updated the smolagents

Oracle/DataSmolAgent.py

@@ -5,28 +5,14 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 
 @tool
 def clean_data(df: pd.DataFrame) -> pd.DataFrame:
-    """Clean the DataFrame by stripping whitespace from column names and dropping rows that are completely empty.
-
-    Args:
-        df (pd.DataFrame): The input DataFrame containing the raw data.
-
-    Returns:
-        pd.DataFrame: A cleaned DataFrame with stripped column names and without completely empty rows.
-    """
+    """Clean the input DataFrame (df) by stripping column whitespace and dropping empty rows."""
     df.columns = df.columns.str.strip()
     df = df.dropna(how="all")
     return df
 
 @tool
 def extract_features(df: pd.DataFrame) -> pd.DataFrame:
-    """Dynamically extract features from the DataFrame.
-
-    Args:
-        df (pd.DataFrame): The input DataFrame containing the raw data.
-
-    Returns:
-        pd.DataFrame: The DataFrame updated with new dynamically engineered features.
-    """
+    """Dynamically extract features from the input DataFrame (df)."""
     # Numeric columns: log transformation
     numeric_cols = df.select_dtypes(include=[np.number]).columns.to_list()
     for col in numeric_cols:
@@ -59,15 +45,7 @@ def extract_features(df: pd.DataFrame) -> pd.DataFrame:
 
 @tool
 def save_to_csv(df: pd.DataFrame, filename: str = "output.csv") -> str:
-    """Save the DataFrame to a CSV file.
-
-    Args:
-        df (pd.DataFrame): The DataFrame to save.
-        filename (str): The name of the output CSV file (default "output.csv").
-
-    Returns:
-        str: The file path of the saved CSV.
-    """
+    """Save the input DataFrame (df) to a CSV file named 'filename'."""
     df.to_csv(filename, index=False)
     return filename