#!/usr/bin/env python

import gradio as gr

from model import Model
from settings import CACHE_EXAMPLES, MAX_SEED
from utils import randomize_seed_fn

caption = gr.load(name="spaces/fffiloni/CoCa-clone")

def create_image_caption(image_init):
    cap = caption(image_init, "Beam search", 1.2, 0.5, 5, 20, fn_index=0)
    print("cap: " + cap)
    return cap, gr.update(visible=True)

def create_demo(model: Model) -> gr.Blocks:

    with gr.Blocks() as demo:
        with gr.Box():
            with gr.Row(elem_id='prompt-container'):
                image_init = gr.Image(type="filepath", source="upload")
                prompt = gr.Text(
                    label='Prompt',
                    show_label=False,
                    max_lines=1,
                    placeholder='Enter your prompt',
                    visible=False).style(container=False)
                run_button = gr.Button('Run').style(full_width=False)
            result = gr.Model3D(label='Result', show_label=False)
            with gr.Accordion('Advanced options', open=False):
                seed = gr.Slider(label='Seed',
                                 minimum=0,
                                 maximum=MAX_SEED,
                                 step=1,
                                 value=0)
                randomize_seed = gr.Checkbox(label='Randomize seed',
                                             value=True)
                guidance_scale = gr.Slider(label='Guidance scale',
                                           minimum=1,
                                           maximum=20,
                                           step=0.1,
                                           value=15.0)
                num_inference_steps = gr.Slider(
                    label='Number of inference steps',
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=64)

        
        image_init.change(fn=create_image_caption, inputs=[image_init], outputs=[prompt, run_button])
        inputs = [
            prompt,
            seed,
            guidance_scale,
            num_inference_steps,
        ]
        prompt.submit(
            fn=randomize_seed_fn,
            inputs=[seed, randomize_seed],
            outputs=seed,
            queue=False,
        ).then(
            fn=model.run_text,
            inputs=inputs,
            outputs=result,
        )
        run_button.click(
            fn=randomize_seed_fn,
            inputs=[seed, randomize_seed],
            outputs=seed,
            queue=False,
        ).then(
            fn=model.run_text,
            inputs=inputs,
            outputs=result,
            api_name='text-to-3d',
        )
    return demo