76 lines
2.2 KiB
JavaScript
76 lines
2.2 KiB
JavaScript
import { pipeline, env, RawImage } from 'sillytavern-transformers';
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import { getConfigValue } from './util.js';
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import path from 'path';
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import _ from 'lodash';
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configureTransformers();
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function configureTransformers() {
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// Limit the number of threads to 1 to avoid issues on Android
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env.backends.onnx.wasm.numThreads = 1;
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// Use WASM from a local folder to avoid CDN connections
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env.backends.onnx.wasm.wasmPaths = path.join(process.cwd(), 'dist') + path.sep;
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}
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const tasks = {
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'text-classification': {
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defaultModel: 'Cohee/distilbert-base-uncased-go-emotions-onnx',
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pipeline: null,
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configField: 'extras.classificationModel',
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},
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'image-to-text': {
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defaultModel: 'Xenova/vit-gpt2-image-captioning',
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pipeline: null,
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configField: 'extras.captioningModel',
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},
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'feature-extraction': {
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defaultModel: 'Xenova/all-mpnet-base-v2',
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pipeline: null,
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configField: 'extras.embeddingModel',
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},
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}
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async function getRawImage(image) {
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try {
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const buffer = Buffer.from(image, 'base64');
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const byteArray = new Uint8Array(buffer);
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const blob = new Blob([byteArray]);
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const rawImage = await RawImage.fromBlob(blob);
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return rawImage;
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} catch {
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return null;
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}
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}
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function getModelForTask(task) {
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const defaultModel = tasks[task].defaultModel;
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try {
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const model = getConfigValue(tasks[task].configField, null);
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return model || defaultModel;
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} catch (error) {
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console.warn('Failed to read config.conf, using default classification model.');
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return defaultModel;
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}
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}
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async function getPipeline(task) {
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if (tasks[task].pipeline) {
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return tasks[task].pipeline;
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}
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const cache_dir = path.join(process.cwd(), 'cache');
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const model = getModelForTask(task);
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const localOnly = getConfigValue('extras.disableAutoDownload', false);
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console.log('Initializing transformers.js pipeline for task', task, 'with model', model);
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const instance = await pipeline(task, model, { cache_dir, quantized: true, local_files_only: localOnly });
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tasks[task].pipeline = instance;
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return instance;
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}
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export default {
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getPipeline,
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getRawImage,
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}
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