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| from fastai.vision.all import * | |
| import gradio as gr | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| learn = load_learner("model-v7.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))} | |
| examples = [ | |
| "a2_b_m.jpg", | |
| "a2_coeur_lyon.jpg", | |
| "gzup_america.jpg", | |
| "gzup_gameboy.jpg", | |
| "pa_425.jpg", | |
| "pa_1341.jpg", | |
| "stork_music.jpg", | |
| "stork_prince.jpg", | |
| ] | |
| DESCRIPTION = """ | |
| Street art deep learning model. It's trained to recognize the style of the following artists active in Paris: | |
| [A2](https://a2-streetart.com/), [gzup](https://www.gzup.fr/in-the-streets/), [invader](https://www.instagram.com/invaderwashere), [stork](https://www.instagram.com/stork_pixelart/). | |
| For more AI experiments check out my [newsletter](https://newsletter.pnote.eu) 💫. | |
| """ | |
| gr.Interface( | |
| fn=predict, | |
| inputs=gr.inputs.Image(shape=(512, 512)), | |
| outputs=gr.outputs.Label(num_top_classes=3), | |
| examples=examples, | |
| title="Art reco, recognizer of street art", | |
| description=DESCRIPTION | |
| ).launch(share=True) | |