Update app.py

app.py

@@ -52,22 +52,18 @@ def parse_args() -> argparse.Namespace:
                         action='store_false')
     parser.add_argument('--allow-flagging', type=str, default='never')
     return parser.parse_args()
-
-def load_model(file_name: str, path:str,device: torch.device) -> nn.Module:
-    path = hf_hub_download(f'{path}',
-                           f'{file_name}',
-                           use_auth_token=TOKEN)
-    with open(path, 'rb') as f:
-        model = torch.load(f)
-    model.eval()
-    model.to(device)
-    with torch.inference_mode():
-        z = torch.zeros((1, model.z_dim)).to(device)
-        label = torch.zeros([1, model.c_dim], device=device)
-        model(z, label, force_fp32=True)
-    return model
    
 def image_create(input_img):
+    args = ProjectorArguments().parse(
+        args=[str(input_path)],
+        namespace=Namespace(
+            spectral_sensitivity=spectral_sensitivity,
+            encoder_ckpt=f"checkpoint/encoder/checkpoint_{spectral_sensitivity}.pt",
+            encoder_name=spectral_sensitivity,
+            #gaussian=gaussian_radius,
+            log_visual_freq=1000,
+            input='text',
+        ))
     device = th.device()
     generator = create_generator("stylegan2-ffhq-config-f.pt","feng2022/Time-TravelRephotography_stylegan2-ffhq-config-f",args, device)
     latent = torch.randn((1, 512), device=device) 
@@ -82,33 +78,29 @@ def main():
     #else:
     #    ini = "False1"
     #result = subprocess.check_output(['nvidia-smi'])
-    #load_model("stylegan2-ffhq-config-f","feng2022/Time-TravelRephotography_stylegan2-ffhq-config-f",device)
-    args = ProjectorArguments().parse(
-        args=[str(input_path)],
-        namespace=Namespace(
-            spectral_sensitivity=spectral_sensitivity,
-            encoder_ckpt=f"checkpoint/encoder/checkpoint_{spectral_sensitivity}.pt",
-            encoder_name=spectral_sensitivity,
-            #gaussian=gaussian_radius,
-            log_visual_freq=1000,
-            input='text',
-        ))
+    args = parse_args()
     iface = gr.Interface(
       fn=image_create, 
-      inputs='text',
-      outputs='image',
+      [
+            gr.inputs.Number(default=0, label='Seed'),
+            gr.inputs.Slider(
+                0, 2, step=0.05, default=0.7, label='Truncation psi'),
+        ],
+      gr.outputs.Image(type='numpy', label='Output'),
       title=TITLE,
       description=DESCRIPTION,
       article=ARTICLE,
-        #theme=args.theme,
-        #allow_flagging=args.allow_flagging,
-        #live=args.live,
+       theme=args.theme,
+       allow_flagging=args.allow_flagging,
+       live=args.live,
     )
     
-    iface.launch()
-        #enable_queue=args.enable_queue,
-        #server_port=args.port,
-        #share=args.share,)
+    iface.launch(
+        enable_queue=args.enable_queue,
+        server_port=args.port,
+        share=args.share,
+        )
+        
 if __name__ == '__main__':
     main()