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import gradio as gr | |
from fastai.vision.all import * | |
import skimage | |
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 = "Redacted Document Classifier" | |
description = "A classifier trained on publicly released redacted (and unredacted) FOIA documents, using fastai." | |
article = "<p style='text-align: center'><a href='https://mlops.systems/fastai/redactionmodel/computervision/datalabelling/2021/09/06/redaction-classification-chapter-2.html' target='_blank'>Blog post</a></p>" | |
examples = [ | |
"test1.jpg", | |
"test2.jpg", | |
"test3.jpg", | |
"test4.jpg", | |
"test5.jpg", | |
] | |
interpretation = "default" | |
enable_queue = True | |
theme = "dark-peach" | |
allow_flagging = "never" | |
gr.Interface( | |
fn=predict, | |
inputs=gr.inputs.Image(shape=(1024, 1024)), | |
outputs=gr.outputs.Label(num_top_classes=3), | |
title=title, | |
description=description, | |
article=article, | |
theme=theme, | |
allow_flagging=allow_flagging, | |
examples=examples, | |
interpretation=interpretation, | |
enable_queue=enable_queue, | |
).launch(share=True, cache_examples=True) | |