeframeLabel: timeframeLabel,
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screenshots: [],
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success: false,
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error: timeframeError.message
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})
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}
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// Small delay between captures
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if (i < timeframes.length - 1) {
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await new Promise(resolve => setTimeout(resolve, 1000))
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}
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}
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console.log(`📊 Batch screenshot collection completed: ${allScreenshots.length} total screenshots`)
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progressTracker.updateStep(sessionId, 'capture', 'completed', `Captured ${allScreenshots.length} total screenshots`)
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// STEP 2: Send ALL screenshots to AI for comprehensive analysis
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let analysis = null
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if (analyze && allScreenshots.length > 0) {
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console.log(`🤖 Starting comprehensive AI analysis on ${allScreenshots.length} screenshots...`)
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progressTracker.updateStep(sessionId, 'analysis', 'active', 'Running comprehensive AI analysis...')
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try {
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if (allScreenshots.length === 1) {
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analysis = await aiAnalysisService.analyzeScreenshot(allScreenshots[0])
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} else {
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analysis = await aiAnalysisService.analyzeMultipleScreenshots(allScreenshots)
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}
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if (analysis) {
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console.log('✅ Comprehensive AI analysis completed')
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progressTracker.updateStep(sessionId, 'analysis', 'completed', 'AI analysis completed successfully!')
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// Store analysis for learning
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await storeAnalysisForLearning(symbol, analysis)
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} else {
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throw new Error('AI analysis returned null')
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}
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} catch (analysisError) {
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console.error('❌ AI analysis failed:', analysisError)
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progressTracker.updateStep(sessionId, 'analysis', 'error', `AI analysis failed: ${analysisError.message}`)
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// Don't fail the entire request - return screenshots without analysis
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analysis = null
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}
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}
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// STEP 3: Format comprehensive results
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const result = {
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success: true,
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type: 'batch_analysis',
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sessionId,
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timestamp: Date.now(),
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symbol: symbol,
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timeframes: timeframes,
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layouts: baseConfig.layouts,
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summary: `Batch analysis completed for ${timeframes.length} timeframes`,
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totalScreenshots: allScreenshots.length,
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screenshotResults: screenshotResults,
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allScreenshots: allScreenshots.map(path => ({
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url: `/screenshots/${path.split('/').pop()}`,
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timestamp: Date.now()
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})),
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analysis: analysis, // Comprehensive analysis of ALL screenshots
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message: `Successfully captured ${allScreenshots.length} screenshots${analysis ? ' with comprehensive AI analysis' : ''}`
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}
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// Clean up session
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setTimeout(() => progressTracker.deleteSession(sessionId), 2000)
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// Trigger post-analysis cleanup in development mode
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if (process.env.NODE_ENV === 'development') {
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try {
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const { default: aggressiveCleanup } = await import('../../../lib/aggressive-cleanup')
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// Run cleanup in background, don't block the response
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aggressiveCleanup.runPostAnalysisCleanup().catch(console.error)
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} catch (cleanupError) {
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console.error('Error triggering post-batch-analysis cleanup:', cleanupError)
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}
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}
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return NextResponse.json(result)
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} catch (error) {
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console.error('❌ Batch analysis failed:', error)
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progressTracker.updateStep(sessionId, 'analysis', 'error', `Batch analysis failed: ${error.message}`)
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setTimeout(() => progressTracker.deleteSession(sessionId), 5000)
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return NextResponse.json(
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{
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success: false,
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error: 'Batch analysis failed',
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message: error.message,
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sessionId: sessionId
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},
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{ status: 500 }
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)
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}
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} catch (error) {
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console.error('Batch analysis API error:', error)
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return NextResponse.json(
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{
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success: false,
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error: 'Batch analysis failed',
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message: error.message
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},
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{ status: 500 }
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)
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}
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}
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// Helper function to get timeframe label
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function getTimeframeLabel(timeframe) {
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const timeframes = [
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{ label: '1m', value: '1' },
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{ label: '5m', value: '5' },
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{ label: '15m', value: '15' },
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{ label: '30m', value: '30' },
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{ label: '1h', value: '60' },
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{ label: '2h', value: '120' },
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{ label: '4h', value: '240' },
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{ label: '1d', value: 'D' },
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{ label: '1w', value: 'W' },
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{ label: '1M', value: 'M' },
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]
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return timeframes.find(t => t.value === timeframe)?.label || timeframe
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}
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export async function GET() {
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return NextResponse.json({
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message: 'Batch Analysis API - use POST method for multi-timeframe analysis',
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endpoints: {
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POST: '/api/batch-analysis - Run multi-timeframe analysis with parameters'
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}
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})
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}
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