Memory leak fixes

.github/copilot-instructions.md

@@ -14,3 +14,4 @@
 - Remain consistent in styling and code structure.
 - Avoid unncessary iterations. If problems is mostly solved, stop.
 - Split big components into subcomponents. Always create smaller subcomponents for better context management later.
+- Do not do what you're not being asked. Stick to scope of my request.

package-lock.json

@@ -19,6 +19,7 @@
 				"fontkit": "^2.0.4",
 				"heic-convert": "^2.1.0",
 				"idb": "^8.0.3",
+				"idb-keyval": "^6.2.2",
 				"p-queue": "^8.1.0",
 				"pdf-lib": "^1.17.1",
 				"uuid": "^11.1.0"
@@ -1194,6 +1195,12 @@
 			"version": "8.0.3",
 			"license": "ISC"
 		},
+		"node_modules/idb-keyval": {
+			"version": "6.2.2",
+			"resolved": "https://registry.npmjs.org/idb-keyval/-/idb-keyval-6.2.2.tgz",
+			"integrity": "sha512-yjD9nARJ/jb1g+CvD0tlhUHOrJ9Sy0P8T9MF3YaLlHnSRpwPfpTX0XIvpmw3gAJUmEu3FiICLBDPXVwyEvrleg==",
+			"license": "Apache-2.0"
+		},
 		"node_modules/is-core-module": {
 			"version": "2.16.1",
 			"dev": true,

package.json

@@ -40,6 +40,7 @@
 		"fontkit": "^2.0.4",
 		"heic-convert": "^2.1.0",
 		"idb": "^8.0.3",
+		"idb-keyval": "^6.2.2",
 		"p-queue": "^8.1.0",
 		"pdf-lib": "^1.17.1",
 		"uuid": "^11.1.0"

src/lib/components/wizard/StepGallery.svelte

@@ -7,6 +7,8 @@
 	import PhotoCard from './subcomponents/PhotoCard.svelte';
 	import * as tf from '@tensorflow/tfjs';
 	import * as blazeface from '@tensorflow-models/blazeface';
+	import PQueue from 'p-queue';
+	import { set, clear } from 'idb-keyval';
 
 	let photos = $state<PhotoInfo[]>([]);
 	let isProcessing = $state(false);
@@ -14,6 +16,8 @@
 	let totalCount = $state(0);
 	let detector: blazeface.BlazeFaceModel | undefined;
 	let detectorPromise: Promise<void> | undefined;
+	let downloadQueue: PQueue;
+	let faceDetectionQueue: PQueue;
 
 	interface PhotoInfo {
 		name: string;
@@ -38,13 +42,59 @@
 		return detectorPromise;
 	}
 
+	// Force memory cleanup
+	async function forceMemoryCleanup() {
+		await tf.nextFrame(); // Wait for any pending GPU operations
+		
+		// Log memory state without aggressive cleanup
+		const memInfo = tf.memory();
+		console.log('Memory status:', {
+			tensors: memInfo.numTensors,
+			dataBuffers: memInfo.numDataBuffers,
+			bytes: memInfo.numBytes
+		});
+		
+		// Only run garbage collection if available, don't dispose variables
+		if (typeof window !== 'undefined' && 'gc' in window) {
+			(window as any).gc();
+		}
+	}
+
 	async function processPhotosInParallel() {
 		if (isProcessing) return;
 
-		console.log('Starting processPhotos in parallel...');
+		console.log('Starting processPhotos with queues...');
 		isProcessing = true;
 		processedCount = 0;
 
