| import onnxruntime as ort | |
| from PIL import Image | |
| import numpy as np | |
| # Load the ONNX model | |
| session = ort.InferenceSession('./saved-model/model.onnx') | |
| # Get input and output names | |
| input_name = session.get_inputs()[0].name | |
| output_name = session.get_outputs()[0].name | |
| # Load and preprocess the image | |
| img = Image.open('./training_images/shirt/00e745c9-97d9-429d-8c3f-d3db7a2d2991.jpg').resize((128, 128)) | |
| img_array = np.array(img).astype(np.float32) / 255.0 # Normalize pixel values to [0, 1] | |
| img_array = np.expand_dims(img_array, axis=0) # Add batch dimension | |
| # Run inference | |
| outputs = session.run([output_name], {input_name: img_array}) | |
| print(f"Inference outputs: {outputs}") | |