Add a test for recognize/unrecognize item.

Also updated fixtures and fixed failing tests.

package.json

@@ -150,7 +150,7 @@
     "npm-run-all": "^4.1.5",
     "nyc": "^17.1.0",
     "pixelmatch": "<6.0.0",
-    "playwright": "<1.47",
+    "playwright": "^1.49.0",
     "pngjs": "^7.0.0",
     "postcss": "^8.4.49",
     "postcss-cli": "^11.0.0",

scripts/generate-fixtures.js

@@ -15,7 +15,8 @@ import psTree from 'ps-tree';
 // 	"apiKey": "",
 // 	"userName": "",
 // 	"userId": "",
-// 	"translateServerURL": ""
+// 	"translateServerUrl": "",
+// 	"recognizerServerUrl": ""
 // }
 
 const URL = 'http://localhost:8001/';
@@ -42,7 +43,8 @@ const fixtures = [
 ];
 
 const config = JSON.stringify({
-	translateUrl: secret.translateServerURL,
+	translateUrl: secret.translateServerUrl,
+	recognizerUrl: secret.recognizerServerUrl,
 	libraries: {
 		include: [{
 			key: "g5119976",
@@ -206,7 +208,6 @@ async function makeFixture(stateURL, name) {
 	state = state.replaceAll(secret.apiKey, 'zzzzzzzzzzzzzzzzzzzzzzzz');
 	state = state.replaceAll(secret.userName, 'testuser');
 	state = state.replaceAll(secret.userId, '1');
-	state = state.replaceAll(secret.translateServerURL, 'https://translate-server.zotero.org/Prod');
 	let stateJSON = JSON.parse(state);
 	try {
 		stateJSON.config.libraries.find(l => l.key === 'g5119976').isReadOnly = false;

src/js/component/modal/metadata-retrieval.jsx

@@ -76,13 +76,14 @@ const MetadataRetrievalTitleCell = memo(props => {
 			{...pick(props, ['colIndex', 'width', 'isFirstColumn', 'isLastColumn'])}
 		>
 			<Icon
-				label={`${iconName} icon`}
-				type={`16/${iconName}`}
-				symbol={iconName}
+				className="metadata-status-icon"
 				color={iconName === 'tick' ? 'var(--accent-green)' : iconName === 'cross' ? 'var(--accent-red)' : 'var(--fill-secondary)'}
-				width="16"
 				height="16"
-				className="metadata-status-icon"
+				label={iconName === 'tick' ? 'Completed' : iconName === 'cross' ? 'Error' : 'Processing'}
+				role="status"
+				symbol={iconName}
+				type={`16/${iconName}`}
+				width="16"
 			/>
 			<div className="truncate" id={labelledById}>
 				{title}
@@ -194,7 +195,9 @@ const MetadataRetrievalModal = () => {
 		return {
 			title: item.itemTitle,
 			parentItemTitle: item.completed ? item.parentItemTitle : item.error ? 'Error' : 'Processing',
-			iconName
+			iconName,
+			completed: item.completed,
+			error: item.error
 		}
 	}, [recognizeEntries]);
 
@@ -267,6 +270,7 @@ const MetadataRetrievalModal = () => {
 								icon
 								className="close"
 								onClick={handleCancel}
+								title="Close Dialog"
 							>
 								<Icon type={'16/close'} width="16" height="16" />
 							</Button>
@@ -279,7 +283,14 @@ const MetadataRetrievalModal = () => {
 				tabIndex={!isTouchOrSmall ? 0 : null}
 			>
 				<div className="recognize-progress">
-					<progress value={recognizeProgress} max="1" />
+					<progress
+						aria-label="Metadata retrieval progress"
+						aria-valuemin={0}
+						aria-valuemax={100}
+						aria-valuenow={recognizeProgress * 100}
+						value={recognizeProgress}
+						max="1"
+					/>
 				</div>
 				{isTouchOrSmall ? (
 					<List

src/js/component/modal/settings.jsx

@@ -72,6 +72,7 @@ const SettingsModal = () => {
 						icon
 						className="close"
 						onClick={handleClose}
+						title="Close Dialog"
 					>
 						<Icon type={'16/close'} width="16" height="16" />
 					</Button>

test/basic-ui.test.jsx

@@ -60,7 +60,7 @@ describe('Basic UI', () => {
 		expect(screen.getByRole('button', { name: 'Tag Selector Options' })).toBeInTheDocument();
 		expect(screen.getByRole('toolbar', { name: 'items toolbar' })).toBeInTheDocument();
 		expect(screen.getByRole('grid', { name: 'items' })).toBeInTheDocument();
-		expect(screen.getByText('81 items in this view')).toBeInTheDocument();
+		expect(screen.getByText('82 items in this view')).toBeInTheDocument();
 	});
 
