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}%"

def video_identity(video):
    return video

demo = gr.Interface(video_identity,
                    gr.Video(),
                    "https://cdn-uploads.huggingface.co/production/uploads/66d6f1b3b50e35e1709bfdf7/x7Ud8PO9QPfmrTvBVcCKE.mp4",
                    )
    

# 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."
)

# Launch the Gradio interface
demo.launch()