Update app.py

app.py

@@ -8,7 +8,6 @@ from models.spabert.models.spatial_bert_model import SpatialBertConfig, SpatialB
 from models.spabert.utils.common_utils import load_spatial_bert_pretrained_weights
 from models.spabert.datasets.osm_sample_loader import PbfMapDataset
 from torch.utils.data import DataLoader
-
 from PIL import Image
 
 device = torch.device('cpu')
@@ -117,10 +116,10 @@ def get_bert_embedding(review_text):
 def get_spaBert_embedding(entity):
     entity_index = entity_index_dict.get(entity.lower(), None)
     if entity_index is None:
-        st.write("Got Bert embedding for: ", entity)
+        #st.write("Got Bert embedding for: ", entity)
         return get_bert_embedding(entity)                            #Fallback in-case SpaBERT could not resolve entity to retrieve embedding. Rare-cases only.
     else:
-        st.write("Got SpaBert embedding for: ", entity)
+        #st.write("Got SpaBert embedding for: ", entity)
         return spaBERT_embeddings[entity_index]
 
         
@@ -143,6 +142,37 @@ 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)
+#num_hidden_layers_d = 2; 
+#hidden_levels_d = [hidden_size for i in range(0, num_hidden_layers_d)]
+#label_list = ["1", "0"]
+#label_list.append('UNL')
+#discriminator = Discriminator(input_size=hidden_size*2, hidden_sizes=hidden_levels_d, num_labels=len(label_list), dropout_rate=out_dropout_rate).to(device)
+
+
+
 # Function to read reviews from a text file
 def load_reviews_from_file(file_path):
     reviews = {}