+		try {
+			// Clear previous session's images from IndexedDB
+			await clear();
+			console.log('Cleared IndexedDB.');
+		} catch (e) {
+			console.error('Could not clear IndexedDB:', e);
+		}
+
+		// Initialize queues with more conservative concurrency
+		downloadQueue = new PQueue({ concurrency: 3 }); // Reduced from 5
+		faceDetectionQueue = new PQueue({ concurrency: 1 }); // Keep at 1 for memory safety
+
+		// When both queues are idle, we're done
+		downloadQueue.on('idle', async () => {
+			if (faceDetectionQueue.size === 0 && faceDetectionQueue.pending === 0) {
+				await forceMemoryCleanup(); // Clean up memory when processing is complete
+				isProcessing = false;
+				console.log('All queues are idle. Processing complete.');
+			}
+		});
+		faceDetectionQueue.on('idle', async () => {
+			if (downloadQueue.size === 0 && downloadQueue.pending === 0) {
+				await forceMemoryCleanup(); // Clean up memory when processing is complete
+				isProcessing = false;
+				console.log('All queues are idle. Processing complete.');
+			}
+		});
+
 		const validRows = $filteredSheetData.filter((row) => row._isValid);
 		const photoUrls = new Set<string>();
 		const photoMap = new Map<string, any[]>();
@@ -62,7 +112,7 @@
 		});
 
 		totalCount = photoUrls.size;
-		console.log(`Found ${totalCount} unique photo URLs`);
+		console.log(`Found ${totalCount} unique photo URLs to process.`);
 
 		photos = Array.from(photoUrls).map((url) => ({
 			name: photoMap.get(url)![0].name + ' ' + photoMap.get(url)![0].surname,
@@ -72,28 +122,12 @@
 			faceDetectionStatus: 'pending' as const
 		}));
 
-		const concurrencyLimit = 5;
+		// Add all photos to the download queue
-		const promises = [];
-
 		for (let i = 0; i < photos.length; i++) {
-			const promise = (async () => {
+			downloadQueue.add(() => loadPhoto(i));
-				await loadPhoto(i);
-				processedCount++;
-			})();
-			promises.push(promise);
-
-			if (promises.length >= concurrencyLimit) {
-				await Promise.all(promises);
-				promises.length = 0;
 		}
 	}
 
-		await Promise.all(promises);
-
-		isProcessing = false;
-		console.log('All photos processed.');
-	}
-
 	// Initialize detector and process photos
 	onMount(() => {
 		console.log('StepGallery mounted');
@@ -148,6 +182,8 @@
 		} catch (error) {
 			console.error(`Failed to load photo for ${photo.name}:`, error);
 			photo.status = 'error';
+			// Since this step failed, we still count it as "processed" to not stall the progress bar
+			processedCount++;
 		}
 	}
 
@@ -175,6 +211,8 @@
 		} catch (e) {
 			console.error(`Failed to convert HEIC image for ${photo.name}:`, e);
 			photo.status = 'error';
+			// Since this step failed, we still count it as "processed" to not stall the progress bar
+			processedCount++;
 		}
 	}
 
@@ -198,12 +236,14 @@
 			photo.status = 'success';
 			console.log(`Photo loaded successfully: ${photo.name}`);
 
-			// Save to pictures store
+			// Save blob to IndexedDB instead of the store
+			await set(photo.url, blob);
+
+			// Save to pictures store, but without the blob to save memory
 			pictures.update((pics) => ({
 				...pics,
 				[photo.url]: {
 					id: photo.url,
-					blob: blob,
 					url: objectUrl,
 					downloaded: true,
 					faceDetected: false,
@@ -211,32 +251,47 @@
 				}
 			}));
 
-			// Automatically run face detection to generate crop
+			// Add face detection to its queue
-			await detectFaceForPhoto(index);
+			faceDetectionQueue.add(() => detectFaceForPhoto(index));
 		} catch (error) {
 			console.error(`Failed to process blob for ${photo.name}:`, error);
 			photo.status = 'error';
+			// Since this step failed, we still count it as "processed" to not stall the progress bar
+			processedCount++;
 		}
 	}
 
 	async function detectFaceForPhoto(index: number) {
+		const photo = photos[index];
+		let imageTensor;
 		try {
 			await initializeDetector(); // Ensure detector is loaded
 			if (!detector) {
-				photos[index].faceDetectionStatus = 'failed';
+				photo.faceDetectionStatus = 'failed';
 				console.error('Face detector not available.');
 				return;
 			}
 
