Update app.py

app.py

@@ -1,79 +1,32 @@
 import gradio as gr
-import os
 import numpy as np
-import torch
-import pickle
-import types
 
-from huggingface_hub import hf_hub_url, cached_download
+def initiate(images_num):
 
-TOKEN = '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'
-
-with open(cached_download(hf_hub_url('i72sijia/ganbanales', 'ganbanales.pkl'), use_auth_token=TOKEN), 'rb') as f:
-    G = pickle.load(f)['G_ema']# torch.nn.Module
-    
-device = torch.device("cpu")
-
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-    G = G.to(device)
-else:
-    _old_forward = G.forward
-
-    def _new_forward(self, *args, **kwargs):
-        kwargs["force_fp32"] = True
-        return _old_forward(*args, **kwargs)
-
-    G.forward = types.MethodType(_new_forward, G)
-
-    _old_synthesis_forward = G.synthesis.forward
+    zeros = np.zeros([256,256,3], dtype=np.uint8)
+    zeros.fill(255)
+        
+    img_array = []
+      
+    for i in range(images_num):
+        img_array.append(zeros)
+        
 
-    def _new_synthesis_forward(self, *args, **kwargs):
-        kwargs["force_fp32"] = True
-        return _old_synthesis_forward(*args, **kwargs)
 
-    G.synthesis.forward = types.MethodType(_new_synthesis_forward, G.synthesis)
-    
-####################################################################
-# Image generation    
 
-def generate(num_images, interpolate):
-    if interpolate:
-        z1 = torch.randn([1, G.z_dim])# latent codes
-        z2 = torch.randn([1, G.z_dim])# latent codes
-        zs = torch.cat([z1 + (z2 - z1) * i / (num_images-1) for i in range(num_images)], 0)
-    else:
-        zs = torch.randn([num_images, G.z_dim])# latent codes
-    with torch.no_grad():
-        zs = zs.to(device)
-        img = G(zs, None, force_fp32=True, noise_mode='const') 
-        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-    return img.cpu().numpy()
-
-####################################################################
-# Graphical User Interface
-def infer(num_images, interpolate):
-    img = generate(round(num_images), interpolate)
-    imgs = list(img)
-    return imgs
-    
 demo = gr.Blocks()
 
 with demo:
-    gr.Markdown(
-    """
-    # EmojiGAN
-    Generate Emojis with AI
-    """)
     images_num = gr.inputs.Slider(default=1, label="Num Images", minimum=1, maximum=16, step=1)
-    interpolate = gr.inputs.Checkbox(default=False, label="Interpolate")
+    #interpolate = gr.inputs.Checkbox(default=False, label="Interpolate")
     
     submit = gr.Button("Generate")
         
     out = gr.Gallery()
 
     submit.click(fn=initiate, 
-               inputs=[images_num, interpolate], 
+               #inputs=[images_num, interpolate],
+               inputs=images_num
                outputs=out)
 
 demo.launch(enable_queue =True)