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/* eslint-disable no-restricted-globals */
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.6.3'
class MyImageClassificationPipeline {
static task = 'image-classification'
static instance = null
static modelId = null
static async getInstance(model, dtype = 'fp32', progress_callback = null) {
if (this.modelId !== model) {
// Dispose of previous pipeline if model changed
if (this.instance && this.instance.dispose) {
this.instance.dispose()
}
this.instance = null
this.modelId = null
}
if (!this.instance) {
try {
// Try WebGPU first
this.instance = await pipeline(this.task, model, {
dtype,
device: 'webgpu',
progress_callback
})
} catch (webgpuError) {
// Fallback to WASM if WebGPU fails
if (progress_callback) {
progress_callback({
status: 'fallback',
message: 'WebGPU failed, falling back to WASM'
})
}
try {
this.instance = await pipeline(this.task, model, {
dtype,
device: 'wasm',
progress_callback
})
} catch (wasmError) {
throw new Error(
`Both WebGPU and WASM failed. WebGPU error: ${webgpuError.message}. WASM error: ${wasmError.message}`
)
}
}
this.modelId = model
}
return this.instance
}
static dispose() {
if (this.instance && this.instance.dispose) {
this.instance.dispose()
}
this.instance = null
this.modelId = null
}
}
// Listen for messages from the main thread
self.addEventListener('message', async (event) => {
try {
const { type, image, model, dtype, config } = event.data
if (!model) {
self.postMessage({
status: 'error',
output: 'No model provided'
})
return
}
// Get the pipeline instance
const classifier = await MyImageClassificationPipeline.getInstance(
model,
dtype,
(x) => {
self.postMessage({ status: 'loading', output: x })
}
)
if (type === 'load') {
self.postMessage({
status: 'ready',
output: `Image classification model ${model}, dtype ${dtype} loaded`
})
return
}
if (type === 'classify') {
if (!image) {
self.postMessage({
status: 'error',
output: 'No image provided for classification'
})
return
}
try {
// Run classification
const output = await classifier(image, config)
// Format predictions
const predictions = output.map((item) => ({
label: item.label,
score: item.score
}))
self.postMessage({
status: 'output',
output: {
predictions
}
})
} catch (error) {
self.postMessage({
status: 'error',
output:
error.message || 'An error occurred during image classification'
})
}
} else if (type === 'dispose') {
MyImageClassificationPipeline.dispose()
self.postMessage({ status: 'disposed' })
}
} catch (error) {
self.postMessage({
status: 'error',
output:
error.message || 'An error occurred during pipeline initialization'
})
}
})
// Handle initialization
self.postMessage({ status: 'ready' })
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