import random

import gradio as gr
import huggingface_hub
import imageio
import numpy as np
import onnxruntime as rt
from numpy.random import RandomState
from skimage import transform


class Model:
    def __init__(self):
        self.g_synthesis = None
        self.g_mapping = None
        self.load_models()

    def load_models(self):
        providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
        g_mapping_path = huggingface_hub.hf_hub_download("skytnt/waifu-gan", "g_mapping.onnx")
        g_synthesis_path = huggingface_hub.hf_hub_download("skytnt/waifu-gan", "g_synthesis.onnx")
        self.g_mapping = rt.InferenceSession(g_mapping_path, providers=providers)
        self.g_synthesis = rt.InferenceSession(g_synthesis_path, providers=providers)

    def get_img(self, w):
        img = self.g_synthesis.run(None, {'w': w})[0]
        return (img.transpose(0, 2, 3, 1) * 127.5 + 128).clip(0, 255).astype(np.uint8)[0]

    def get_w(self, z, psi1, psi2):
        return self.g_mapping.run(None, {'z': z, 'psi': np.asarray([psi1, psi2], dtype=np.float32)})[0]

    def gen_video(self, w1, w2, path, frame_num=10):
        video = imageio.get_writer(path, mode='I', fps=frame_num // 2, codec='libx264', bitrate='16M')
        lin = np.linspace(0, 1, frame_num)
        for i in range(0, frame_num):
            img = self.get_img(((1 - lin[i]) * w1) + (lin[i] * w2))
            video.append_data(img)
        video.close()


def get_thumbnail(img):
    img_new = np.full((192, 288, 3), 200, dtype=np.uint8)
    img_new[:, 80:208] = transform.resize(img, (192, 128), preserve_range=True)
    return img_new


def gen_fn(method, seed, psi1, psi2):
    if method == 0:
        seed = random.randint(0, 2 ** 32 -1)
    z = RandomState(int(seed)).randn(1, 1024)
    w = model.get_w(z.astype(dtype=np.float32), psi1, psi2)
    img_out = model.get_img(w)
    return img_out, seed, w, get_thumbnail(img_out)


def gen_video_fn(w1, w2, frame):
    if w1 is None or w2 is None:
        return None
    model.gen_video(w1, w2, "video.mp4", int(frame))
    return "video.mp4"


if __name__ == '__main__':
    model = Model()

    app = gr.Blocks()
    with app:
        gr.Markdown("# Waifu GAN\n\n"
                    "![visitor badge](https://api.visitorbadge.io/api/visitors?path=skytnt.waifu-gan&countColor=%23263759&style=flat&labelStyle=lower)\n\n")
        with gr.Tabs():
            with gr.TabItem("generate image"):
                with gr.Row():
                    with gr.Column():
                        with gr.Row():
                            gen_input1 = gr.Radio(label="method", value="random",
                                                  choices=["random", "use seed"], type="index")
                            gen_input2 = gr.Slider(minimum=0, maximum=2 ** 32 - 1, step=1, value=0,
                                                   label="seed")
                        gen_input3 = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="truncation psi 1")
                        gen_input4 = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="truncation psi 2")
                        with gr.Group():
                            gen_submit = gr.Button("Generate", variant="primary")
                    with gr.Column():
                        gen_output1 = gr.Image(label="output image")
                        select_img_input_w1 = gr.State()
                        select_img_input_img1 = gr.State()

            with gr.TabItem("generate video"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("## generate video between 2 images")
                        with gr.Row():
                            with gr.Column():
                                gr.Markdown("please select image 1")
                                select_img1_dropdown = gr.Radio(label="source", value="current generated image",
                                                                choices=["current generated image"], type="index")
                                with gr.Group():
                                    select_img1_button = gr.Button("Select", variant="primary")
                                select_img1_output_img = gr.Image(label="selected image 1")
                                select_img1_output_w = gr.State()
                            with gr.Column():
                                gr.Markdown("please select image 2")
                                select_img2_dropdown = gr.Radio(label="source", value="current generated image",
                                                                choices=["current generated image"], type="index")
                                with gr.Group():
                                    select_img2_button = gr.Button("Select", variant="primary")
                                select_img2_output_img = gr.Image(label="selected image 2")
                                select_img2_output_w = gr.State()
                        generate_video_frame = gr.Slider(minimum=10, maximum=30, step=1, label="frame", value=15)
                        with gr.Group():
                            generate_video_button = gr.Button("Generate", variant="primary")
                    with gr.Column():
                        generate_video_output = gr.Video(label="output video")
        gen_submit.click(gen_fn, [gen_input1, gen_input2, gen_input3, gen_input4],
                         [gen_output1, gen_input2, select_img_input_w1, select_img_input_img1])
        select_img1_button.click(lambda i, img1, w1: (img1, w1),
                                 [select_img1_dropdown, select_img_input_img1, select_img_input_w1],
                                 [select_img1_output_img, select_img1_output_w])
        select_img2_button.click(lambda i, img1, w1: (img1, w1),
                                 [select_img2_dropdown, select_img_input_img1, select_img_input_w1],
                                 [select_img2_output_img, select_img2_output_w])
        generate_video_button.click(gen_video_fn,
                                    [select_img1_output_w, select_img2_output_w, generate_video_frame],
                                    [generate_video_output])
    app.launch()