Update spider.py

spider.py

@@ -1,24 +1,22 @@
-from datasets import load_dataset, Dataset
 import pandas as pd
+from datasets import Dataset
 
-# Step 1: Download your uploaded Spider dataset
-my_spider_dataset = load_dataset("HusnaManakkot/new-spider-HM", split='train')
+# Step 1: Read the Parquet files
+train_df = pd.read_parquet('train-00000-of-00001.parquet')
+validation_df = pd.read_parquet('validation-00000-of-00001.parquet')
 
-# Step 2: Convert the dataset to a Pandas DataFrame
-my_spider_df = pd.DataFrame(my_spider_dataset)
-
-# Step 3: Add a new row to the DataFrame
+# Step 2: Add the new row to the training DataFrame
 new_example = {
-    'db_id': 'employee_database',  # Replace with the actual database ID
-    'query': "SELECT AVG(salary) FROM employees WHERE join_date > '2015-01-01' AND department = 'Marketing';",
-    'question': 'What is the average salary of employees who joined after 2015 and work in the Marketing department?',
-    'query_toks': ['SELECT', 'AVG', '(', 'salary', ')', 'FROM', 'employees', 'WHERE', 'join_date', '>', "'2015-01-01'", 'AND', 'department', '=', "'Marketing'", ';'],
-    'query_toks_no_value': ['SELECT', 'AVG', '(', 'salary', ')', 'FROM', 'employees', 'WHERE', 'join_date', '>', 'VALUE', 'AND', 'department', '=', 'VALUE', ';'],
-    'question_toks': ['What', 'is', 'the', 'average', 'salary', 'of', 'employees', 'who', 'joined', 'after', '2015', 'and', 'work', 'in', 'the', 'Marketing', 'department', '?'],
+    'db_id': 'hr_1',  # Replace with the actual database ID
+    'query': "SELECT COUNT(*) FROM employees WHERE department = 'Sales' AND salary > 50000;",
+    'question': 'How many employees in the Sales department have a salary greater than $50,000?',
+    'query_toks': ['SELECT', 'COUNT', '(', '*', ')', 'FROM', 'employees', 'WHERE', 'department', '=', "'Sales'", 'AND', 'salary', '>', '50000', ';'],
+    'query_toks_no_value': ['SELECT', 'COUNT', '(', '*', ')', 'FROM', 'employees', 'WHERE', 'department', '=', 'VALUE', 'AND', 'salary', '>', 'VALUE', ';'],
+    'question_toks': ['How', 'many', 'employees', 'in', 'the', 'Sales', 'department', 'have', 'a', 'salary', 'greater', 'than', '$50,000', '?'],
     'sql': {
-        'select': [(0, [(3, (0, 'salary'))])],  # (agg_id, val_unit), agg_id for AVG is 0, val_unit is (column_id, column_name)
+        'select': [(0, [(3, (0, '*'))])],  # (agg_id, val_unit), agg_id for COUNT is 0, val_unit is (column_id, column_name)
         'from': {'table_units': [('table_unit', 0)], 'conds': []},  # ('table_unit', table_id), assuming 'employees' is the first table
-        'where': [(0, False, [(2, (0, 'join_date')), '>', (1, "'2015-01-01'")]), 'AND', (0, False, [(2, (0, 'department')), '=', (1, "'Marketing'")])],
+        'where': [(0, False, [(2, (0, 'department')), '=', (1, "'Sales'")]), 'AND', (0, False, [(2, (0, 'salary')), '>', (1, '50000')])],
         'groupBy': [],
         'having': [],
         'orderBy': [],
@@ -28,11 +26,7 @@ new_example = {
         'except': None
     }
 }
+train_df = train_df.append(new_example, ignore_index=True)
 
-my_spider_df = my_spider_df.append(new_example, ignore_index=True)
-
-# Step 4: Convert the modified DataFrame back to a Hugging Face dataset
-modified_spider_dataset = Dataset.from_pandas(my_spider_df)
-
-# Step 5: Push the modified dataset back to your Hugging Face account
-modified_spider_dataset.push_to_hub("HusnaManakkot/new-spider-HM")
+# Step 3: Write the modified DataFrame back to Parquet
+train_df.to_parquet('train-00000-of-00001.parquet')