README.md

metadata

title: LLM Web Scraper
emoji: 🕸️
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860

LLM Web Scraper (🕸️ → 🤖 → 🧠 → ❓ → 📄)

Scrape any web page, ask questions, and get structured answers powered by LangChain, FireCrawl/Crawl4AI and leading LLMs from NVIDIA and Google—all wrapped in a clean Gradio interface.

🔗 Live Demo: https://huggingface.co/spaces/frkhan/llm-web-scrapper

🔗 Read Full Story: From Broken Selectors to Intelligent Scraping: A Journey into LLM-Powered Web Automation

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🚀 Features

• 🕸️ Multi-Backend Scraping: Choose between FireCrawl for robust, API-driven scraping and Crawl4AI for local, Playwright-based scraping.
• 🧠 Intelligent Extraction: Use powerful LLMs (NVIDIA or Google Gemini) to understand your query and extract specific information from scraped content.
• 📊 Structured Output: Get answers in markdown tables, JSON, or plain text, as requested.
• 📈 Full Observability: Integrated with Langfuse to trace both scraping and LLM-extraction steps.
• ✨ Interactive UI: A clean and simple interface built with Gradio.
• 🐳 Docker-Ready: Comes with Dockerfile and docker-compose configurations for easy local and production deployment.

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🛠️ Tech Stack

Component	Purpose
LangChain	Orchestration of LLM calls
FireCrawl / Crawl4AI	Web scraping backends
NVIDIA / Gemini	LLM APIs for information extraction
Langfuse	Tracing and observability for all operations
Gradio	Interactive web UI
Docker	Containerized deployment
Playwright	Web scraping using Crawl4AI

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⚠️ Important Disclaimer

This tool is provided for educational and experimental purposes only. Users are solely responsible for their actions and must comply with the terms of service of any website they scrape. Always respect robots.txt and be mindful of the website's policies. The developers of this tool are not liable for any misuse.

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📦 Installation

Option 1: Run Locally

1. Clone the repository:

   git clone https://github.com/KI-IAN/llm-web-scrapper.git
   cd llm-web-scrapper
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Install Playwright browsers (for Crawl4AI):

   playwright install
   

4. Create a .env file in the root directory with your API keys:

   GOOGLE_API_KEY=your_google_api_key
   NVIDIA_API_KEY=your_nvidia_api_key
   FIRECRAWL_API_KEY=your_firecrawl_api_key
   
   # Optional: For Langfuse tracing
   LANGFUSE_PUBLIC_KEY=pk-lf-...
   LANGFUSE_SECRET_KEY=sk-lf-...
   LANGFUSE_HOST=https://cloud.langfuse.com
   

5. Run the application:

   python app.py
   

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Option 2: Run with Docker

1. For Production: This uses the standard docker-compose.yml.

   docker compose up --build
   

2. For Local Development (with live code reload): This uses docker-compose.dev.yml to mount your local code into the container.

   docker compose -f docker-compose.dev.yml up --build
   

Access the app at http://localhost:12200.

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🔑 Getting API Keys

To use this app, you'll need API keys for Google Gemini, NVIDIA NIM, and FireCrawl. For full observability, you'll also need keys for Langfuse.

🌐 Gemini API Key

Gemini is Google's family of generative AI models. To get an API key:

1. Visit the Google AI Studio.
2. Sign in with your Google account.
3. Click "Create API Key" and copy the key shown.
4. Use this key in your .env file or configuration as GEMINI_API_KEY.

Note: Gemini API access may be limited based on region or account eligibility. Check the Gemini API Rate Limits here

🌐 NVIDIA NIM API Key

NIM (NVIDIA Inference Microservices) provides hosted models via REST APIs. To get started:

1. Go to the NVIDIA API Catalog.
2. Choose a model (e.g., nim-gemma, nim-mistral, etc.) and click "Get API Key".
3. Sign in or create an NVIDIA account if prompted.
4. Copy your key and use it as NVIDIA_NIM_API_KEY in your environment.

Tip: You can test NIM endpoints directly in the browser before integrating.

🌐 FireCrawl API Key

1. Sign up at FireCrawl.
2. Find your API key in the dashboard.

🌐 Langfuse API Keys (Optional)

1. Sign up or log in at Langfuse Cloud.
2. Navigate to your project settings and then to the "API Keys" tab.
3. Create a new key pair to get your LANGFUSE_PUBLIC_KEY (starts with pk-lf-...) and LANGFUSE_SECRET_KEY (starts with sk-lf-...).
4. Add these to your .env file to enable tracing.

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🧪 How to Use

1. Enter a URL: Provide the URL of the web page you want to analyze.
2. Define Your Query: Specify what you want to extract (e.g., "product name, price, and rating" or "summarize this article").
3. Scrape the Web Page: Choose a scraper (Crawl4AI or FireCrawl) and click "Scrape Website".
4. Select Model & Provider: Choose an LLM to process the scraped content.
5. Extract Info: Click "Extract Info by LLM" to get a structured answer.

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📁 File Structure

llm-web-scrapper/
├── .env                  # Local environment variables (not tracked by git)
├── .github/              # GitHub Actions workflows
├── .gitignore
├── docker-compose.yml    # Production Docker configuration
├── docker-compose.dev.yml# Development Docker configuration
├── Dockerfile
├── requirements.txt
├── app.py                # Gradio UI and application logic
├── config.py             # Environment variable loading
├── crawl4ai_client.py    # Client for Crawl4AI scraper
├── firecrawl_client.py   # Client for FireCrawl scraper
└── llm_inference_service.py # Logic for LLM calls

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📜 License

This project is open-source and distributed under the MIT License. Feel free to use, modify, and distribute it.

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🤝 Acknowledgements

• LangChain for orchestrating LLM interactions.
• FireCrawl & Crawl4AI for providing powerful scraping backends.
• NVIDIA AI Endpoints & Google Gemini API for their state-of-the-art LLMs.
• Langfuse for providing excellent observability tools.
• Gradio for making UI creation simple and elegant.
• Docker for containerization
• Playwright for web scraping using Crawl4AI

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