by moonboy

Selenium AI Scraper with Google Gemini Flash

Test this app for free
175
import json
from abilities import llm
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)
Get full code

Frequently Asked Questions

What are some potential business applications for the AI Scraper with Google Gemini Flash?

The AI Scraper with Google Gemini Flash has numerous business applications across various industries. Some potential use cases include: - Market research: Automatically extracting competitor pricing, product features, or customer reviews from e-commerce websites. - Lead generation: Scraping contact information or company details from business directories or professional networking sites. - Financial analysis: Gathering stock prices, financial reports, or economic indicators from financial websites. - Real estate: Collecting property listings, prices, and details from real estate portals. - News and media monitoring: Extracting relevant articles, headlines, or sentiment analysis from news websites.

The AI Scraper's ability to understand context and extract specific information makes it a versatile tool for businesses looking to automate data collection and analysis.

How can the AI Scraper with Google Gemini Flash improve efficiency in data collection processes?

The AI Scraper with Google Gemini Flash can significantly improve efficiency in data collection processes by: - Automating manual data extraction: Instead of having employees manually browse websites and copy information, the AI Scraper can quickly retrieve relevant data. - Handling complex queries: The integration with Google Gemini Flash allows the scraper to understand and respond to nuanced questions about web content. - Providing structured output: The scraper not only extracts information but also provides the HTML element and Selenium code for future automation. - Scaling data collection: Businesses can easily scale their data collection efforts by running multiple instances of the AI Scraper simultaneously. - Reducing human error: By automating the extraction process, the AI Scraper minimizes the risk of human error in data collection.

These efficiency improvements can lead to significant time and cost savings for businesses that rely on web data for their operations.

What are the ethical considerations when using the AI Scraper with Google Gemini Flash?

When using the AI Scraper with Google Gemini Flash, it's important to consider several ethical aspects: - Respect for website terms of service: Ensure that scraping is allowed by the target website's terms of service. - Data privacy: Be cautious about collecting and storing personal information, and comply with data protection regulations like GDPR. - Server load: Implement rate limiting to avoid overwhelming target websites with requests. - Copyright: Respect copyright laws and don't republish scraped content without permission. - Transparency: If using the scraped data for business purposes, be transparent about its source. - Fairness: Avoid using the tool to gain unfair competitive advantages or manipulate markets.

It's crucial for businesses to use the AI Scraper responsibly and in compliance with legal and ethical standards.

How can I modify the AI Scraper to handle websites with dynamic content loaded via JavaScript?

To handle websites with dynamic content loaded via JavaScript, you can modify the SeleniumUtility class in the selenium_utils.py file. Here's an example of how you can add a wait mechanism:

```python from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.common.by import By

class SeleniumUtility: # ... existing code ...

   def get_page_source(self, url: str):
       source = None
       try:
           logger.info(f"Opening {url}")
           self.open_url(url)
           # Wait for a specific element to be present, indicating the page has loaded
           WebDriverWait(self.driver, 10).until(
               EC.presence_of_element_located((By.TAG_NAME, "body"))
           )
           source = self.driver.page_source
           logger.info(f"Page source for {url} retrieved")
       except Exception as e:
           logger.error(f"Failed to retrieve page source for {url}: {e}")
       finally:
           self.close()
       return source

```

This modification adds a wait of up to 10 seconds for the <body> tag to be present, allowing time for dynamic content to load before capturing the page source.

Can I extend the AI Scraper with Google Gemini Flash to handle multiple URLs in a single request?

Yes, you can extend the AI Scraper to handle multiple URLs in a single request. Here's an example of how you can modify the main.py file to achieve this:

```python @app.post("/process-multiple-pages", response_class=HTMLResponse) async def process_multiple_pages(request: Request): form_data = await request.form() urls = form_data.get("urls").split(",") question = form_data.get("question") selenium_util = SeleniumUtility() results = []

   for url in urls:
       page_source = selenium_util.get_page_source(url.strip())
       prompt = f"""Analyze the following webpage HTML source and answer this question: {question}

       Page Source:
       {page_source}

       Provide the following information:

Created: | Last Updated:

Web application to help scraping specific webpages. This selenium app sends the code of the page to Gemini flash 1.5 and asks it to retrieve the answer and points out the element on the page to do so.

Here's a step-by-step guide for using the AI Scraper with Google Gemini Flash template:

Introduction

The AI Scraper with Google Gemini Flash template is a powerful web application that helps you scrape specific webpages using Selenium and Google's Gemini 1.5 Flash AI model. This app allows you to input a URL and a question, then retrieves the relevant information from the webpage and provides you with a detailed answer, the HTML element containing the answer, and Python code to extract the answer using Selenium WebDriver.

Getting Started

To begin using this template, follow these steps:

  1. Click "Start with this Template" in the Lazy Builder interface.
  2. Press the "Test" button to deploy the application.

Using the App

Once the app is deployed, you'll receive a server link to access the web interface. Follow these steps to use the AI Scraper:

  1. Open the provided server link in your web browser.
  2. You'll see a form with two input fields:
  3. URL: Enter the webpage URL you want to scrape.
  4. Question: Enter the question you want to answer based on the webpage content.
  5. Click the "Submit" button to process your request.
  6. Wait for the AI to analyze the webpage and generate a response.

Understanding the Results

After processing your request, the app will display the following information:

  1. Answer: A detailed response to your question based on the webpage content.
  2. HTML Element: The specific HTML tag containing the answer on the webpage.
  3. Selenium Code: Python code snippet to extract the relevant content using Selenium WebDriver.

Integrating the App

This AI Scraper can be integrated into other applications or workflows. Here's a sample code snippet to make a request to the API endpoint:

```python import requests

url = "https://your-lazy-app-url.com/process-page-info" data = { "url": "https://example.com", "question": "What is the main topic of this webpage?" }

response = requests.post(url, data=data) result = response.json()

print("Answer:", result["answer"]) print("HTML Element:", result["html_element"]) print("Selenium Code:", result["selenium_code"]) ```

Replace "https://your-lazy-app-url.com" with the actual URL provided by Lazy for your deployed app.

By following these steps, you can easily use the AI Scraper with Google Gemini Flash to extract and analyze information from webpages efficiently.



Here are 5 key business benefits for this AI Scraper template:

Template Benefits

  1. Automated Web Data Extraction: This template enables businesses to quickly and efficiently extract specific information from websites without manual searching, saving time and reducing human error.

  2. Flexible Information Retrieval: By allowing users to input custom questions, the template can adapt to various data extraction needs across different industries and use cases.

  3. SEO and Competitor Analysis: Businesses can use this tool to analyze competitor websites, gather market intelligence, and inform their SEO strategies by easily extracting relevant information.

  4. Enhanced Decision Making: By providing rapid access to web-based information, this template supports data-driven decision making across various business functions, from marketing to product development.

  5. Scalable Web Monitoring: The template can be adapted for monitoring multiple websites for changes or specific information, enabling businesses to stay updated on industry trends, pricing changes, or other critical data points.

Technologies

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Enhance HTML Development with Lazy AI: Automate Templates, 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 Enhance Selenium Automation with Lazy AI: API Testing, Scraping and More
Optimize Gemini Workflows with Lazy AI: Automate AI Model Management, Fine-Tuning, Integration and More  Optimize Gemini Workflows with Lazy AI: Automate AI Model Management, Fine-Tuning, Integration and More
Python App Templates for Scraping, Machine Learning, Data Science and More Python App Templates for Scraping, Machine Learning, Data Science and More

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