Delete app.py

app.py

@@ -1,87 +0,0 @@
-import os
-import sys
-import re
-from typing import List, Optional, Tuple, Union
-import random
-
-sys.path.append('stylegan3-fun')  # change this to the path where dnnlib is located
-
-import numpy as np
-import PIL.Image
-import torch
-import streamlit as st
-import dnnlib
-import legacy
-
-
-def parse_range(s: Union[str, List]) -> List[int]:
-    '''Parse a comma separated list of numbers or ranges and return a list of ints.
-
-    Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
-    '''
-    if isinstance(s, list): return s
-    ranges = []
-    range_re = re.compile(r'^(\d+)-(\d+)$')
-    for p in s.split(','):
-        m = range_re.match(p)
-        if m:
-            ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
-        else:
-            ranges.append(int(p))
-    return ranges
-
-def make_transform(translate: Tuple[float,float], angle: float):
-    m = np.eye(3)
-    s = np.sin(angle/360.0*np.pi*2)
-    c = np.cos(angle/360.0*np.pi*2)
-    m[0][0] = c
-    m[0][1] = s
-    m[0][2] = translate[0]
-    m[1][0] = -s
-    m[1][1] = c
-    m[1][2] = translate[1]
-    return m
-
-def generate_image(network_pkl: str, seed: int, truncation_psi: float, noise_mode: str, translate: Tuple[float,float], rotate: float, class_idx: Optional[int]):
-    print('Loading networks from "%s"...' % network_pkl)
-    device = torch.device('cuda')
-    with open(network_pkl, 'rb') as f:
-        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
-
-    # Labels.
-    label = torch.zeros([1, G.c_dim], device=device)
-    if G.c_dim != 0:
-        if class_idx is None:
-            raise Exception('Must specify class label when using a conditional network')
-        label[:, class_idx] = 1
-
-    z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
-
-    if hasattr(G.synthesis, 'input'):
-        m = make_transform(translate, rotate)
-        m = np.linalg.inv(m)
-        G.synthesis.input.transform.copy_(torch.from_numpy(m))
-
-    img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
-    img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-    img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
-    return img
-
-def main():
-    st.title('Kpop Face Generator')
-
-    st.write('Press the button below to generate a new image:')
-    if st.button('Generate'):
-        network_pkl = 'kpopGG.pkl'
-        seed = random.randint(0, 99999)
-        truncation_psi = 0.45
-        noise_mode = 'const'
-        translate = (0.0, 0.0)
-        rotate = 0.0
-        class_idx = None
-
-        image = generate_image(network_pkl, seed, truncation_psi, noise_mode, translate, rotate, class_idx)
-        st.image(image)
-
-if __name__ == "__main__":
-    main()