| import gradio as gr | |
| from fastai.vision.all import * | |
| import pathlib | |
| plt = platform.system() | |
| if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath | |
| learn = load_learner('model.pkl') | |
| labels = learn.dls.vocab | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred,pred_idx,probs = learn.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| title = "Shirt Fit Classifier" | |
| description = "A Loose vs. Fitted shirt classifier" | |
| article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>" | |
| examples = ['demo1.jpg', 'demo2.jpg', 'demo3.jpg', 'demo4.jpg'] | |
| interpretation='default' | |
| enable_queue=True | |
| gr.Interface(fn=predict, | |
| inputs=gr.inputs.Image(shape=(300, 300)), | |
| outputs=gr.outputs.Label(num_top_classes=2), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| interpretation=interpretation, | |
| enable_queue=enable_queue).launch() |