Update app.py

app.py

@@ -142,26 +142,7 @@ def processSpatialEntities(review, nlp):
     return processed_embedding
 
 
-#Discriminator Initialization section
-class Discriminator(nn.Module):
-    def __init__(self, input_size=512, hidden_sizes=[512], num_labels=2, dropout_rate=0.1):
-        super(Discriminator, self).__init__()
-        self.input_dropout = nn.Dropout(p=dropout_rate)
-        layers = []
-        hidden_sizes = [input_size] + hidden_sizes
-        for i in range(len(hidden_sizes)-1):
-            layers.extend([nn.Linear(hidden_sizes[i], hidden_sizes[i+1]), nn.LeakyReLU(0.2, inplace=True), nn.Dropout(dropout_rate)])
-
-        self.layers = nn.Sequential(*layers) #per il flatten
-        self.logit = nn.Linear(hidden_sizes[-1],num_labels+1) # +1 for the probability of this sample being fake/real.
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, input_rep):
-        input_rep = self.input_dropout(input_rep)
-        last_rep = self.layers(input_rep)
-        logits = self.logit(last_rep)
-        probs = self.softmax(logits)
-        return last_rep, logits, probs
+
 
 #dConfig = AutoConfig.from_pretrained("bert-base-uncased")
 #hidden_size = int(dConfig.hidden_size)