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import gradio as gr
import skimage
from fastai.learner import load_learner
from fastai.vision.all import *
from huggingface_hub import hf_hub_download
learn = load_learner(
hf_hub_download("strickvl/redaction-classifier-fastai", "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 Image Classifier"
description = "A classifier trained on publicly released redacted (and unredacted) FOIA documents, using [fastai](https://github.com/fastai/fastai)."
with open("article.md") as f:
article = f.read()
examples = [
"test1.jpg",
"test2.jpg",
"test3.jpg",
"test4.jpg",
"test5.jpg",
]
interpretation = "default"
enable_queue = True
theme = "default"
allow_flagging = "never"
demo = 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,
)
demo.launch(
cache_examples=True,
enable_queue=enable_queue,
)
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