app.py

import gradio as gr
from transformers import pipeline
from pathlib import Path

pipeline = pipeline(task="image-classification", model="acidtib/tcg-magic-cards")

sample_img_paths = [str(p) for p in Path('./samples').glob('*.png')]

def predict(input_img):
    predictions = pipeline(input_img)
    return input_img, {p["label"]: p["score"] for p in predictions} 

gradio_app = gr.Interface(
    predict,
    inputs=gr.Image(label="Select Magic Card", sources=['upload', 'webcam'], type="pil"),
    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=1)],
    examples=sample_img_paths,
    title="TCG Magic Classifier",
    description='This classifier returns the id of cards from scryfall!, try one of the samples below'
)

if __name__ == "__main__":
    gradio_app.launch()