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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() |