Implementation of Coduction Codes for Firecrawl and Ai-Powered Sfifizing Using Google Gemini

Quick growth of web content promides challenging and summarizing the relevant information. In this lesson, we show how we can see it Firecrawl With the web rows and process data issued using AI models you can go to Google Gemini. By combining these tools in Google Colab, we create a complete vacancies of the web pages, returning sound content, and creates short summaries using the Models of the United States. Whether you want to exchange information, remove information on articles, or create powerful apps AI, this lesson provides a solid solution and flexible.

!pip install google-generativeai firecrawl-py

First, we include Google-Generativeai Firecrawl-PY, including the two important libraries needed for this tutorial. Google-Generativeai provides access to the Gemini A-powered Text Genelivery Gemini, while Firecrawl-PY enables the web to download the content of the Web pages in orderly format.

import os
from getpass import getpass


# Input your API keys (they will be hidden as you type)
os.environ["FIRECRAWL_API_KEY"] = getpass("Enter your Firecrawl API key: ")

Then we can safely set the Firecrawl API key as a natural flexibility on Google Colab. It uses GetPass () to motivate the user with an API key without showing it, to ensure confidentiality. Keeping the key to OS.invirviron Vice allows Supply Verification of Web Formal Web Services on the entire session.

from firecrawl import FirecrawlApp


firecrawl_app = FirecrawlApp(api_key=os.environ["FIRECRAWL_API_KEY"])


target_url = "
result = firecrawl_app.scrape_url(target_url)
page_content = result.get("markdown", "")
print("Scraped content length:", len(page_content))

We start firecrawl by creating a Firecrawlapp example using the saved API key. It opens content on the specified webpage (this, Python's Provide Field page and release information on mark format. Finally, the printer is a deceptive content, allows us to ensure a successful return before further conducting.

import google.generativeai as genai
from getpass import getpass


# Securely input your Gemini API Key
GEMINI_API_KEY = getpass("Enter your Google Gemini API Key: ")
genai.configure(api_key=GEMINI_API_KEY)

We begin to compose Google Vemini API by safe capturing API key using Grass (), to prevent it from the obvious text display. Genai.Configure (API_key = Gemini_api_key) The command puts the API client, allowing seamorks for Gemino Ai production and summarizing activities. This ensures secure reassurance before making requests in AI model.

for model in genai.list_models():
    print(model.name)

We use the models available on Google Gemini API using Genaire.list_models () and print their names. This helps users to ensure which models are accessed with their API keys and select the appropriate one of the activities like a text or summary. If the model is not available, this AIDS step is fixing and selecting alternative.

model = genai.GenerativeModel("gemini-1.5-pro")
response = model.generate_content(f"Summarize this:nn{page_content[:4000]}")
print("Summary:n", response.text)

Finally, we start the Gemini 1.5 Pro Model model using the Genai.generativesmodel (“Gemini-4.5-Pro”) sends a summary of a bragging content. Restricts the installation of 4,000 characters to live within the API problems. The model processes the request and returns a short summary, and printer is provided, provides a constructed review and issued AI of the Web page content issued.

In conclusion, by combining a Firecrawl and Google Gemini, we create a default webpage and produce logical summaries with less effort. The study showed many AI solutions, allowing fluctuations based on API availability and quota issues. Whether you work NLP apps, default for research, or the consolidation of content, this method enables the release of effective data and summarizing the scale.

Here is the Colab Notebook. Also, don't forget to follow Sane and join ours Telegraph station including LinkedIn Grtopic. Don't forget to join ours 80k + ml subreddit.

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