| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| learn = load_learner('export.pkl') | |
| categories = ('Lego Ninjago','Lego (non Ninjago)') | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred, pred_idx, probs = learn.predict(img) | |
| return dict(zip(categories, map(float,probs))) | |
| title = "Lego Classifier" | |
| description = "Classifies Lego into 'Ninjago' and 'Non Ninjago' with fastai. Created from the fastai demo for Gradio and HuggingFace Spaces." | |
| #article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>" | |
| examples = ['ninjago.jpg', 'lego_nn.jpg'] | |
| interpretation = 'default' | |
| enable_queue = True | |
| gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=2), title=title, | |
| description=description, examples=examples, interpretation=interpretation, enable_queue=enable_queue).launch() | |