app.py

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,
)