Update app.py

app.py

@@ -12,18 +12,18 @@ def load_bert():
 
 tokenizer, model = load_bert()
 
-def calculate_similarity(word1, word2):
-def calculate_similarity(word1, word2):
-    # Tokenize and get embeddings
-    input_ids1 = torch.tensor([tokenizer.encode(word1, add_special_tokens=True)])
-    input_ids2 = torch.tensor([tokenizer.encode(word2, add_special_tokens=True)])
-
-    with torch.no_grad():
-        embeddings1 = model(input_ids1)[0][0, 0, :] 
-        embeddings2 = model(input_ids2)[0][0, 0, :]
-
-    cos_sim = torch.nn.functional.cosine_similarity(embeddings1, embeddings2, dim=0)
-    return cos_sim.item()  # Convert tensor to a float
+    def calculate_similarity(word1, word2):
+    def calculate_similarity(word1, word2):
+        # Tokenize and get embeddings
+        input_ids1 = torch.tensor([tokenizer.encode(word1, add_special_tokens=True)])
+        input_ids2 = torch.tensor([tokenizer.encode(word2, add_special_tokens=True)])
+    
+        with torch.no_grad():
+            embeddings1 = model(input_ids1)[0][0, 0, :] 
+            embeddings2 = model(input_ids2)[0][0, 0, :]
+    
+        cos_sim = torch.nn.functional.cosine_similarity(embeddings1, embeddings2, dim=0)
+        return cos_sim.item()  # Convert tensor to a float
 
 # Streamlit interface
 st.title("Word Similarity Checker")