import PIL.Image
import PIL.ImageOps
import gradio as gr
import numpy as np
import tensorflow as tf

from poser import draw_bones, movenet


def predict(image: PIL.Image):
    input_size = 256
    size = (1280, 1280)
    image = PIL.ImageOps.fit(image, size, PIL.Image.LANCZOS)
    image_tf = tf.keras.preprocessing.image.img_to_array(image)
    # Resize and pad the image to keep the aspect ratio and fit the expected size.
    input_image = tf.expand_dims(image_tf, axis=0)
    input_image = tf.image.resize_with_pad(input_image, input_size, input_size)
    keypoints = movenet(input_image)
    keypoints = np.array(keypoints)
    image = tf.keras.preprocessing.image.array_to_img(image_tf)
    draw_bones(image, keypoints)
    return image


footer = r"""
<center>
<b>
Demo for <a href='https://www.tensorflow.org/hub/tutorials/movenet'>MoveNet</a>
</b>
</center>
"""

with gr.Blocks(title="MoveNet") as app:
    gr.HTML("<center><h1>Human Pose Estimation with MoveNet</h1></center>")
    gr.HTML("<center><h3>MoveNet: Ultra fast and accurate pose detection model</h3></center>")
    with gr.Row().style(equal_height=False):
        with gr.Column():
            input_img = gr.Image(type="pil", label="Input image")
            run_btn = gr.Button(variant="primary")
        with gr.Column():
            output_img = gr.Image(type="numpy", label="Output image")
            gr.ClearButton(components=[input_img, output_img], variant="stop")

    run_btn.click(predict, [input_img], [output_img])

    with gr.Row():
        blobs = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
        examples = gr.Dataset(components=[input_img], samples=blobs)
        examples.click(lambda x: x[0], [examples], [input_img])

    with gr.Row():
        gr.HTML(footer)

app.launch(share=False, debug=True, show_error=True)
app.queue()