app.py

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

from streamlit_lottie import st_lottie
import requests

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


model_name = {
    "aurora": 'huggan/fastgan-few-shot-aurora',
    "painting": 'huggan/fastgan-few-shot-painting',
    "shell": 'huggan/fastgan-few-shot-shells',
    "fauvism": 'huggan/fastgan-few-shot-fauvism-still-life',
    "universe": 'huggan/fastgan-few-shot-universe',
    "grumpy cat": 'huggan/fastgan-few-shot-grumpy-cat',
    "anime": 'huggan/fastgan-few-shot-anime-face',
}

#@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(device)
    _ = 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=device).normal_(0.0, 1.0)
    with torch.no_grad():
        gan_images, _ = generator(noise)

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

def load_lottieurl(url: str):
    r = requests.get(url)
    if r.status_code != 200:
        return None
    return r.json()

def show_model_summary(expanded):
    with st.expander("Model gallery", expanded=expanded):
        col1, col2, col3, col4 = st.columns(4)
        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=200)

            st.markdown('Painting GAN [model](https://huggingface.co/huggan/fastgan-few-shot-painting)', unsafe_allow_html=True)
            st.image('painting.png', width=200)

        with col2:
            st.markdown('Aurora GAN [model](https://huggingface.co/huggan/fastgan-few-shot-aurora)', unsafe_allow_html=True)
            st.image('aurora.png', width=200)

            st.markdown('Universe GAN [model](https://huggingface.co/huggan/fastgan-few-shot-universe)', unsafe_allow_html=True)
            st.image('universe.png', width=200)

        with col3:
            st.markdown('Anime GAN [model](https://huggingface.co/huggan/fastgan-few-shot-anime-face)', unsafe_allow_html=True)
            st.image('anime.png', width=200)

            st.markdown('Shell GAN [model](https://huggingface.co/huggan/fastgan-few-shot-shells)', unsafe_allow_html=True)
            st.image('shell.png', width=200)

        with col4:
            st.markdown('Grumpy cat GAN [model](https://huggingface.co/huggan/fastgan-few-shot-grumpy-cat)', unsafe_allow_html=True)
            st.image('grumpy_cat.png', width=200)


def main():

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

    lottie_penguin = load_lottieurl('https://assets7.lottiefiles.com/packages/lf20_mm4bsl3l.json')

    with st.sidebar:
        st_lottie(lottie_penguin, height=200)
    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'>
    based on
    <br/>
    <a href="https://github.com/silentz/Towards-Faster-And-Stabilized-GAN-Training-For-High-Fidelity-Few-Shot-Image-Synthesis" target="_blank">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")
    checked = st.checkbox('Click here if you want to create one of your own !')

    if not checked:
        show_model_summary(True)
    if checked:
        col11, col12, col13 = st.columns([3,3.5,3.5])
        with col11:
            img_type = st.selectbox("Choose type of image to generate", index=0,
                                    options=["aurora", "anime", "painting", "fauvism", "shell", "universe", "grumpy cat"])
        # with col12:
            number_imgs = st.slider('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 !')

            generate_button = st.button('Get Image')
            if generate_button:
                st.markdown("""
                    <small><i>Predictions may take up to 1 minute under high load. Please stand by.</i></small>
                """,
                unsafe_allow_html=True,)

        if generate_button:
            generator = load_generator(model_name[img_type])
            gan_images = generate_images(generator, number_imgs)
            with col12:
                st.image(gan_images[0], width=300)
            if len(gan_images) > 1:
                with col13:
                    if len(gan_images) <= 2:
                        st.image(gan_images[1], width=300)
                    else:
                        st.image(gan_images[1:], width=150)



if __name__ == '__main__':
    main()