Implement Visual Interaction Tools with TabbyML

Expand the VisualNavigator agent's capabilities by adding tools for 'click_element_by_text' and 'extract_text_content_by_selector'. Use TabbyML as an AI coding assistant to generate the Playwright/Selenium code for these tools. These tools should simulate actual browser interactions. Ensure the agent can call these new tools effectively within the OpenAI Agents SDK framework. ```python
# Assume client and assistant are already initialized # Example of integrating a new tool schema
click_tool_schema = { "type": "function", "function": { "name": "click_element_by_text", "description": "Clicks a visible element on the page containing specific text.", "parameters": { "type": "object", "properties": { "text": {"type": "string", "description": "The text content of the element to click."} }, "required": ["text"] } }
} # Update assistant with new tool
# assistant = client.beta.assistants.update(assistant.id, tools=[navigate_tool_schema, click_tool_schema, ...]) # Your task: use TabbyML to assist in writing the Python function 'click_element_by_text(text)'
# that uses Playwright/Selenium to find and click the element.
# Integrate this function with your OpenAI Agents SDK tool execution loop.
```