app.py

import gradio as gr
from PIL import Image, ImageOps, ImageStat
from transformers import pipeline

PIPE = pipeline(
    task="image-classification",
    model="kmewhort/beit-sketch-classifier",
    top_k=5,
)


def preprocess(image: Image.Image):
    if image is None:
        return None
    img = image.convert("L")
    # Ensure black strokes on white background
    if ImageStat.Stat(img).mean[0] < 128:
        img = ImageOps.invert(img)
    return img.convert("RGB")


def predict(image: Image.Image):
    img = preprocess(image)
    if img is None:
        return []
    return PIPE(img)


with gr.Blocks() as demo:
    gr.Markdown("# QuickDraw Sketch Classifier")
    inp = gr.Image(type="pil", label="Sketch")
    out = gr.JSON(label="Predictions")
    btn = gr.Button("Predict")
    btn.click(predict, inputs=inp, outputs=out, api_name="predict")

demo.launch()