| 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(label="Input Image",type="pil"), "label", title=title, | |
| description=description, examples=examples,cache_examples=False).launch(enable_queue=True) | |