import pandas as pd
import numpy as np
import streamlit as st

from models import Generator, Discriminrator
from utils import image_to_base64
import torch
import torchvision.transforms as T
from torchvision.utils import make_grid
from PIL import Image

device = 'cuda' if torch.cuda.is_available() else 'cpu'


model_name = {
    "aurora": 'huggan/fastgan-few-shot-aurora-bs8',
    "painting": 'huggan/fastgan-few-shot-painting-bs8',
    "shell": 'huggan/fastgan-few-shot-shells',
    "fauvism": 'huggan/fastgan-few-shot-fauvism-still-life',
}

#@st.cache(allow_output_mutation=True)
def load_generator(model_name_or_path):
    generator = Generator(in_channels=256, out_channels=3)
    generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3)
    _ = generator.to('cuda')
    _ = generator.eval()

    return generator

def _denormalize(input: torch.Tensor) -> torch.Tensor:
    return (input * 127.5) + 127.5


def generate_images(generator, number_imgs):
    noise = torch.zeros(number_imgs, 256, 1, 1, device='cuda').normal_(0.0, 1.0)
    with torch.no_grad():
        gan_images, _ = generator(noise)

    gan_images = _denormalize(gan_images.detach()).cpu()
    gan_images = make_grid(gan_images, nrow=number_imgs, normalize=True)
    gan_images = gan_images.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
    gan_images = Image.fromarray(gan_images)
    return gan_images


def main():

    st.set_page_config(
        page_title="FastGAN Generator",
        page_icon="🖥️",
        layout="wide",
        initial_sidebar_state="expanded"
    )

    # st.sidebar.markdown(
    #     """
    # <style>
    # .aligncenter {
    #     text-align: center;
    # }
    # </style>
    # <p class="aligncenter">
    #     <img src="https://e7.pngegg.com/pngimages/510/121/png-clipart-machine-learning-deep-learning-artificial-intelligence-algorithm-machine-learning-angle-text.png"/>
    # </p>
    # """,
    #     unsafe_allow_html=True,
    # )
    st.sidebar.markdown(
        """
    ___
    <p style='text-align: center'>
    FastGAN is an few-shot GAN model that generates images of several types!
    </p>
    <p style='text-align: center'>
    Model training and Space creation by
    <br/>
    <a href="https://huggingface.co/vumichien" target="_blank">Chien Vu</a> | <a href="https://huggingface.co/geninhu" target="_blank">Nhu Hoang</a>
    <br/>
    </p>

    <p style='text-align: center'>
    <a href="https://github.com/silentz/Towards-Faster-And-Stabilized-GAN-Training-For-High-Fidelity-Few-Shot-Image-Synthesis" target="_blank">based on FastGAN model</a> | <a href="https://arxiv.org/abs/2101.04775" target="_blank">Article</a>
    </p>
            """,
        unsafe_allow_html=True,
    )

    st.header("Welcome to FastGAN")

    col1, col2, col3, col4 = st.columns([3,3,3,3])
    with col1:
        st.markdown('Fauvism GAN [model](https://huggingface.co/huggan/fastgan-few-shot-fauvism-still-life)', unsafe_allow_html=True)
        st.image('fauvism.png', width=300)

    with col2:
        st.markdown('Aurora GAN [model](https://huggingface.co/huggan/fastgan-few-shot-aurora-bs8)', unsafe_allow_html=True)
        st.image('aurora.png', width=300)

    with col3:
        st.markdown('Painting GAN [model](https://huggingface.co/huggan/fastgan-few-shot-painting-bs8)', unsafe_allow_html=True)
        st.image('painting.png', width=300)
    with col4:
        st.markdown('Shell GAN [model](https://huggingface.co/huggan/fastgan-few-shot-shells)', unsafe_allow_html=True)
        st.image('shell.png', width=300)

    # Choose generator
    col11, col12, col13 = st.columns([4,4,2])
    with col11:
        st.markdown('Choose type of image to generate', unsafe_allow_html=True)
        img_type = st.selectbox("", index=0, options=["shell", "aurora", "painting", "fauvism"])

    with col12:
        number_imgs = st.number_input('How many images you want to generate ?', min_value=1, max_value=5)
        if number_imgs is None:
            st.write('Invalid number ! Please insert number of images to generate !')
            raise ValueError('Invalid number ! Please insert number of images to generate !')
    with col13:
        generate_button = st.button('Get Image!')

    # row2 = st.columns([10])
    # with row2:
    if generate_button:
        st.markdown("""
            <small><i>Predictions may take up to 1mn under high load. Please stand by.</i></small>
        """,
        unsafe_allow_html=True,)
        generator = load_generator(model_name[img_type])
        gan_images = generate_images(generator, number_imgs)
        # margin = 0.1  # for better position of zoom in arrow
        # n_columns = 2
        # cols = st.columns([1] + [margin, 1] * (n_columns - 1))
        # for i, img in enumerate(gan_images):
        #     cols[(i % n_columns) * 2].image(img)

        st.image(gan_images, width=200*number_imgs)


if __name__ == '__main__':
    main()