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import gradio as gr
from PIL import Image
from vit_model_test import CustomModel
import time
# Initialize the model
model = CustomModel()
def predict(image: Image.Image):
# 住讬诪讜诇爪讬讛 砖诇 讝诪谉 注讬讘讜讚 (诇诪砖诇 5 砖谞讬讜转)
time.sleep(5) # 讗驻砖专 诇砖谞讜转 讗转 讛讝诪谉 诇驻讬 讛爪讜专讱
label, confidence = model.predict(image)
result = "AI image" if label == 1 else "Real image"
return result, f"Confidence: {confidence:.2f}%"
# Define the Gradio interface
with gr.Blocks() as demo:
image_input = gr.Image(type="pil", label="Upload an image")
animation = gr.Video("https://cdn-uploads.huggingface.co/production/uploads/66d6f1b3b50e35e1709bfdf7/x7Ud8PO9QPfmrTvBVcCKE.mp4", visible=False) # 讛讜住驻转 讛讜讜讬讚讗讜
output_label = gr.Textbox(label="Classification Result", interactive=False)
output_confidence = gr.Textbox(label="Confidence", interactive=False)
def show_animation(image):
return animation.update(visible=True), "", "" # 诇讛爪讬讙 讗转 讛讗谞讬诪爪讬讛
def hide_animation(image):
result, confidence = predict(image)
return animation.update(visible=False), result, confidence # 诇讛住转讬专 讗转 讛讗谞讬诪爪讬讛
image_input.change(show_animation, inputs=image_input, outputs=[animation, output_label, output_confidence])
image_input.change(hide_animation, inputs=image_input, outputs=[animation, output_label, output_confidence])
# Launch the Gradio interface
demo.launch()
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