41 lines
1.3 KiB
JavaScript
41 lines
1.3 KiB
JavaScript
// npm install @huggingface/huggingface jimp
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const fs = require('fs');
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const { ImageProcessor, Model } = require('@huggingface/huggingface');
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const Jimp = require('jimp'); // For image processing
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async function main() {
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const modelName = 'vishnun0027/Crop_Disease_model_1';
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// Load the image processor and model
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const imageProcessor = await ImageProcessor.fromPretrained(modelName);
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const model = await Model.fromPretrained(modelName);
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// Load your image
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let image;
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try {
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image = await Jimp.read('/home/overnion/Status200/tomato.png'); // Replace with the actual path to your image
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// Convert the image to RGB if it's not already
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if (image.bitmap.colorType !== 2) { // 2 means RGB
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image = image.colorType(2);
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}
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} catch (error) {
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console.error("Error: Unable to open image. Check the file type or path.");
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console.error(error);
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return;
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}
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// Prepare the image for the model
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const inputs = imageProcessor(images: image, returnTensors: "pt");
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// Make the prediction
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const outputs = await model(inputs);
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const logits = outputs.logits;
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const predictedClassIdx = logits.argMax(-1).dataSync()[0];
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// Print the predicted class
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console.log("Predicted class:", model.config.id2label[predictedClassIdx]);
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}
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main().catch(console.error);
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