Update app.py

app.py

@@ -51,6 +51,7 @@ conn.close()
 num_records = 30
 num_columns = 20
 
+'''
 data = {
     f"column_{i}": np.random.randint(0, 100, num_records) for i in range(num_columns)
 }
@@ -58,17 +59,17 @@ data = {
 # Randomize the year and city columns
 years = list(range(2000, 2023))  # Range of years
 cities = ["New York", "Los Angeles", "Chicago", "Houston", "Miami"]  # List of cities
-
+'''
 #data["year"] = [random.choice(years) for _ in range(num_records)]
 #data["city"] = [random.choice(cities) for _ in range(num_records)]
 
-table = pd.DataFrame(data)
+#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
@@ -84,8 +85,8 @@ 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}")
+#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}")
 
 #sql_response = None
 conversation_history = []