Update app.py

app.py

@@ -21,9 +21,9 @@ sys.path.append("stylegan3")
 
 
 
-DESCRIPTION = f'''This model generates healthy MR Brain Images.
+DESCRIPTION = f''This model generates healthy MR Brain Images.
 ![Example]("https://huggingface.co/spaces/SerdarHelli/Brain-MR-Image-Generation-GAN/blob/main/ex.png")
-'''
+''
 
 def make_transform(translate: Tuple[float,float], angle: float):
     m = np.eye(3)
@@ -46,7 +46,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 G.eval()
 G.to(device)
 
-def predict(Seed,noise_mode,truncation_psi,trans_x,trans_y,angle):
+def predict(Seed,noise_mode,truncation_psi):
 
   # Generate images.
     z = torch.from_numpy(np.random.RandomState(Seed).randn(1, G.z_dim)).to(device)
@@ -55,10 +55,7 @@ def predict(Seed,noise_mode,truncation_psi,trans_x,trans_y,angle):
     # generator expects this matrix as an inverse to avoid potentially failing numerical
     # operations in the network.
     
-    if hasattr(G.synthesis, 'input'):
-        m = make_transform((trans_x,trans_y), angle)
-        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)
@@ -73,9 +70,6 @@ interface=gr.Interface(fn=predict, title="Brain MR Image Generation with StyleGA
                        article = "Author: S.Serdar Helli",
                        inputs=[gr.inputs.Slider( minimum=0, maximum=2**12,label='Seed'),gr.inputs.Radio( choices=noises,  default='const',label='Noise Mods'),
                                            gr.inputs.Slider(0, 2, step=0.05, default=1, label='Truncation psi'),
-                                           gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate X'),
-                                           gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate Y'),
-                                           gr.inputs.Slider(-180, 180, step=5, default=0, label='Angle'),],
                        outputs=gr.outputs.Image( type="numpy", label="Output"))