import gradio as gr
from transformers import AutoImageProcessor, ResNetForImageClassification
import torch

from gradio.themes.utils import sizes


theme = gr.themes.Default(radius_size=sizes.radius_none).set(
    block_label_text_color = '#4D63FF',
    block_title_text_color = '#4D63FF',
    button_primary_text_color = '#4D63FF',
    button_primary_background_fill='#FFFFFF',
    button_primary_border_color='#4D63FF',
    button_primary_background_fill_hover='#EDEFFF',
)

processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")

def image_classification(image):
    inputs = processor(image, return_tensors="pt")
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_label = logits.argmax(-1).item()

    return model.config.id2label[predicted_label]

demo = gr.Interface(fn=image_classification, 
                    inputs=gr.Image(), 
                    outputs=gr.Label(num_top_classes=1),
		    css = "footer {visibility: hidden}", 
		    allow_flagging = "never",
                    theme = theme,
                    examples = ['dog.jpeg'])


if __name__ == "__main__":
    demo.queue(concurrency_count=10)
    demo.launch(server_name = "0.0.0.0")