This is a submission for the Bright Data Web Scraping Challenge: Most Creative Use of Web Data for AI Models

What I Built

As someone deeply interested in academic research, I often found myself juggling between different websites to find relevant papers and research work. I wanted a tool that could simplify this process, so I built Research Paper AI.

Research Paper AI is a streamlined academic search engine that scrapes papers from various sources like arXiv and Google Scholar in real-time. What makes it special is how it uses Bright Data's infrastructure to reliably access these academic sources, which are typically challenging to scrape due to their anti-bot measures and complex structures.

The magic happens when you type in a search query - the app simultaneously searches across multiple academic sources, handling all the complexities of web scraping behind the scenes, and presents you with clean, unified results. Think of it as your personal research assistant that knows how to navigate the academic web.

Here's what it looks like in action:

1. Main search interface
   

2. Search results with papers
   

How I Used Bright Data

This is where things get interesting! Academic websites are notoriously tricky to scrape - they have CAPTCHAs, rate limits, and sometimes require complex JavaScript rendering. Bright Data's tools made these challenges much more manageable.

Here's how I leveraged Bright Data:

1. Scraping Browser Integration: I used Bright Data's Scraping Browser to handle JavaScript-heavy pages and bypass anti-bot measures. Here's a snippet of how it works:

class BrightScraper:
    def __init__(self, config: Dict):
        self.username = config['username']
        self.password = config['password']
        self.proxy_url = f"http://{self.username}:{self.password}@{self.host}"

    async def scrape_arxiv(self, query: str):
        async with self.session.get(search_url, proxy=self.proxy_url) as response:
            # Bright Data handles all the complex stuff behind the scenes
            html = await response.text()
            return self._parse_results(html)

1. Proxy Management: Instead of dealing with proxy rotation and management myself, Bright Data's infrastructure handles it automatically. This means:
   • No more blocked requests
   • Reliable access to academic sources
   • Clean, consistent data collection

The real breakthrough came when dealing with Google Scholar, which is typically very difficult to scrape. Bright Data's tools made it feel almost trivial!

Tech Stack

• Frontend: React + TailwindCSS (because life's too short for bad UIs)
• Backend: FastAPI (because async is awesome)
• Scraping: Bright Data's Scraping Browser
• Data Sources: arXiv, Google Scholar

Interesting Challenges

1. The CAPTCHA Conundrum: Academic sites love their CAPTCHAs. Bright Data's Scraping Browser handled these seamlessly.

2. Rate Limiting: Initially, I was getting blocked after a few requests. Switching to Bright Data's proxy network solved this instantly.

3. JavaScript Rendering: Some sites needed full JavaScript execution to load content. The Scraping Browser handled this without breaking a sweat.

Future Plans

I'm excited to add more features:

• More academic sources (IEEE, Semantic Scholar)
• Citation network visualization
• Paper similarity analysis
• PDF content extraction

Want to Try It Yourself?

Check out the GitHub repo and give it a spin! The setup is pretty straightforward:

1. Clone the repo
2. Add your Bright Data credentials
3. Run the backend and frontend
4. Start discovering papers!

Conclusion

Building Research Paper AI was a fantastic learning experience. Bright Data's tools turned what could have been a complex scraping nightmare into a manageable and fun project. The best part? It actually solves a real problem that researchers face daily.

brightdatachallenge #webdev #api #ai