-			photos[index].faceDetectionStatus = 'processing';
+			photo.faceDetectionStatus = 'processing';
 			const img = new Image();
 			img.crossOrigin = 'anonymous';
-			img.src = photos[index].objectUrl!;
+			img.src = photo.objectUrl!;
 			await new Promise((r, e) => {
 				img.onload = r;
 				img.onerror = e;
 			});
-			const predictions = await detector.estimateFaces(img, false);
+
+			// Create tensor and manually dispose it after use
+			imageTensor = tf.browser.fromPixels(img);
+			const predictions = await detector.estimateFaces(imageTensor, false);
+
+			// Log memory usage for debugging
+			const memInfo = tf.memory();
+			console.log(`TensorFlow.js memory after face detection for ${photo.name}:`, {
+				numTensors: memInfo.numTensors,
+				numDataBuffers: memInfo.numDataBuffers,
+				numBytes: memInfo.numBytes
+			});
 
 			if (predictions.length > 0) {
 				const getProbability = (p: number | tf.Tensor) =>
@@ -245,26 +300,27 @@
 				const face = predictions.sort(
 					(a, b) => getProbability(b.probability!) - getProbability(a.probability!)
 				)[0];
-				// Coordinates in displayed image space
+
-				let [x1, y1] = face.topLeft as [number, number];
+				const topLeft = face.topLeft as [number, number];
-				let [x2, y2] = face.bottomRight as [number, number];
+				const bottomRight = face.bottomRight as [number, number];
-				// Scale to natural image size
+
+				let [x1, y1] = topLeft;
+				let [x2, y2] = bottomRight;
 				const scaleX = img.naturalWidth / img.width;
 				const scaleY = img.naturalHeight / img.height;
 				const faceWidth = (x2 - x1) * scaleX;
 				const faceHeight = (y2 - y1) * scaleY;
 				const faceCenterX = (x1 + (x2 - x1) / 2) * scaleX;
 				const faceCenterY = (y1 + (y2 - y1) / 2) * scaleY;
-				// Load crop config from env
+
 				const cropRatio = parseFloat(env.PUBLIC_CROP_RATIO || '1.0');
 				const offsetX = parseFloat(env.PUBLIC_FACE_OFFSET_X || '0.0');
 				const offsetY = parseFloat(env.PUBLIC_FACE_OFFSET_Y || '0.0');
 				const cropScale = parseFloat(env.PUBLIC_CROP_SCALE || '2.5');
-				// Compute crop size and center
+
 				let cropWidth = faceWidth * cropScale;
 				let cropHeight = cropWidth / cropRatio;
 
-				// If crop is larger than image, scale it down while maintaining aspect ratio
 				if (cropWidth > img.naturalWidth || cropHeight > img.naturalHeight) {
 					const widthRatio = img.naturalWidth / cropWidth;
 					const heightRatio = img.naturalHeight / cropHeight;
@@ -276,9 +332,11 @@
 				let centerX = faceCenterX + cropWidth * offsetX;
 				let centerY = faceCenterY + cropHeight * offsetY;
 
-				// Clamp center to ensure crop fits
 				centerX = Math.max(cropWidth / 2, Math.min(centerX, img.naturalWidth - cropWidth / 2));
-				centerY = Math.max(cropHeight / 2, Math.min(centerY, img.naturalHeight - cropHeight / 2));
+				centerY = Math.max(
+					cropHeight / 2,
+					Math.min(centerY, img.naturalHeight - cropHeight / 2)
+				);
 
 				const cropX = centerX - cropWidth / 2;
 				const cropY = centerY - cropHeight / 2;
@@ -289,32 +347,40 @@
 					width: Math.round(cropWidth),
 					height: Math.round(cropHeight)
 				};
-				photos[index].cropData = crop;
+				photo.cropData = crop;
-				photos[index].faceDetectionStatus = 'completed';
+				photo.faceDetectionStatus = 'completed';
 
-				// Save crop data to store
 				cropRects.update((crops) => ({
 					...crops,
-					[photos[index].url]: crop
+					[photo.url]: crop
 				}));
 
