Discord
Account
Edit Profile
My Plan
LogoutAI Scraper Selenium App
This video demonstrates how to use the AI Scraper Selenium App template.
import json
from abilities import llm_prompt
from fastapi import Request
import logging
import uvicorn
from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles
from selenium_utils import SeleniumUtility
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = FastAPI()
templates = Jinja2Templates(directory="templates")
app.mount("/static", StaticFiles(directory="static"), name="static")
@app.get("/", response_class=HTMLResponse)
async def form_page(request: Request):
return templates.TemplateResponse("form.html", {"request": request})
@app.post("/process-page-info", response_class=HTMLResponse)Frequently Asked Questions
What are some potential business applications for the AI Scraper Selenium App?
The AI Scraper Selenium App has numerous business applications across various industries. Some potential use cases include: - Market research: Analyzing competitor websites for pricing, product information, and customer reviews. - Lead generation: Extracting contact information from business directories or industry-specific websites. - Content aggregation: Collecting news articles or blog posts from multiple sources for content curation platforms. - E-commerce monitoring: Tracking price changes and product availability across different online retailers. - SEO analysis: Gathering data on keyword usage, meta descriptions, and backlinks from target websites.
How can the AI Scraper Selenium App improve efficiency in data collection processes?
The AI Scraper Selenium App can significantly enhance data collection efficiency by: - Automating the process of navigating websites and extracting relevant information. - Utilizing AI to interpret and summarize page content, reducing the need for manual analysis. - Handling dynamic web content that traditional web scraping methods might struggle with. - Providing a user-friendly interface for non-technical team members to input URLs and questions. - Generating structured JSON output that can be easily integrated into existing data pipelines or analytics tools.
What industries could benefit most from implementing the AI Scraper Selenium App?
Several industries can benefit from the AI Scraper Selenium App, including: - Financial services: For monitoring stock prices, financial news, and market trends. - Real estate: Gathering property listings, pricing information, and market analytics. - Healthcare: Collecting medical research data, drug information, and clinical trial results. - Travel and hospitality: Aggregating flight prices, hotel rates, and customer reviews. - Retail: Tracking competitor pricing, product availability, and consumer sentiment.
How can I modify the AI Scraper Selenium App to handle pagination when scraping multiple pages?
To handle pagination in the AI Scraper Selenium App, you can modify the get_page_text method in the SeleniumUtility class. Here's an example of how you might implement this:
python
def get_page_text(self, url: str, max_pages: int = 5):
text = ""
try:
logger.info(f"Opening {url}")
self.open_url(url)
for page in range(max_pages):
text += self.driver.find_element("tag name", "body").text
next_button = self.driver.find_elements("xpath", "//a[contains(text(), 'Next')]")
if not next_button:
break
next_button[0].click()
time.sleep(2) # Wait for the next page to load
logger.info(f"Page text for {url} retrieved")
except Exception as e:
logger.error(f"Failed to retrieve page text for {url}: {e}")
finally:
self.close()
return text
This modification allows the app to click through multiple pages and aggregate the text content, up to a specified maximum number of pages.
Can the AI Scraper Selenium App handle websites that require authentication?
Yes, the AI Scraper Selenium App can be adapted to handle websites requiring authentication. You would need to modify the SeleniumUtility class to include a login method. Here's an example of how you might implement this:
```python def login(self, url: str, username: str, password: str): try: self.open_url(url) username_field = self.driver.find_element("name", "username") password_field = self.driver.find_element("name", "password") submit_button = self.driver.find_element("xpath", "//input[@type='submit']")
username_field.send_keys(username)
password_field.send_keys(password)
submit_button.click()
time.sleep(3) # Wait for login to complete
logger.info("Login successful")
except Exception as e:
logger.error(f"Login failed: {e}")
```
You would then call this login method before attempting to scrape any protected pages. Remember to handle authentication securely and in compliance with the website's terms of service.
Created: | Last Updated:
Getting Started
To begin using this template, simply click on "Start with this Template" on the Lazy platform. This will pre-populate the code in the Lazy Builder interface, so you won't need to copy or paste any code manually.
Test: Deploying the App
Once you have started with the template, press the "Test" button to deploy your app. The Lazy CLI will handle the deployment process, and you will not need to install any libraries or set up your environment. If the code requires any user input, you will be prompted to provide it through the Lazy CLI after pressing the "Test" button.
Entering Input
After deployment, if the app requires any user input, the Lazy CLI will prompt you to enter the necessary information. For this template, you will need to input the URL of the web page you want to scrape and the question you want to ask about the page content.
Using the App
Once the app is running, you will be provided with a dedicated server link to interact with the app's API. If you're using FastAPI, you will also receive a link to the API documentation. Navigate to the provided server link to access the web interface where you can submit your URL and question.
The app's frontend will render a form where you can enter the URL of the web page you want to analyze and the question you have about that page. After submitting the form, the app will display the information extracted from the page, formatted in a user-friendly manner.
Integrating the App
If you wish to integrate this app into an external service or frontend, you may need to use the server link provided by Lazy. For example, you could add the app's server link to an external tool that requires web scraping capabilities. If the app provides API endpoints, you can also integrate these endpoints into other applications or tools that you are using.
Remember, all the deployment and environment complexities are handled by Lazy, so you can focus on building and integrating your application.
If you encounter any issues or have questions about the template, refer to the Selenium documentation for more information on using Selenium with Python.
Here is an example of how you might integrate the template into another tool:
Sample code to call the AI Scraper Selenium App API endpoint:
import requests
The URL of the page you want to scrape:
page_url = "https://example.com"
The question you want to ask about the page:
question = "What is the main topic of the page?"
The server link provided by Lazy after deployment:
server_link = "http://your-lazy-app-server-link"
The endpoint to process page information:
endpoint = f"{server_link}/process-page-info"
Prepare the data for the POST request:
data = {
"url": page_url,
"question": question
}
Make the POST request to the app's endpoint: response = requests.post(endpoint, data=data)
Print the response from the app:
print(response.json())`
By following these steps, you can effectively use the AI Scraper Selenium App template to build and integrate a web scraping application into your projects.
Template Benefits
-
Automated Web Scraping: This template provides a powerful tool for businesses to automatically extract information from websites, saving time and resources on manual data collection.
-
AI-Powered Data Analysis: By integrating AI capabilities, the template can intelligently interpret and summarize web content, offering valuable insights without human intervention.
-
Customizable Information Retrieval: The user-friendly interface allows businesses to input specific URLs and questions, enabling targeted data extraction tailored to their unique needs.
-
Scalable Web Research: With its ability to handle multiple requests and process various websites, this template can support large-scale web research projects for market analysis, competitor monitoring, or trend tracking.
-
Efficient Decision Support: By quickly gathering and analyzing web data, this tool can provide rapid, data-driven insights to support business decision-making processes across various departments, from marketing to product development.
Technologies
Streamline CSS Development with Lazy AI: Automate Styling, Optimize Workflows and More
Enhance HTML Development with Lazy AI: Automate Templates, Optimize Workflows and More
Enhance Selenium Automation with Lazy AI: API Testing, Scraping and More
Python App Templates for Scraping, Machine Learning, Data Science and More
