import gradio as gr
import func as fu




def clear_all(image1, image2, output_image, output_json):
  return None, None, None, None

with gr.Blocks() as demo:
    gr.Markdown("Choose or upload a dog image and press cpmpare!! the system will retutn the 2 detected faces with the recognition result")
    with gr.Row():
        with gr.Column():
            image1_input = gr.Image(type="pil", label="Image 1")
            examples_image1 = gr.Examples(examples=["./images/dob1.jpg", "./images/p1.jpg", "./images/dob3.jpg", "./images/d1.jpg"], inputs=image1_input)
        with gr.Column():
            image2_input = gr.Image(type="pil", label="Image 2")
            examples_image2 = gr.Examples(examples=["./images/dob2.jpg", "./images/p2.jpg", "./images/dob4.jpg", "./images/d2.jpg"], inputs=image2_input)
    threshold_input = gr.Slider(minimum=0.0, maximum=1.0, value=0.55, step=0.05, label="Threshold")
#    detect_input =  gr.Radio(["Yes", "No"], label="Detect dog face", info="Detect dog face on compare!"),
    compare_button = gr.Button("Compare")
    clear_button = gr.Button("Clear")

    output_image = gr.Image(type="pil", label="Stacked Image", interactive=False)
    output_json = gr.JSON(label="Result")

    compare_button.click(
        fn=fu.compare_faces,
        inputs=[image1_input, image2_input, threshold_input],
        outputs=[output_image, output_json]
    )
 
    clear_button.click(
      fn=clear_all,
      inputs=[image1_input, image2_input, output_image, output_json],
      outputs=[image1_input, image2_input,output_image, output_json]
    )


demo.launch()