import os
import sys
import torch
import gradio as gr
import numpy as np
import torchvision.transforms as transforms


from torch.autograd import Variable
from network.Transformer import Transformer

from PIL import Image

import logging

logger = logging.getLogger(__name__)

MAX_DIMENSION = 1280
MODEL_PATH = "models"
COLOUR_MODEL = "RGB"

STYLE_SHINKAI = "Makoto Shinkai"
STYLE_HOSODA = "Mamoru Hosoda"
STYLE_MIYAZAKI = "Hayao Miyazaki"
STYLE_KON = "Satoshi Kon"
DEFAULT_STYLE = STYLE_SHINKAI
STYLE_CHOICE_LIST = [STYLE_SHINKAI, STYLE_HOSODA, STYLE_MIYAZAKI, STYLE_KON]

shinkai_model = Transformer()
hosoda_model = Transformer()
miyazaki_model = Transformer()
kon_model = Transformer()


shinkai_model.load_state_dict(
    torch.load(os.path.join(MODEL_PATH, "shinkai_makoto.pth"))
)
hosoda_model.load_state_dict(
    torch.load(os.path.join(MODEL_PATH, "hosoda_mamoru.pth"))
)
miyazaki_model.load_state_dict(
    torch.load(os.path.join(MODEL_PATH, "miyazaki_hayao.pth"))
)
kon_model.load_state_dict(
    torch.load(os.path.join(MODEL_PATH, "kon_satoshi.pth"))
)

shinkai_model.eval()
hosoda_model.eval()
miyazaki_model.eval()
kon_model.eval()

enable_gpu = torch.cuda.is_available()


def get_model(style):
    if style == STYLE_SHINKAI:
        return shinkai_model
    elif style == STYLE_HOSODA:
        return hosoda_model
    elif style == STYLE_MIYAZAKI:
        return miyazaki_model
    elif style == STYLE_KON:
        return kon_model
    else:
        logger.warning(
            f"Style {style} not found. Defaulting to Makoto Shinkai"
        )
        return shinkai_model


def adjust_image_for_model(img):
    logger.info(f"Image Height: {img.height}, Image Width: {img.width}")
    if img.height > MAX_DIMENSION or img.width > MAX_DIMENSION:
        logger.info(f"Dimensions too large. Resizing to {MAX_DIMENSION}px.")
        img.thumbnail((MAX_DIMENSION, MAX_DIMENSION), Image.ANTIALIAS)

    return img


def inference(img, style):
    img = adjust_image_for_model(img)

    # load image
    input_image = img.convert(COLOUR_MODEL)
    input_image = np.asarray(input_image)
    # RGB -> BGR
    input_image = input_image[:, :, [2, 1, 0]]
    input_image = transforms.ToTensor()(input_image).unsqueeze(0)
    # preprocess, (-1, 1)
    input_image = -1 + 2 * input_image

    if enable_gpu:
        logger.info(f"CUDA found. Using GPU.")
        input_image = Variable(input_image).cuda()
    else:
        logger.info(f"CUDA not found. Using CPU.")
        input_image = Variable(input_image).float()

    # forward
    model = get_model(style)
    output_image = model(input_image)
    output_image = output_image[0]
    # BGR -> RGB
    output_image = output_image[[2, 1, 0], :, :]
    output_image = output_image.data.cpu().float() * 0.5 + 0.5

    return transforms.ToPILImage()(output_image)


title = "Anime Background GAN"
description = "Gradio Demo for CartoonGAN by Chen Et. Al. Models are Shinkai Makoto, Hosoda Mamoru, Kon Satoshi, and Miyazaki Hayao."
article = "<p style='text-align: center'><a href='http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2205.pdf' target='_blank'>CartoonGAN Whitepaper from Chen et.al</a></p><p style='text-align: center'><a href='https://github.com/venture-anime/cartoongan-pytorch' target='_blank'>Github Repo</a></p><p style='text-align: center'><a href='https://github.com/Yijunmaverick/CartoonGAN-Test-Pytorch-Torch' target='_blank'>Original Implementation from Yijunmaverick</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akiyamasho' alt='visitor badge'></center></p>"

examples = [
    ["examples/garden_in.jpg", STYLE_SHINKAI],
    ["examples/library_in.jpg", STYLE_KON],
]


gr.Interface(
    fn=inference,
    inputs=[
        gr.inputs.Image(
            type="pil",
            label="Input Photo (less than 1280px on both width and height)",
        ),
        gr.inputs.Dropdown(
            STYLE_CHOICE_LIST,
            type="value",
            default=DEFAULT_STYLE,
            label="Style",
        ),
    ],
    outputs=gr.outputs.Image(
        type="pil",
        label="Output Image",
    ),
    title=title,
    description=description,
    article=article,
    examples=examples,
    allow_flagging="never",
    allow_screenshot=False,
).launch(enable_queue=True)