update

app.py

@@ -28,14 +28,12 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 
 @tool 
-def extract_table_from_html(html: str) -> list:
+def extract_table_from_html(url: str) -> list:
     """
-    A tool that extracts HTML tables from HTML content and returns them as pandas DataFrames. 
+    A tool that extracts table data from a website and returns them as pandas DataFrames. 
     Example usecases include extracting tables from Wikipedia pages, HTML emails, or other web content.
     Args:
-        html (str): The HTML content containing HTML tables to extract. This can be raw HTML 
-                   string content or a URL to a webpage.
-    
+        url (str): URL to a webpage.
     Returns:
         list: A list of pandas DataFrames, where each DataFrame represents a table found 
               in the HTML content. Returns an empty list if no tables are found.
@@ -44,7 +42,7 @@ def extract_table_from_html(html: str) -> list:
     
     try:
         # Extract tables using pandas
-        tables = pd.read_html(html)
+        tables = pd.read_html(url)
         
         # Return the list of DataFrames directly
         return tables if tables else []
@@ -203,7 +201,7 @@ class BasicAgent:
         ) 
         
         self.code_agent = CodeAgent(
-            tools=[VisitWebpageTool(), GoogleSearchTool("serper"), get_file, audio_to_text],
+            tools=[VisitWebpageTool(), GoogleSearchTool("serper"), get_file, audio_to_text, extract_table_from_html],
             model=InferenceClientModel(
                 model_id="Qwen/Qwen2.5-Coder-32B-Instruct"
             ),
@@ -235,9 +233,8 @@ class BasicAgent:
                     - Parsing and extracting data from Wikipedia pages.
                 This agent follows rules below:
                 1. `wikipedia` Python package is provided, we should use it to search and retrieve Wikipedia pages.
-                2. `pandas` Python package is provided, we should use it to extract table data from Wikipedia pages.
             """,
-            verbosity_level=0,
+            verbosity_level=1,
             max_steps=10,
         )