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import gradio as gr
from fastai.vision.all import *
import skimage
def is_cat(x):
return x[0].isupper()
learn = load_learner('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 = "Pet Breed Classifier"
description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a 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 = ['siamese.jpg']
interpretation='default'
enable_queue=True
image = gr.Image(height=192, width=192)
label = gr.Label(num_top_classes=3)
examples = ['dog.jpg', 'cat.jpg', 'dogcat.jpg']
intf = gr.Interface(
fn=predict,
inputs=image,
outputs=label,
examples=examples,
title='Cat or Dog Classifier',
description='This is a cat or dog classifier. Upload an image of a cat or dog and it will predict which it is.'
)
intf.launch()
# gr.Interface(fn=predict,inputs=gr.components.Image(height=512, width=512),outputs=gr.components.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch() |