Added cache function

app.py

@@ -5,34 +5,32 @@ import torch
 import disvae
 import transforms as trans
 
-P_MODEL = "models/btcvae_celeba"
+@st.cache_resource
+def load_decode_function():
+    P_MODEL = "models/btcvae_celeba"
 
-# Decode Funktion --------------------------------------------------
-sorter = trans.LatentSorter(disvae.get_kl_dict(P_MODEL))
-vae = disvae.load_model(P_MODEL)
-scaler = trans.MinMaxScaler(_min=torch.tensor([1.3]),_max=torch.tensor([4.0]),min_norm=0.3,max_norm=0.6)
-imaging = trans.SumField()
+    sorter = trans.LatentSorter(disvae.get_kl_dict(P_MODEL))
+    vae = disvae.load_model(P_MODEL)
 
-_dec = trans.sequential_function(
-    sorter.inv,
-    vae.decoder
-)
+    _dec = trans.sequential_function(
+        sorter.inv,
+        vae.decoder
+    )
 
-def decode(latent):
-    with torch.no_grad():
-        return trans.np_sample(_dec)(latent)
+    def decode(latent):
+        with torch.no_grad():
+            return trans.np_sample(_dec)(latent)
+    return decode
 
 # GUI -----------------------------------------------------------
 
+decode = load_decode_function()
+
 latent_vector = np.array([st.slider(f"L{l}",min_value=-3.0,max_value=3.0,value=0.0) for l in range(3)])
 latent_vector = np.concatenate([latent_vector,np.zeros(7)],axis=0)
 
 value = decode(latent_vector)
 
-value = np.swapaxes(np.swapaxes(value, 0, 2), 0, 1)# * 255
+value = np.swapaxes(np.swapaxes(value, 0, 2), 0, 1)
 
-# st.write(value)
 st.image(value, use_column_width="always")
-
-# x = st.slider("Select a value")
-# st.write(x, "squared is", x * x)