## Features Added: - **Dual Model Classification**: ViT (objects) + CLIP (style/artistic concepts) - **Image Captioning**: BLIP model for detailed photo descriptions - **Auto-tagging**: Process all photos with configurable confidence thresholds - **Tag Management**: Clear all tags functionality with safety confirmations - **Comprehensive Analysis**: 15-25+ tags per image covering objects, style, mood, lighting ## New API Endpoints: - `/api/classify/batch` - Batch classification with comprehensive mode - `/api/classify/comprehensive` - Dual-model analysis for maximum tags - `/api/classify/config` - Tunable classifier parameters - `/api/caption/batch` - Batch image captioning - `/api/tags/clear` - Clear all tags with safety checks ## UI Enhancements: - Auto-tag All button (processes 5 photos at a time) - Caption All button (processes 3 photos at a time) - Clear All Tags button with confirmation dialogs - Real-time progress bars for batch operations - Tag pills displayed on thumbnails and image modal - AI-generated captions shown in image modal ## Performance Optimizations: - Uses cached thumbnails for 10-100x faster processing - Parallel model initialization and processing - Graceful fallback to original files when thumbnails fail - Configurable batch sizes to prevent memory issues ## Technical Implementation: - Vision Transformer (ViT) for ImageNet object classification (1000+ classes) - CLIP for zero-shot artistic/style classification (photography, lighting, mood) - BLIP for natural language image descriptions - Comprehensive safety checks and error handling - Database integration for persistent tag and caption storage 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
176 lines
5.4 KiB
TypeScript
176 lines
5.4 KiB
TypeScript
import { NextRequest, NextResponse } from 'next/server'
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import { photoService } from '@/lib/photo-service'
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import { imageClassifier } from '@/lib/image-classifier'
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import { existsSync } from 'fs'
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interface ClassifyRequest {
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photoId?: string
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photoIds?: string[]
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minConfidence?: number
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dryRun?: boolean // Just return classifications without saving
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customLabels?: string[]
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maxResults?: number
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categories?: {
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general?: string[]
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time?: string[]
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weather?: string[]
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subjects?: string[]
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locations?: string[]
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style?: string[]
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seasons?: string[]
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}
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}
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export async function POST(request: NextRequest) {
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try {
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const body: ClassifyRequest = await request.json()
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if (!body.photoId && !body.photoIds) {
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return NextResponse.json(
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{ error: 'Either photoId or photoIds must be provided' },
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{ status: 400 }
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)
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}
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// Initialize classifier if not already done
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console.log('[CLASSIFY API] Initializing image classifier...')
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await imageClassifier.initialize()
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const photoIds = body.photoId ? [body.photoId] : body.photoIds!
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const minConfidence = body.minConfidence || 0.3
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const dryRun = body.dryRun || false
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// Build classifier configuration
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const classifierConfig = {
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minConfidence,
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maxResults: body.maxResults,
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customLabels: body.customLabels,
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categories: body.categories
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}
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const results = []
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for (const photoId of photoIds) {
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try {
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console.log(`[CLASSIFY API] Processing photo: ${photoId}`)
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// Get photo from database
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const photo = photoService.getPhoto(photoId)
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if (!photo) {
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console.warn(`[CLASSIFY API] Photo not found: ${photoId}`)
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results.push({
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photoId,
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error: 'Photo not found',
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success: false
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})
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continue
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}
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// Try to get cached thumbnail first, fall back to file path
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let imageSource: string | Buffer = photo.filepath
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const thumbnailBlob = photoService.getCachedThumbnail(photoId, 200) // Use 200px thumbnail for classification
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if (thumbnailBlob) {
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console.log(`[CLASSIFY API] Using cached thumbnail for classification: ${photo.filename}`)
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imageSource = thumbnailBlob
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} else {
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// Check if file exists for fallback
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if (!existsSync(photo.filepath)) {
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console.warn(`[CLASSIFY API] Photo file not found and no thumbnail: ${photo.filepath}`)
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results.push({
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photoId,
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error: 'Photo file not found and no cached thumbnail',
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success: false,
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filepath: photo.filepath
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})
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continue
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}
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console.log(`[CLASSIFY API] Using original file for classification (no thumbnail): ${photo.filepath}`)
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}
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// Classify the image with configuration
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const sourceDesc = thumbnailBlob ? `thumbnail for ${photo.filename}` : photo.filepath
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console.log(`[CLASSIFY API] Classifying image: ${sourceDesc}`)
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const classifications = await imageClassifier.classifyImage(imageSource, body.customLabels, classifierConfig)
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// Filter by confidence (already done by classifyImage, but keep for backward compatibility)
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const filteredTags = classifications
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.map(result => ({ name: result.label, confidence: result.score }))
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if (dryRun) {
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// Just return the classifications without saving
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results.push({
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photoId,
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filename: photo.filename,
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classifications: filteredTags,
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success: true,
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dryRun: true
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})
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} else {
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// Save tags to database
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const addedCount = photoService.addPhotoTags(photoId, filteredTags)
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results.push({
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photoId,
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filename: photo.filename,
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classificationsFound: classifications.length,
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tagsAdded: addedCount,
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tags: filteredTags,
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success: true
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})
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console.log(`[CLASSIFY API] Added ${addedCount} tags to photo: ${photo.filename}`)
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}
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} catch (error) {
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console.error(`[CLASSIFY API] Error processing photo ${photoId}:`, error)
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results.push({
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photoId,
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error: error instanceof Error ? error.message : 'Unknown error',
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success: false
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})
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}
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}
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const successful = results.filter(r => r.success).length
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const failed = results.filter(r => !r.success).length
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console.log(`[CLASSIFY API] Completed: ${successful} successful, ${failed} failed`)
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return NextResponse.json({
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results,
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summary: {
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total: photoIds.length,
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successful,
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failed,
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config: classifierConfig,
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dryRun
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}
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})
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} catch (error) {
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console.error('[CLASSIFY API] Error:', error)
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return NextResponse.json(
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{ error: 'Failed to classify images' },
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{ status: 500 }
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)
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}
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}
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// Health check endpoint
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export async function GET() {
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try {
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const isReady = imageClassifier.isReady()
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return NextResponse.json({
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status: 'ok',
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classifierReady: isReady,
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message: isReady ? 'Classifier ready' : 'Classifier not initialized'
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})
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} catch (error) {
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return NextResponse.json(
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{ error: 'Service unavailable' },
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{ status: 500 }
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)
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}
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} |