import onnxruntime from torchvision import transforms import torch import torch.nn.functional as F import gradio as gr orst_run = onnxruntime.InferenceSession("model.onnx") idx_to_class = {0: 'chapati', 1: 'mukimo', 2: 'kukuchoma', 3: 'kachumbari', 4: 'ugali', 5: 'githeri', 6: 'matoke', 7: 'pilau', 8: 'nyamachoma', 9: 'sukumawiki', 10: 'bhaji', 11: 'mandazi', 12: 'masalachips'} def predict(image): preprocess = transforms.Compose([ transforms.Resize((256,256)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(image) input_batch = input_tensor.unsqueeze(0) output = orst_run.run(None, {'input': input_batch.numpy()}) output = torch.from_numpy(output[0]) prediction=F.softmax(output,dim=1) predProab,predIndexs = torch.topk(prediction, 3) predProab = predProab.numpy()[0] predIndexs = predIndexs.numpy()[0] confidences = {idx_to_class[predIndexs[i]]: float(predProab[i]) for i in range(3)} return confidences def inference(img): return predict(img) title = 'Kenyan Food Classification' description = "Kenyan Food Classification" examples = ['1.jpg','2.jpg','3.jpg','4.jpg'] gr.Interface(inference, gr.Image(type="pil"), "label", server_name="0.0.0.0",title=title, description=description, examples=examples).launch()