Update app.py

app.py

@@ -23,7 +23,7 @@ bert_model.to(device)
 bert_model.eval()
 
 #SpaBERT Initialization Section
-data_file_path = 'models/spabert/datasets/SPABERT_finetuning_data_combined.json'
+data_file_path = 'models/spabert/datasets/SPABERT_finetuning_data_combined.json'                  #Make a new json file with only the geo entities needed, or it takes too long to run.
 pretrained_model_path = 'models/spabert/datasets/fine-spabert-base-uncased-finetuned-osm-mn.pth'
 
 config = SpatialBertConfig()
@@ -45,8 +45,8 @@ spatialDataset = PbfMapDataset(data_file_path = data_file_path,
                                         distance_norm_factor = 0.0001,
                                         spatial_dist_fill = 20,
                                         with_type = False,
-                                        sep_between_neighbors = True,    #Initially false, play around with this potentially?
-                                        label_encoder = None,             #Initially None, potentially change this because we do have real/fake reviews.
+                                        sep_between_neighbors = True,    
+                                        label_encoder = None,             
                                         mode = None)                      #If set to None it will use the full dataset for mlm
 
 data_loader = DataLoader(spatialDataset, batch_size=1, num_workers=0, shuffle=False, pin_memory=False, drop_last=False) #issue needs to be fixed with num_workers not stopping after finished
@@ -152,7 +152,10 @@ if st.button("Highlight Geo-Entities"):
         
         # Debug: Print the embeddings themselves (optional)
         st.write("Embeddings:", bert_embedding)
-        
+
+        combined_embedding = torch.cat((bert_embedding,all_embeddings[0]),dim=-1)
+        st.write("Concatenated Embedding Shape:", concatenated_embedding.shape)
+        st.write("Concatenated Embedding:", concatenated_embedding)
         # Process the text using spaCy
         doc = nlp(selected_review)