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 = "default"
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(cache_examples=True)