Update app.py

app.py

@@ -48,10 +48,10 @@ conn.close()
 '''
 
 # Create a sample DataFrame with 3,000 records and 20 columns
+'''
 num_records = 20
 num_columns = 20
 
-
 data = {
     f"column_{i}": np.random.randint(0, 100, num_records) for i in range(num_columns)
 }
@@ -64,12 +64,13 @@ cities = ["New York", "Los Angeles", "Chicago", "Houston", "Miami"]  # List of c
 #data["city"] = [random.choice(cities) for _ in range(num_records)]
 
 table = pd.DataFrame(data)
+'''
 
 data = {
     "year": [1896, 1900, 1904, 2004, 2008, 2012],
     "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
 }
-#table = pd.DataFrame.from_dict(data)
+table = pd.DataFrame.from_dict(data)
 
 
 # Load the chatbot model
@@ -85,8 +86,9 @@ sql_model_name = "microsoft/tapex-large-finetuned-wtq"
 sql_tokenizer = TapexTokenizer.from_pretrained(sql_model_name)
 sql_model = BartForConditionalGeneration.from_pretrained(sql_model_name)
 
-#max_token_limit = sql_tokenizer.max_model_input_sizes[sql_model_name]
-#print(f"SQL Maximum token limit for {sql_model_name}: {max_token_limit}")
+stokenizer = AutoTokenizer.from_pretrained(model_name)
+max_token_limit = stokenizer.max_model_input_sizes[sql_model_name]
+print(f"SQL Maximum token limit for {sql_model_name}: {max_token_limit}")
 
 #sql_response = None
 conversation_history = []