Delete app.py

app.py

@@ -1,140 +0,0 @@
-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()