Update app.py

app.py

@@ -12,7 +12,7 @@ import gradio as gr
 sys.path.insert(0, "/StyleGAN2-GANbanales")
 
 import dnnlib
-#from StyleGAN2-GANbanales import dnnlib.tflib as tflib
+import dnnlib.tflib as tflib
 
 ##############################################################################
 # Generation functions
@@ -24,12 +24,12 @@ def seed2vec(Gs, seed):
 def init_random_state(Gs, seed):
     rnd = np.random.RandomState(seed) 
     noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
-    dnnlib.tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
+    tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
 
 def generate_image(Gs, z, truncation_psi, prefix="image", save=False, show=False):
     # Render images for dlatents initialized from random seeds.
     Gs_kwargs = {
-        'output_transform': dict(func=dnnlib.tflib.convert_images_to_uint8, nchw_to_nhwc=True),
+        'output_transform': dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True),
         'randomize_noise': False
     }
     if truncation_psi is not None:
@@ -119,7 +119,7 @@ def concatenate(img_array):
 def initiate(seed, n_imgs, text):
 
   pkl_file = "networks/experimento_2.pkl"
-  dnnlib.tflib.init_tf()
+  tflib.init_tf()
 
   with open(pkl_file, 'rb') as pickle_file:
     _G, _D, Gs = pickle.load(pickle_file)