 
@@ -96,7 +96,7 @@ describe('Basic UI', () => {
 		await user.click(clearSearchButton);
 
 		await waitFor(() => expect(searchBox).toHaveValue(''));
-		await waitFor(() => expect(screen.getByText('81 items in this view')).toBeInTheDocument());
+		await waitFor(() => expect(screen.getByText('82 items in this view')).toBeInTheDocument());
 		expect(await screen.findByRole('row', { name: 'Border Collie' })).toBeInTheDocument();
 		expect(screen.queryByRole('row', { name: 'Summary of pathfinding algorithms used in game development' })).not.toBeInTheDocument();
 	});
@@ -203,7 +203,7 @@ describe('Basic UI', () => {
 		const tagSelector = screen.getByRole('navigation', { name: 'tag selector' });
 		const tagButton = getByRole(tagSelector, 'button', { name: 'to read', pressed: false });
 
-		expect(screen.getByText('81 items in this view')).toBeInTheDocument();
+		expect(screen.getByText('82 items in this view')).toBeInTheDocument();
 
 		await userEvent.click(tagButton);
 		await waitForPosition();
@@ -227,7 +227,7 @@ describe('Basic UI', () => {
 		renderWithProviders(<MainZotero />, { preloadedState: libraryViewState });
 		await waitForPosition();
 
-		expect(screen.getByText('81 items in this view')).toBeInTheDocument();
+		expect(screen.getByText('82 items in this view')).toBeInTheDocument();
 		expect(screen.queryByRole('tablist', { name: 'Item Details' })).not.toBeInTheDocument();
 
 		const item = screen.getByRole('row',
@@ -240,7 +240,7 @@ describe('Basic UI', () => {
 			{ name: 'A comparison of sequential Delaunay triangulation algorithms' })
 		).toHaveAttribute('aria-selected', 'true');
 
-		expect(screen.queryByRole('81 items in this view')).not.toBeInTheDocument();
+		expect(screen.queryByRole('82 items in this view')).not.toBeInTheDocument();
 		expect(screen.getByRole('tablist', { name: 'Item Details' })).toBeInTheDocument();
 		expect(screen.getByRole('tab', { name: 'Info', selected: true })).toBeInTheDocument();
 	});

test/fixtures/response/search-by-identifier-recognize.json

@@ -0,0 +1,69 @@
+[
+    {
+        "key": "PBXD4AZ2",
+        "version": 0,
+        "itemType": "preprint",
+        "creators": [
+            {
+                "firstName": "Ashish",
+                "lastName": "Vaswani",
+                "creatorType": "author"
+            },
+            {
+                "firstName": "Noam",
+                "lastName": "Shazeer",
+                "creatorType": "author"
+            },
+            {
+                "firstName": "Niki",
+                "lastName": "Parmar",
+                "creatorType": "author"
+            },
+            {
+                "firstName": "Jakob",
+                "lastName": "Uszkoreit",
+                "creatorType": "author"
+            },
+            {
+                "firstName": "Llion",
+                "lastName": "Jones",
+                "creatorType": "author"
+            },
+            {
+                "firstName": "Aidan N.",
+                "lastName": "Gomez",
+                "creatorType": "author"
+            },
+            {
+                "firstName": "Lukasz",
+                "lastName": "Kaiser",
+                "creatorType": "author"
+            },
+            {
+                "firstName": "Illia",
+                "lastName": "Polosukhin",
+                "creatorType": "author"
+            }
+        ],
+        "tags": [
+            {
+                "tag": "Computer Science - Computation and Language",
+                "type": 1
+            },
+            {
+                "tag": "Computer Science - Machine Learning",
+                "type": 1
+            }
+        ],
+        "title": "Attention Is All You Need",
+        "date": "2023-08-02",
+        "abstractNote": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.",
+        "url": "http://arxiv.org/abs/1706.03762",
+        "extra": "arXiv:1706.03762",
+        "repository": "arXiv",
+        "archiveID": "arXiv:1706.03762",
+        "DOI": "10.48550/arXiv.1706.03762",
+        "libraryCatalog": "arXiv.org",
+        "accessDate": "2025-01-24T16:42:30Z"
+    }
+]

