import gradio as gr | |
from fastai.vision.all import * | |
import skimage | |
learn = load_learner('export.pkl') | |
categories = ('Lego Ninjago','Lego (non Ninjago)') | |
def predict(img): | |
img = PILImage.create(img) | |
pred, pred_idx, probs = learn.predict(img) | |
return dict(zip(categories, map(float,probs))) | |
title = "Lego Classifier" | |
description = "Classifies Lego into 'Ninjago' and 'Non Ninjago' with fastai. Created from the fastai 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 = ['ninjago.jpg', 'lego_nn.jpg'] | |
interpretation = 'default' | |
enable_queue = True | |
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=2), title=title, | |
description=description, examples=examples, interpretation=interpretation, enable_queue=enable_queue).launch() | |