added code

gui.py

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+import streamlit as slt 
+import math
+import torch
+from torchvision.utils import make_grid
+from utils import norm,recover_image
+# it seems streamlit reruns the whole script when an event occurs
+from transformers import AutoModel
+if "generator" not in slt.session_state:
+    slt.session_state.generator=AutoModel.from_pretrained("hikmatfarhat/WGANGP_generator",trust_remote_code=True)
+slt.markdown("# Choose the number of images to generate from the sidebar")
+def display_images():
+    with torch.no_grad():
+        total=slt.session_state.size
+        rows=int(math.sqrt(total))
+        noise=torch.randn(total,128, 1, 1)
+        generator=slt.session_state.generator
+        fake_images=generator(noise)
+        res=make_grid(fake_images,nrow=rows,padding=2,normalize=True)
+        norm(res)
+        img=recover_image(res)
+        slt.session_state.image=img
+        #slt.image(img)
+if 'image' in slt.session_state:
+    slt.image(slt.session_state.image)
+
+slt.sidebar.selectbox("Select number of images",[16,32,64],key="size")
+
+slt.sidebar.button("Generate images",on_click=display_images)#,args=[slt.session_state.total])

requirements.txt

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+torch
+torchvision

utils.py

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+import torch
+import numpy as np
+from PIL import Image
+def norm(img):
+            low=float(img.min())
+            high=float(img.max())
+            img.sub_(low).div_(max(high - low, 1e-5))
+
+def recover_image(tensor):
+        tensor=tensor.cpu().numpy().transpose(1, 2,0)*255
+        
+        return Image.fromarray(tensor.astype(np.uint8))