test/fixtures/response/test-user-add-by-identifier-recognize.json

@@ -0,0 +1,123 @@
+{
+    "successful": {
+        "0": {
+            "key": "S8CIV6VJ",
+            "version": 250,
+            "library": {
+                "type": "user",
+                "id": 1,
+                "name": "testuser",
+                "links": {
+                    "alternate": {
+                        "href": "https://www.zotero.org/testuser",
+                        "type": "text/html"
+                    }
+                }
+            },
+            "links": {
+                "self": {
+                    "href": "https://api.zotero.org/users/1/items/S8CIV6VJ",
+                    "type": "application/json"
+                },
+                "alternate": {
+                    "href": "https://www.zotero.org/testuser/items/S8CIV6VJ",
+                    "type": "text/html"
+                }
+            },
+            "meta": {
+                "creatorSummary": "Vaswani et al.",
+                "parsedDate": "2023-08-02",
+                "numChildren": 0
+            },
+            "data": {
+                "key": "S8CIV6VJ",
+                "version": 250,
+                "itemType": "preprint",
+                "title": "Attention Is All You Need",
+                "creators": [
+                    {
+                        "creatorType": "author",
+                        "firstName": "Ashish",
+                        "lastName": "Vaswani"
+                    },
+                    {
+                        "creatorType": "author",
+                        "firstName": "Noam",
+                        "lastName": "Shazeer"
+                    },
+                    {
+                        "creatorType": "author",
+                        "firstName": "Niki",
+                        "lastName": "Parmar"
+                    },
+                    {
+                        "creatorType": "author",
+                        "firstName": "Jakob",
+                        "lastName": "Uszkoreit"
+                    },
+                    {
+                        "creatorType": "author",
+                        "firstName": "Llion",
+                        "lastName": "Jones"
+                    },
+                    {
+                        "creatorType": "author",
+                        "firstName": "Aidan N.",
+                        "lastName": "Gomez"
+                    },
+                    {
+                        "creatorType": "author",
+                        "firstName": "Lukasz",
+                        "lastName": "Kaiser"
+                    },
+                    {
+                        "creatorType": "author",
+                        "firstName": "Illia",
+                        "lastName": "Polosukhin"
+                    }
+                ],
+                "abstractNote": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.",
+                "genre": "",
+                "repository": "arXiv",
+                "archiveID": "arXiv:1706.03762",
+                "place": "",
+                "date": "2023-08-02",
+                "series": "",
+                "seriesNumber": "",
+                "DOI": "10.48550/arXiv.1706.03762",
+                "citationKey": "",
+                "url": "http://arxiv.org/abs/1706.03762",
+                "accessDate": "2025-01-24T16:42:30Z",
+                "archive": "",
+                "archiveLocation": "",
+                "shortTitle": "",
+                "language": "en",
+                "libraryCatalog": "arXiv.org",
+                "callNumber": "",
+                "rights": "",
+                "extra": "arXiv:1706.03762",
+                "tags": [
+                    {
+                        "tag": "Computer Science - Computation and Language",
+                        "type": 1
+                    },
+                    {
+                        "tag": "Computer Science - Machine Learning",
+                        "type": 1
+                    }
+                ],
+                "collections": [
+                    "CSB4KZUU"
+                ],
+                "relations": {},
+                "dateAdded": "2025-01-24T16:42:31Z",
+                "dateModified": "2025-01-24T16:42:31Z"
+            }
+        }
+    },
+    "success": {
+        "0": "S8CIV6VJ"
+    },
+    "unchanged": {},
+    "failed": {}
+}

test/playwright/items.mobile.test.js

@@ -37,7 +37,7 @@ describe('Mobile Snapshots', () => {
 			await page.goto(`http://localhost:${port}/testuser/collections/WTTJ2J56/item-list`);
 			await waitForLoad(page);
 			const itemsList = page.getByRole('list', { name: 'items' });
-			expect(await itemsList.getByRole('listitem').count()).toBe(7);
+			await expect(itemsList.getByRole('listitem')).toHaveCount(7);
 			expect(await screenshot(page, `mobile-items-list-${browserName}`)).toBeTruthy();
 
 			if (singleColumnContexts.includes(browserName)) {