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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):
print("Predict function called") # 讛讜住驻转 砖讜专转 讛讚驻住讛
time.sleep(5) # 住讬诪讜诇爪讬讛 砖诇 讝诪谉 注讬讘讜讚
label, confidence = model.predict(image)
result = "AI image" if label == 1 else "Real image"
return result, f"Confidence: {confidence:.2f}%"
def loading_animation(image):
return gr.Video.update(visible=True), "", ""
def show_results(image):
result, confidence = predict(image)
return gr.Video.update(visible=False), result, confidence
# 讬爪讬专转 诪诪砖拽 Gradio 注诐 讗谞讬诪爪讬讜转
with gr.Blocks(css="""
.gr-button {
background-color: #4CAF50;
color: white;
transition: background-color 0.3s ease;
}
.gr-button:hover {
background-color: #45a049;
}
""") as demo:
with gr.Row():
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)
output_label = gr.Textbox(label="Classification Result", interactive=False, visible=False)
output_confidence = gr.Textbox(label="Confidence", interactive=False, visible=False)
image_input.change(loading_animation, inputs=image_input, outputs=[animation, output_label, output_confidence])
image_input.change(show_results, inputs=image_input, outputs=[animation, output_label, output_confidence])
# 讛讜住驻转 讻驻转讜专
submit_button = gr.Button("Submit")
submit_button.click(predict, inputs=image_input, outputs=[output_label, output_confidence])
# 讛砖拽转 讛诪诪砖拽
demo.launch()
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