app.py

import gradio as gr
from transformers import pipeline

pipe = pipeline("image-classification", "trpakov/vit-pneumonia")


def classify_image(image):
    outputs = pipe(image)
    outputs = {
        x["label"]: x["score"] for x in sorted(outputs, key=lambda x: x["label"])
    }
    return outputs


with gr.Blocks(
    title="ViT Chest X-ray Classification",
) as demo:
    gr.Markdown("# ViT Chest X-ray Pneumonia Classification")
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                "Classify chest x-ray scans as either having or not having pneumonia"
            )
            input_image = gr.Image(type="pil")
            classify_button = gr.Button("Classify!")
        with gr.Column():
            output_label = gr.Label(label="Probabilities", num_top_classes=2)

    with gr.Row():
        gr.Examples(
            "./samples",
            inputs=input_image,
            outputs=output_label,
            cache_examples=True,
            fn=classify_image,
            run_on_click=True,
        )

    classify_button.click(fn=classify_image, inputs=input_image, outputs=output_label)


demo.launch(debug=True, enable_queue=True)