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| from fastai.vision.core import PILImageBW, TensorImageBW | |
| from datasets import ClassLabel | |
| import gradio as gr | |
| from fastai.learner import load_learner | |
| from PIL import Image | |
| from numpy import array | |
| def get_image_attr(x): return x['image'] | |
| def get_target_attr(x): return x['target'] | |
| def get_label_attr(x): return x['label'] | |
| def img2tensor(im: Image.Image): | |
| return TensorImageBW(array(im)).unsqueeze(0) | |
| classLabel = ClassLabel(names=['T - shirt / top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'], id=None) | |
| labels = classLabel.names | |
| def add_target(x:dict): | |
| x['target'] = classLabel.int2str(x['label']) | |
| return x | |
| learn = load_learner('export.pkl', cpu=True) | |
| def classify(inp): | |
| img = PILImageBW.create(inp) | |
| item = dict(image=img) | |
| pred, _, prob = learn.predict(item) | |
| return {label: float(prob[i]) for i, label in enumerate(labels)} | |
| # return classLabel.int2str(int(pred)) | |
| examples = ['shoes.jpg', 't-shirt.jpg'] | |
| interpretation='default' | |
| iface = gr.Interface( | |
| fn=classify, | |
| inputs=gr.inputs.Image(image_mode='L'), | |
| outputs=gr.outputs.Label(num_top_classes=3), | |
| title="Fashion Mnist Classifier", | |
| description="fastai deployment in Gradio.", | |
| examples=examples, | |
| interpretation=interpretation, | |
| ).launch() |