Advanced Web Scraper with Llama Model Integration

This project integrates a Llama model into a web scraper to enhance data extraction capabilities.

Features

• Llama Model Integration: Utilizes a Llama model to improve the accuracy and efficiency of web scraping tasks.
• Data Parsing: Includes robust parsing mechanisms to process and structure scraped data effectively.
• Automation: Automates the process of navigating websites and extracting relevant information.

Installation

1. Clone the Repository:

   git clone https://github.com/Hammadh7/adv-webscraper.git

2. Navigate to the Project Directory:

   cd adv-webscraper

3. Install Dependencies:

   Ensure you have Python installed, then run:

   pip install -r requirements.txt

Usage

1. Configure the Scraper:

   Modify the scrape.py file to specify the target website and data to be extracted.

2. Run the Scraper:

   python main.py

3. Process the Data:

   The scraped data will be processed using the integrated Llama model as defined in the parse.py file.

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes.

License

This project is licensed under the MIT License.

Contact

For any inquiries, please contact me.