feat: prettier and eslint

server/events/addTrainData.js

@@ -2,15 +2,21 @@ import { readFileSync, writeFileSync } from 'node:fs';
 import { join } from 'node:path';
 
 export default function addTrainData(eventData) {
-    const file = readFileSync(join(global.__dirname, global.config.fasttext.trainFile), 'utf-8');
-    const data = file.split('\n');
-    const { label, text } = eventData;
+	const file = readFileSync(
+		join(global.__dirname, global.config.fasttext.trainFile),
+		'utf-8'
+	);
+	const data = file.split('\n');
+	const { label, text } = eventData;
 
-    const labelIndex = data.findIndex((data) => data.startsWith(label));
+	const labelIndex = data.findIndex((data) => data.startsWith(label));
 
-    data.splice(labelIndex === -1 ? 0 : labelIndex, 0, `${label} ${text}`);
+	data.splice(labelIndex === -1 ? 0 : labelIndex, 0, `${label} ${text}`);
 
-    writeFileSync(join(global.__dirname, global.config.fasttext.trainFile), data.join('\n'));
+	writeFileSync(
+		join(global.__dirname, global.config.fasttext.trainFile),
+		data.join('\n')
+	);
 
-    return;
-}
+	return;
+}

server/events/ai.js

@@ -1,15 +1,15 @@
 import { serialize } from 'bson';
 
 export default async function runAI(client, data) {
-    const predictions = await global.ft.predict(data.text);
-    const jsonData = {
-        op: 2,
-        id: data.id,
-        predictions
-    };
+	const predictions = await global.ft.predict(data.text);
+	const jsonData = {
+		op: 2,
+		id: data.id,
+		predictions
+	};
 
-    const bsonData = serialize(jsonData);
-    client.write(bsonData);
-    
-    return;
-}
+	const bsonData = serialize(jsonData);
+	client.write(bsonData);
+
+	return;
+}

server/events/index.js

@@ -3,9 +3,4 @@ import trainAI from './trainAI.js';
 import runOCR from './ocr.js';
 import addTrainData from './addTrainData.js';
 
-export {
-    runAI,
-    trainAI,
-    runOCR,
-    addTrainData
-}
+export { runAI, trainAI, runOCR, addTrainData };

server/events/ocr.js

@@ -2,22 +2,22 @@ import { recognize } from 'node-tesseract-ocr';
 import { serialize } from 'bson';
 
 export default async function runOCR(client, eventData) {
-    const config = {
-        lang: 'eng',
-        oem: 3,
-        psm: 3,
-      };
+	const config = {
+		lang: 'eng',
+		oem: 3,
+		psm: 3
+	};
 
-      const ocrText = await recognize(eventData.url, config);
+	const ocrText = await recognize(eventData.url, config);
 
-      const jsonData = {
-        op: 6,
-        id: eventData.id,
-        ocrText
-      };
+	const jsonData = {
+		op: 6,
+		id: eventData.id,
+		ocrText
+	};
 
-      const bsonData = serialize(jsonData);
-      client.write(bsonData);
+	const bsonData = serialize(jsonData);
+	client.write(bsonData);
 
-      return;
-}
+	return;
+}

server/events/trainAI.js

@@ -2,53 +2,56 @@ import FastText from 'fasttext.js';
 import { join } from 'node:path';
 
 export default async function trainAI() {
-    const ft = new FastText({
-        train: {
-            // number of concurrent threads
-            thread: 8,
-            // verbosity level [2]
-            verbose: 4,
-            // number of negatives sampled [5]
-            neg: 7,
-            // loss function {ns, hs, softmax} [ns]
-            loss: 'ns',
-            // learning rate [0.05]
-            lr: 1,
-            // change the rate of updates for the learning rate [100]
-            lrUpdateRate: 1000,
-            // max length of word ngram [1]
-            wordNgrams: 5,
-            // minimal number of word occurences
-            minCount: 1,
-            // minimal number of word occurences
-            minCountLabel: 1,
-            // size of word vectors [100]
-            dim: 100,
-            // size of the context window [5]
-            ws: 5,
-            //  number of epochs [5]
-            epoch: 20,
-            // number of buckets [2000000]
-            bucket: 2000000,
-            // min length of char ngram [3]
-            minn: process.env.TRAIN_MINN || 3,
-            // max length of char ngram [6]
-            maxn: process.env.TRAIN_MAXN || 6,
-            // sampling threshold [0.0001]
-            t: 0.0001,
-            // load pre trained word vectors from unsupervised model
-            pretrainedVectors: ''
-        },
-        serializeTo: join(global.__dirname, global.config.fasttext.loadModel).replace('.bin', ''),
-        trainFile: join(global.__dirname, global.config.fasttext.trainFile),
-        bin: join(global.__dirname, global.config.fasttext.bin)
-    });
-    
-    global.ft.unload();
+	const ft = new FastText({
+		train: {
+			// number of concurrent threads
+			thread: 8,
+			// verbosity level [2]
+			verbose: 4,
+			// number of negatives sampled [5]
+			neg: 7,
+			// loss function {ns, hs, softmax} [ns]
+			loss: 'ns',
+			// learning rate [0.05]
+			lr: 1,
+			// change the rate of updates for the learning rate [100]
+			lrUpdateRate: 1000,
+			// max length of word ngram [1]
+			wordNgrams: 5,
+			// minimal number of word occurences
+			minCount: 1,
+			// minimal number of word occurences
+			minCountLabel: 1,
+			// size of word vectors [100]
+			dim: 100,
+			// size of the context window [5]
+			ws: 5,
+			//  number of epochs [5]
+			epoch: 20,
+			// number of buckets [2000000]
+			bucket: 2000000,
+			// min length of char ngram [3]
+			minn: process.env.TRAIN_MINN || 3,
+			// max length of char ngram [6]
+			maxn: process.env.TRAIN_MAXN || 6,
+			// sampling threshold [0.0001]
+			t: 0.0001,
+			// load pre trained word vectors from unsupervised model
+			pretrainedVectors: ''
+		},
+		serializeTo: join(
+			global.__dirname,
+			global.config.fasttext.loadModel
+		).replace('.bin', ''),
+		trainFile: join(global.__dirname, global.config.fasttext.trainFile),
+		bin: join(global.__dirname, global.config.fasttext.bin)
+	});
 
-    await ft.train()
+	global.ft.unload();
 
-    global.ft.load();
+	await ft.train();
 
-    return;
-}
+	global.ft.load();
+
+	return;
+}