import gradio as gr
from transformers import BeitImageProcessor, BeitForImageClassification


def inference(img):
    pretrained_model_path = "beit-base-patch16-224-pt22k-ft22k"

    processor = BeitImageProcessor.from_pretrained(pretrained_model_path)
    model = BeitForImageClassification.from_pretrained(pretrained_model_path)

    inputs = processor(images=img, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    # model predicts one of the 21,841 ImageNet-22k classes
    predicted_class_idx = logits.argmax(-1).item()
    # print("Predicted class:", model.config.id2label[predicted_class_idx])

    return model.config.id2label[predicted_class_idx]


title = "Image classification:beit-base-patch16-224-pt22k-ft22k"
description = "Gradio Demo for beit-base-patch16-224-pt22k-ft22k. To use it, simply upload your image, or click one of the examples to load them."
article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
examples = [['example_cat.jpg'], ['Masahiro.png']]

demo = gr.Interface(
    fn=inference,
    inputs=[gr.inputs.Image(type="pil")],
    outputs=gr.outputs.Textbox(),
    title=title,
    description=description,
    article=article,
    examples=examples)

demo.launch()