Smarter Web Scraping with Python + AI

Set up your local project for smarter web scraping with Python by cloning the repo, creating a virtual environment, installing requirements, and preparing environment variables for a smooth start.

Create a hello world in a Jupyter notebook and load environment variables with a reusable helpers module, leveraging decouple or config and dot env files to avoid hard coding.

Demonstrates connecting Selenium through Bright Data proxy with IP rotation to achieve reliable, headless scraping using the scraping browser and Chrome options.

Develop a reusable remote connection utility using bright data proxies and a proxy URL. Configure dot env variables, import helpers, and streamline selenium-based web driver setup for web scraping.

Understand how URL patterns map to list and detail views to guide smarter web scraping, identifying item ids in URLs and parsing detail pages from sites like Hacker News.

Parse HTML with BeautifulSoup to extract item IDs and detail links from a hacker news list, using soup.find, soup.find_all, and href attributes to assemble usable detail URLs.

Extract more post data from list views by using Beautiful Soup to parse table rows, grab IDs, titles, scores, and points, and navigate pagination for additional pages.

Implement pagination in web scraping by iterating Hacker News pages with date and page parameters, separating scraping from parsing, and throttling requests with optional local HTML saves.

Save scraped data to local files by creating project-root directories with pathlib and using gitignore, then use boolean flags to scrape thread details and comments, balancing accuracy and cost.

Download and run Ollama with Llama 2 to run open-source language models locally. Test prompts with OpenAI compatibility to summarize text or extract keywords from raw data.

Learn how to test OpenAI's Python library with Ollama, configure system and user prompts, and extract concise json-formatted summaries and keywords from scraped content for smarter web scraping.

Refine prompts to extract summaries and keywords from scraped data, and output results in JSON for reliable parsing. Explore scraping and parsing with BeautifulSoup and OpenAI or Olama models.

Explore how to query the Apple iTunes search API with Python, construct URLs, filter for podcasts, handle JSON results, and refine language and media type to fetch English podcast data.

Turn predicted keywords into iTunes search API queries to fetch podcast data and save results in per-podcast directories. Apply AI prompts for language prediction, summaries, and keyword extraction.

Learn to download podcast episodes by cleaning file names, extracting suffixes, decoding URLs, saving files by podcast ID, converting to wav, and transcribing with whisper.

Chunk and transcribe podcast episodes by creating 32-second audio segments, detecting language, and transcribing with Whisper. Assemble a full transcript and explore parallel processing and storage considerations.

Learn to summarize and recommend podcast episodes by using the iTunes search API, leveraging transcripts, and applying AI-driven summaries and keywords to generate structured recommendations.

Store all scraped data in a SQL database and object storage to enable continuous analysis, leverage Bright Data for reliable scraping, and build smarter recommendations from the collected data.