update

app.py

@@ -28,7 +28,8 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 @tool 
 def extract_table_from_html(html: str, match: str | None = None) -> list:
     """
-    A tool that extracts HTML tables from HTML content and returns them as pandas DataFrames. Example use-cases include extracting tables from Wikipedia pages, HTML emails, or other web content.
+    A tool that extracts HTML tables from HTML content and returns them as pandas DataFrames. 
+    Example usecases include extracting tables from Wikipedia pages, HTML emails, or other web content.
     
     This function uses pandas.read_html() to parse HTML tables from the provided HTML content 
     and returns the extracted tables as a list of pandas DataFrames. It can optionally filter 
@@ -49,26 +50,6 @@ def extract_table_from_html(html: str, match: str | None = None) -> list:
     Raises:
         ValueError: If the HTML content is invalid or cannot be parsed.
         Exception: If HTML parsing fails or other unexpected errors occur.
-    
-    Example:
-        >>> html_content = '''
-        ... <table>
-        ...   <tr><th>Name</th><th>Age</th></tr>
-        ...   <tr><td>John</td><td>25</td></tr>
-        ... </table>
-        ... '''
-        >>> tables = extract_table_from_html(html_content)
-        >>> print(f"Found {len(tables)} tables")
-        >>> if tables:
-        ...     first_table = tables[0]
-        ...     print(f"First table shape: {first_table.shape}")
-        ...     print(first_table.head())
-    
-        >>> # Extract tables containing specific text
-        >>> tables = extract_table_from_html(html_content, match="Name")
-        >>> for i, table in enumerate(tables):
-        ...     print(f"Table {i}: {table.shape[0]} rows, {table.shape[1]} columns")
-    
     Note:
         - Uses pandas.read_html() which requires lxml, html5lib, or BeautifulSoup4
         - Tables must be properly formatted HTML <table> elements