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Upload app.py
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app.py
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import numpy as np
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import gradio as gr
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from huggingface_hub import from_pretrained_fastai
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from lime import lime_image
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from skimage.segmentation import mark_boundaries
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learn = from_pretrained_fastai('mindwrapped/pokemon-card-checker')
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def check_card(img):
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pred_label, _, scores = learn.predict(img)
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scores = scores.detach().numpy()
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scores = {'real': float(scores[1]), 'fake': float(scores[0])}
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print(np.array(img).shape)
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# Lime Explanation
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explainer = lime_image.LimeImageExplainer()
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explanation = explainer.explain_instance(
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np.array(img),
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classifier_fn=classify_cards,
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labels=['0', '1'],
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num_samples=1000,
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random_seed=42,
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)
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temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=10, hide_rest=False)
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img_boundry = mark_boundaries(temp/255.0, mask)
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return scores, img_boundry
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def classify_cards(imgs):
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print(imgs.shape)
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scores = []
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for i in range(imgs.shape[0]):
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pred_label, _, score = learn.predict(imgs[i])
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scores.append(score.detach().numpy())
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scores = np.array(scores)
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print(scores.shape)
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return scores
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demo = gr.Interface(
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fn=check_card,
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inputs='image',
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outputs=["label", "image"],
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examples=['real-1.jpeg','real-2.jpeg','fake-1.jpeg','fake-2.jpeg','real-3.jpeg','real-4.jpeg','fake-3.jpeg','fake-4.jpeg'],
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title='Pokemon Card Checker',
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description='This space uses a resnet34 model fine-tuned to determine whether Pokemon cards are real or fake. \n\nAdded [LIME](https://github.com/marcotcr/lime) to show what contributed to the predicted label (green shows what contributed towards that label and red shows what contributed against the label predicted).\n\n[Dataset](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards) created by [Shujian Ong](https://www.kaggle.com/ongshujian).',
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article='Can you guess which cards are real and fake? \n\nI can\'t 🤔 \n\n([View Labels](https://gist.github.com/mindwrapped/e5aad747757ef006037a1a1982be34fc)) \n\nFeel free to like if you like it. \n\n',
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live=False,
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)
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demo.launch(debug=True)
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