- author: All About AI
Exploring a New Approach to Web Scraping with Puppeteer
In this article, we will delve into a unique method of web scraping that involves using a combination of Puppeteer, Google Cloud Vision, and Mistal models. By following this approach, we can capture screenshots of web pages and extract valuable information from them, resulting in more reliable data retrieval.
Understanding the Flowchart
To grasp the entire process, let's first take a look at the flowchart of the project:
As seen in the flowchart, the process begins by setting the URLs of the web pages we want to extract data from. Traditionally, this would involve using libraries like Beautiful Soup and web scraping techniques. However, in this case, we will take a different approach and employ Puppeteer to capture screenshots of each web page. Once we have these screenshots, we can analyze them using Google Cloud Vision. The Vision API allows us to extract the desired information efficiently.
Benefits of the Puppeteer Method
Why use Puppeteer instead of traditional web scraping techniques? The answer lies in the more comprehensive and reliable information we can gather. By capturing screenshots of web pages, we gain access to the visual elements that may not be easily extractable through traditional scraping methods. This gives us a broader scope of data to work with, enhancing the accuracy and quality of our results.
Leveraging Mistal Models for Structured Data
With the information we have gathered from the screenshots, we can leverage Mistal models to convert the data into a structured format. Prompt engineering techniques can be used to generate this structured format, and we can even utilize OpenAI models for this purpose. By doing so, we can extract refined information from the web pages effectively.
Adding a Voice Version to the Output
To make our analysis even more engaging, we can incorporate a voice version of the output. By using the 11 Labs API, we can convert the text-based output into an audio format. This adds a level of accessibility and makes it easier for users to consume the information.
Example Code and Practical Applications
Let's take a closer look at the Python code that implements this approach. While the examples here will be demonstrated using Visual Studio Code and Notepad++, you can adapt them to your preferred code editor. Before diving into the code, it's worth mentioning the inspiration for using Puppeteer in this project. The Puppeteer code base was inspired by AI Json's YouTube channel, and the initial code was forked with enhancements.
In the code, the Puppeteer library is used to capture screenshots, and additional functionalities were added using the Stealth Plugin. The
setViewport function helps define the shape and aspect ratio of the screenshots, ensuring we capture the necessary information accurately. The
get_mistal_response function utilizes the Mistal API and the Mysterious media model to generate the structured format we desire. The response from Puppeteer is then processed using subprocessing, ultimately resulting in the desired screenshots.
To demonstrate a practical example, we are building a web scraper specifically geared towards extracting tech news headlines. The code fetches screenshots from predefined URLs, analyzes them using Google Cloud Vision, and utilizes Mistal models to generate a concise bullet-point format for the news headlines. Finally, the text-to-speech conversion and download functionality is implemented using the 11 Labs API.
Tracking Sports Games with Puppeteer: A Comprehensive Report
In a major move to expand its offerings, Adobe has recently ventured into the realm of sports game tracking using Puppeteer technology. This exciting project aims to provide users with both textual and audio outputs, allowing them to receive live updates on multiple games and gain insights into scores, statistics, and player performances. By leveraging Puppeteer's screenshot capabilities, a new era of sports tracking has been unlocked, opening up possibilities for innovative developments in the field.
To put this technology to the test, a basketball game and a football game have been selected as examples. With the respective URLs provided, the goal is to extract key information about each game, such as the score, basic statistics, and the best performing player. Let's dive into the results and analyze the outcomes of this venture into sports game tracking.
Basketball Game Analysis
The first game under review is a basketball match between Yamagata Wyverns and Aomori. The final score stood at 77-66, with the Yamagata Wyverns emerging as the victors. Examining the statistics, Yamagata's Okajima stood out as the best performing player, scoring 17 points, along with two rebounds and one assist. These findings provide valuable insights into the game's progress and the standout performances of individual players.
Football Game Analysis
Turning our attention to football, we have a match between Zerum Spore and Umrani ESP. Despite a relatively low-scoring affair, with no goals on target, the statistics reveal an interesting dynamic. Zerum Spore held 52% ball possession, while Umrani ESP maintained possession with 48%. Both teams attempted a goal each, but none found their mark. Although the game lacked excitement in terms of goals, these insights shed light on the strategies employed and the evenly contested nature of the match.
To enhance the user experience, the extracted data is processed and narrated by MistoL, creating voiceover reports that provide a more engaging means of accessing game-related information. Upon listening to the basketball game report, we learn that the Yamagata Wyverns defeated Aomori with a score of 77-66, while Okajima from the Yamagata Wyverns showcased an outstanding performance, contributing 17 points, two rebounds, and one assist to the team's victory.
Similarly, the football match report presents the facts concisely, with Zerum Spore enjoying 52% ball possession compared to Umrani ESP's 48%. Both teams attempted one goal each but failed to hit the target. Although the voiceover reports capture the essence of the games, future improvements in prompt engineering can make them more engaging and captivating for users.
Future Prospects andThis article has provided an overview of a unique approach to web scraping using puppeteer, google cloud vision, and mistal models. by utilizing screenshots and advanced models, we can extract more reliable and comprehensive information from web pages. this approach opens up new possibilities for enhanced web scraping and data extraction.
while the code examples focused on extracting tech news headlines, the methodology can be applied in various contexts. whether it's analyzing market trends, monitoring competitor activity, or gathering research data, this approach offers a versatile solution for web scraping needs.
feel free to experiment with different scenarios and expand upon the code provided. the possibilities for leveraging puppeteer and advanced models are vast, and this project serves as a starting point for further exploration.
This foray into sports game tracking with Puppeteer marks an exciting development in the field. The integration of Puppeteer's screenshot capabilities for web scraping purposes adds a unique dimension to the traditional approach, as observed in the utilization of Puppeteer and the generated screenshots as a dataset. While this project serves as a fantastic starting point, there is ample room for exploration and expansion. Possibilities include refining the prompt engineering process and incorporating additional features to enhance the user experience.
If you have any novel ideas or suggestions regarding the future prospects of this technology, we would love to hear from you. Leave your thoughts and comments below, as they could shape the trajectory of this groundbreaking project. To access the code and contribute to its development, feel free to visit the GitHub repository linked in the description.
Thank you for tuning in and following along on this thrilling journey of sports game tracking with Puppeteer. Stay tuned for more exciting updates and advancements in the field.