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| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| learn = load_learner("luxury_bag_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 = "Luxury Bag Classifier" | |
| description = ( | |
| "A luxury bag classifier trained on photos of a few brands. Created with resnet18 architecture." | |
| ) | |
| article = "<p style='text-align: center'><a href='https://www.kaggle.com/code/sellde/fastai-chapter-2' target='_blank'>Model Source</a></p>" | |
| examples = [["gucci.jpg"], ["chanel.jpg"], ["vuitton.jpg"]] | |
| interpretation = "default" | |
| enable_queue = True | |
| gr.Interface( | |
| fn=predict, | |
| inputs=gr.inputs.Image(shape=(512, 512)), | |
| outputs=gr.outputs.Label(), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| interpretation=interpretation, | |
| enable_queue=enable_queue, | |
| thumbnail="pineapple_bag.jpeg", | |
| ).launch() | |