Introduction

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Overview

By the end of this guide, you will be able to capture screenshots of current mortgage rates and analyze them using vision capabilities.

You’ll learn how to:

1. Capture real-time mortgage data using Browserbase’s headless browser automation.
2. Analyze visual data with IBM watsonx.ai’s available vision language models to extract meaningful information.

This integration is useful for:

• Accessing otherwise inaccessible data. Data can often be embedded within iframes, making it difficult to scrape with traditional methods
• Automating financial data collection from websites without structured APIs.
• Converting visual mortgage rate data into structured information.
• Building financial monitoring tools that track rate changes over time.

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Prerequisites

Before you start, make sure you have:

• IBM Watson Project ID & API key
  • Access to IBM’s foundation models including vision capabilities
• Browserbase Project ID & API key
  • Python environment with required dependencies

Note: This guide is only available in Python

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Why Screenshots?

Traditional web scraping methods often fail when dealing with:

• Content embedded within iframes (like the Freddie Mac mortgage rates)
• Data rendered by JavaScript after page load
• Complex interactive visualizations
• Protected or anti-scraping content

By using Browserbase to capture full screenshots of rendered pages and IBM watsonx.ai to interpret the visual content, you can extract information that would be otherwise inaccessible through HTML parsing or API calls.

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Quickstart Guides