matyuss/DLapp

1import gradio as gr2from fastai.vision.all import *3 4 5 6def is_cat(x): return x[0].isupper() 7 8learn = load_learner('model.pkl')9 10categories = ('Dota 2', 'League of Legends')11 12 13 14def classify_image(img):15    pred,idx, probs = learn.predict(img)16    return dict(zip(categories,map(float,probs)))17 18image = gr.inputs.Image(shape=(214,214))19label = gr.outputs.Label()20examples = ['download.jpg']21 22intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)23intf.launch(inline=False)24# title = "Pet Breed Classifier"25# description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."26# article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"27# examples = ['siamese.jpg']28# interpretation = 'default'29# enable_queue = True30 31# gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3), title=title,32#              description=description, article=article, examples=examples, interpretation=interpretation, enable_queue=enable_queue).launch()33