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const fs = require('fs');
const sharp = require('sharp');
const ort = require('onnxruntime-node');
(async () => {
try {
// Step 1: Load and preprocess the image
const imageBuffer = await sharp('./training_images/hat.jpg')
.resize(128, 128) // Resize to 128x128
.raw() // Get raw pixel data
.toBuffer();
// Normalize to [0, 1] and create input tensor
const imgArray = Float32Array.from(imageBuffer).map(value => value / 255.0);
const inputTensor = new ort.Tensor('float32', imgArray, [1, 128, 128, 3]);
// Step 2: Load ONNX model
const session = await ort.InferenceSession.create('./saved-model/model.onnx');
// Step 3: Run inference
const results = await session.run({ [session.inputNames[0]]: inputTensor });
// Step 4: Get output probabilities
const probabilities = results[session.outputNames[0]].data; // Float32Array
const labelIndex = probabilities.indexOf(Math.max(...probabilities)); // Find the index of the max probability
// Step 5: Load the label map
const labelMap = JSON.parse(fs.readFileSync('./labelMap.json', 'utf8')); // Assuming you saved the label map
const label = Object.keys(labelMap).find(key => labelMap[key] === labelIndex);
console.log(`Predicted label: ${label}`);
console.log(`Confidencex: ${(probabilities[labelIndex] * 100).toFixed(2)}%`);
} catch (err) {
console.error('Error:', err);
}
})();
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