-				// Update pictures store with face detection info
 				pictures.update((pics) => ({
 					...pics,
-					[photos[index].url]: {
+					[photo.url]: {
-						...pics[photos[index].url],
+						...pics[photo.url],
 						faceDetected: true,
 						faceCount: predictions.length
 					}
 				}));
 			} else {
-				photos[index].faceDetectionStatus = 'failed';
+				photo.faceDetectionStatus = 'failed';
 			}
 		} catch (error) {
-			console.error(`Face detection failed for ${photos[index].name}:`, error);
+			console.error(`Face detection failed for ${photo.name}:`, error);
-			photos[index].faceDetectionStatus = 'failed';
+			photo.faceDetectionStatus = 'failed';
+		} finally {
+			// Manually dispose of the input tensor to prevent memory leaks
+			if (imageTensor) {
+				imageTensor.dispose();
+			}
+			
+			// Add a small delay to allow GPU memory to be freed before next operation
+			await new Promise(resolve => setTimeout(resolve, 100));
+			
+			// This is the final step for a photo, so we increment the processed count here.
+			processedCount++;
 		}
-		// No need to reassign photos array with $state reactivity
 	}
 
 	async function retryPhoto(index: number) {
@@ -325,7 +391,8 @@
 		}
 
 		photo.retryCount++;
-		await loadPhoto(index, true);
+		// Add the retry attempt back to the download queue
+		downloadQueue.add(() => loadPhoto(index, true));
 	}
 
 	function handleCropUpdate(
@@ -364,6 +431,13 @@
 	// Cleanup on unmount using $effect
 	$effect(() => {
 		return () => {
+			// Clear queues on component unmount to stop any ongoing processing
+			if (downloadQueue) {
+				downloadQueue.clear();
+			}
+			if (faceDetectionQueue) {
+				faceDetectionQueue.clear();
+			}
 			cleanupObjectUrls();
 		};
 	});

src/lib/components/wizard/StepGenerate.svelte

@@ -3,6 +3,7 @@
 	import { filteredSheetData, currentStep, pictures, cropRects } from '$lib/stores';
 	import { PDFDocument, StandardFonts, rgb } from 'pdf-lib';
 	import * as fontkit from 'fontkit';
+	import { clear } from 'idb-keyval';
 	import {
 		BORDER_CONFIG,
 		TEXT_CONFIG,
@@ -10,6 +11,7 @@
 		calculateGrid,
 		getAbsolutePositionPt,
 		getAbsolutePhotoDimensionsPt,
+		getImageBlob,
 		MM_TO_PT
 	} from '$lib/pdfLayout';
 	import {
@@ -52,10 +54,34 @@
 
 	let files = $state<GeneratedFile[]>(JSON.parse(JSON.stringify(initialFiles)));
 
+	// Cleanup function to clear IndexedDB and sensitive data
+	async function clearSensitiveData() {
+		try {
+			await clear(); // Clear all data from IndexedDB
+			console.log('IndexedDB cleared for security');
+		} catch (error) {
+			console.error('Failed to clear IndexedDB:', error);
+		}
+	}
+
+	// Handle tab close or page unload
+	function handleBeforeUnload() {
+		clearSensitiveData();
+	}
+
 	onMount(() => {
+		// Add event listener for page unload
+		window.addEventListener('beforeunload', handleBeforeUnload);
+		
 		// Start generation automatically when the component mounts
 		handleGenerate('people_data.pdf');
 		handleGenerate('people_photos.pdf');
+		
+		// Cleanup function when component unmounts
+		return () => {
+			window.removeEventListener('beforeunload', handleBeforeUnload);
+			clearSensitiveData();
+		};
 	});
 
 	// Load Roboto font
@@ -150,6 +176,13 @@
 			fileToUpdate.url = URL.createObjectURL(blob);
 			fileToUpdate.size = pdfBytes.length;
 			fileToUpdate.state = 'done';
+
+			// Check if both PDFs are done, then clear sensitive data
+			const allDone = files.every((f) => f.state === 'done' || f.state === 'error');
+			if (allDone) {
+				console.log('All PDFs generated, clearing sensitive data...');
+				await clearSensitiveData();
+			}
 		} catch (error: any) {
 			console.error(`PDF generation failed for ${fileName}:`, error);
 			fileToUpdate.state = 'error';
@@ -310,7 +343,10 @@
 
