import gradio as gr

def single_inference():
    pass

def blended_inference():
    pass

TITLE = """MKG Analogy"""

with gr.Blocks() as block:
    with gr.Column(elem_id="col-container"):
        gr.HTML(TITLE)

        with gr.Tab("Single Analogical Reasoning"):
            with gr.Row():
                gr.Markdown(""" $(I_h, I_t) : (T_q, ?)$
                            """)
                with gr.Column():
                    head_image = gr.Image(type='pil', label="Head Image")
                    head_ent = gr.Textbox(lines=1, label="Head Entity")
                with gr.Column():
                    tail_image = gr.Image(type='pil', label="Tail Image")
                    tail_ent = gr.Textbox(lines=1, label="Tail Entity")
                with gr.Column():
                    question_text = gr.Textbox(lines=1, label="Question Name")
                    question_ent = gr.Textbox(lines=1, label="Question Entity")
            submit_btn = gr.Button("Submit")
            output_text = gr.Textbox(label="Output")

            # examples=[['example01.jpg', MODELS[0], 'best'], ['example02.jpg', MODELS[0], 'best']]
            # ex = gr.Examples(
            #     examples=examples, 
            #     fn=image_to_prompt, 
            #     inputs=[input_image, input_model, input_mode], 
            #     outputs=[output_text, share_button, community_icon, loading_icon], 
            #     cache_examples=True, 
            #     run_on_click=True
            # )
            # ex.dataset.headers = [""]

        with gr.Tab("Blended Analogical Reasoning"):
            pass
        
        # gr.HTML(ARTICLE)

    # submit_btn.click(
    #     fn=image_to_prompt, 
    #     inputs=[input_image, input_model, input_mode], 
    #     outputs=[output_text, share_button, community_icon, loading_icon]
    # )
    # share_button.click(None, [], [], _js=None)

block.queue(max_size=64).launch(enable_queue=True)