https://github.com/ki-ian/llm-web-scrapper
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.
https://github.com/ki-ian/llm-web-scrapper
docker google-gemini-api gradio huggingface-spaces llm-webcrawler nvidia-nim-api playwright python webscraping
Last synced: about 1 month ago
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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.
- Host: GitHub
- URL: https://github.com/ki-ian/llm-web-scrapper
- Owner: KI-IAN
- License: mit
- Created: 2025-09-29T10:14:49.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-03T03:24:58.000Z (9 months ago)
- Last Synced: 2025-10-03T05:36:34.795Z (9 months ago)
- Topics: docker, google-gemini-api, gradio, huggingface-spaces, llm-webcrawler, nvidia-nim-api, playwright, python, webscraping
- Language: Python
- Homepage: https://huggingface.co/spaces/frkhan/llm-web-scrapper
- Size: 48.8 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
---
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](https://medium.com/@frkhan/from-broken-selectors-to-intelligent-scraping-a-journey-into-llm-powered-web-automation-fc76d5fe2dbc)
---
### 🚀 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.
---
### 🛠️ 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|
---
## ⚠️ 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.
---
## 📦 Installation
### Option 1: Run Locally
1. **Clone the repository:**
```bash
git clone https://github.com/KI-IAN/llm-web-scrapper.git
cd llm-web-scrapper
```
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Install Playwright browsers (for Crawl4AI):**
```bash
playwright install
```
4. **Create a `.env` file** in the root directory with your API keys:
```env
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:**
```bash
python app.py
```
---
### Option 2: Run with Docker
1. **For Production:**
This uses the standard `docker-compose.yml`.
```bash
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.
```bash
docker compose -f docker-compose.dev.yml up --build
```
Access the app at http://localhost:12200.
---
## 🔑 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](https://aistudio.google.com/api-keys).
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](https://ai.google.dev/gemini-api/docs/rate-limits)
### 🌐 NVIDIA NIM API Key
NIM (NVIDIA Inference Microservices) provides hosted models via REST APIs. To get started:
1. Go to the [NVIDIA API Catalog](https://build.nvidia.com/?integrate_nim=true&hosted_api=true&modal=integrate-nim).
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](https://www.firecrawl.dev/).
2. Find your API key in the dashboard.
### 🌐 Langfuse API Keys (Optional)
1. Sign up or log in at [Langfuse Cloud](https://cloud.langfuse.com/).
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.
---
## 🧪 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.
---
### 📁 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
```
---
## 📜 License
This project is open-source and distributed under the **MIT License**. Feel free to use, modify, and distribute it.
---
## 🤝 Acknowledgements
- [LangChain](https://www.langchain.com/) for orchestrating LLM interactions.
- [FireCrawl](https://www.firecrawl.dev/) & [Crawl4AI](https://docs.crawl4ai.com/) for providing powerful scraping backends.
- [NVIDIA AI Endpoints](https://build.nvidia.com/models) & [Google Gemini API](https://ai.google.dev/gemini-api/docs) for their state-of-the-art LLMs.
- [Langfuse](https://langfuse.com/) for providing excellent observability tools.
- [Gradio](https://www.gradio.app/) for making UI creation simple and elegant.
- [Docker](https://www.docker.com/) for containerization
- [Playwright](https://playwright.dev/) for web scraping using [Crawl4AI](https://docs.crawl4ai.com/)
---