 			if (pictureInfo && cropData) {
 				try {
-					const croppedImageBytes = await cropImage(pictureInfo.blob, cropData);
+					// Get blob from IndexedDB instead of the store
+					const imageBlob = await getImageBlob(pictureUrl);
+					if (imageBlob) {
+						const croppedImageBytes = await cropImage(imageBlob, cropData);
 						const embeddedImage = await pdfDoc.embedJpg(croppedImageBytes);
 
 						const imageAspectRatio = embeddedImage.width / embeddedImage.height;
@@ -334,6 +370,15 @@
 							width: imageWidth,
 							height: imageHeight
 						});
+					} else {
+						console.warn(`Image blob not found in IndexedDB for ${pictureUrl}`);
+						// Draw placeholder when blob not found
+						page.drawRectangle({
+							...photoDimsPt,
+							borderColor: rgb(BORDER_CONFIG.color.r, BORDER_CONFIG.color.g, BORDER_CONFIG.color.b),
+							borderWidth: BORDER_CONFIG.width
+						});
+					}
 				} catch (error: any) {
 					console.error(`Failed to embed photo for ${row.name}:`, error);
 					// Draw placeholder on error
@@ -399,6 +444,10 @@
 			}
 		});
 		files = JSON.parse(JSON.stringify(initialFiles));
+		
+		// Clear sensitive data when starting over
+		clearSensitiveData();
+		
 		currentStep.set(0);
 	}
 

src/lib/components/wizard/StepRowFilter.svelte

@@ -107,7 +107,7 @@
 				return processedRow;
 			});
 
-			// Initially select rows based on validity and "Already Printed" status, up to 200
+			// Initially select rows based on validity and "Already Printed" status
 			const rowsToConsider = processedData.filter((row) => {
 				if (!row._isValid) return false;
 				const alreadyPrinted = row.alreadyPrinted;
@@ -118,7 +118,7 @@
 				return true;
 			});
 
-			const initialSelection = rowsToConsider.slice(0, 200).map((row) => row._rowIndex);
+			const initialSelection = rowsToConsider.map((row) => row._rowIndex);
 			selectedRows = new Set(initialSelection);
 
 			updateSelectAllState();
@@ -131,10 +131,6 @@
 		if (selectedRows.has(rowIndex)) {
 			selectedRows.delete(rowIndex);
 		} else {
-			if (selectedRows.size >= 200) {
-				alert('You can only select a maximum of 200 rows at a time.');
-				return;
-			}
 			selectedRows.add(rowIndex);
 		}
 		selectedRows = new Set(selectedRows); // Trigger reactivity
@@ -150,20 +146,13 @@
 				}
 			});
 		} else {
-			// Select all visible valid rows that aren't already printed, up to the limit
+			// Select all visible valid rows that aren't already printed
 			const rowsToSelect = filteredData.filter(
 				(row) => row._isValid && !isRowAlreadyPrinted(row) && !selectedRows.has(row._rowIndex)
 			);
 
-			const availableSlots = 200 - selectedRows.size;
+			for (const row of rowsToSelect) {
-			if (rowsToSelect.length > availableSlots) {
+				selectedRows.add(row._rowIndex);
-				alert(
-					`You can only select up to 200 rows. Only the first ${availableSlots} available rows will be selected.`
-				);
-			}
-
-			for (let i = 0; i < Math.min(rowsToSelect.length, availableSlots); i++) {
-				selectedRows.add(rowsToSelect[i]._rowIndex);
 			}
 		}
 		selectedRows = new Set(selectedRows);

src/lib/pdfLayout.ts

@@ -6,6 +6,7 @@ import {
 	TEXT_FIELD_LAYOUT,
 	PHOTO_FIELD_LAYOUT
 } from './pdfSettings';
+import { get } from 'idb-keyval';
 
 // Conversion factor from millimeters to points (1 inch = 72 points, 1 inch = 25.4 mm)
 export const MM_TO_PT = 72 / 25.4;
@@ -17,6 +18,11 @@ export interface GridLayout {
 	cellHeight: number; // mm
 }
 
+// Function to retrieve a blob from IndexedDB
+export async function getImageBlob(url: string): Promise<Blob | undefined> {
+	return await get(url);
+}
+
 // Calculate how many cards can fit on a page.
 export function calculateGrid(
 	pageWidth: number,