Update app.py

app.py

@@ -20,31 +20,30 @@ dataset_names = [
 datasets = {name: load_dataset(name) for name in dataset_names}
 
 # Load SentenceTransformer model
-model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+sentence_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 
 # Define sentences
 sentences = [
     "The firewall successfully blocked unauthorized access attempts.",
     "The system detected a potential phishing attack targeting users.",
     "Regular software updates are essential to patch known vulnerabilities.",
-    "Implementing multi-factor authentication enhances account security."
+    "Implementing multi-factor authentication enhances account security.",
     "The function returns the sum of two numbers.",
     "A list comprehension provides a concise way to create lists.",
     "The 'try' block is used to handle exceptions in Python.",
     "Using 'lambda' allows for the creation of anonymous functions."
 ]
 
-
 # Compute sentence embeddings
-embeddings = model.encode(sentences)
+embeddings = sentence_model.encode(sentences)
 
 # Calculate cosine similarity between sentence embeddings
 similarities = cosine_similarity(embeddings)
 
 # Print similarity matrix shape and values
-print(similarities.shape)  # Expected output: (4, 4)
+print(similarities.shape)  # Expected output: (8, 8)
 print(similarities)
 
 # Load transformer model for Seq2Seq tasks
 tokenizer = AutoTokenizer.from_pretrained("cssupport/t5-small-awesome-text-to-sql")
-model = AutoModelForSeq2SeqLM.from_pretrained("cssupport/t5-small-awesome-text-to-sql")
+seq2seq_model = AutoModelForSeq2SeqLM.from_pretrained("cssupport/t5-small-awesome-text-to-sql")