app.py

import gradio as gr
from PIL import Image
from vit_model_test import CustomModel

# Initialize the model
model = CustomModel()

def predict(image: Image.Image):
        label, confidence = model.predict(image)
        result = "AI image" if label == 1 else "Real image"
        return result, f"Confidence: {confidence:.2f}%"


    # Define the Gradio interface
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=[gr.Textbox(), gr.Textbox()],
title="Vision Transformer Model",
description="Upload an image to classify it using the Vision Transformer model.",
theme = gr.themes.Soft()
)

# Launch the Gradio interface
if __name__ == "__main__":
    demo.launch(share=True)