当前位置：首页 > news >正文

两个网站做的h5如何合在一起市场调研方案

news 2025/7/27 23:42:21

两个网站做的h5如何合在一起,市场调研方案,做网站必须要公网ip,小白用网站建设工具在数据驱动的动态领域，从在线资源中提取有价值的见解至关重要。从市场分析到学术研究，对特定数据的需求推动了对强大的网络抓取工具的需求。 NSDT工具推荐： Three.js AI纹理开发包 - YOLO合成数据生成器 - GLTF/GLB在线编辑 - 3D模型格式在线…

在数据驱动的动态领域，从在线资源中提取有价值的见解至关重要。从市场分析到学术研究，对特定数据的需求推动了对强大的网络抓取工具的需求。

NSDT工具推荐： Three.js AI纹理开发包 - YOLO合成数据生成器 - GLTF/GLB在线编辑 - 3D模型格式在线转换 - 可编程3D场景编辑器 - REVIT导出3D模型插件 - 3D模型语义搜索引擎 - AI模型在线查看 - Three.js虚拟轴心开发包 - 3D模型在线减面 - STL模型在线切割

传统上，像 BeautifulSoup 和 Scrapy 这样的 Python 库一直是首选解决方案，需要用户利用编程专业知识来浏览复杂的网络结构。例如这个BeautifulSoup的示例：

# BeautifulSoup Example
from bs4 import BeautifulSoup
import requestsurl = 'https://example.com'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
print(soup.title)

或这个Scrapy的示例：

# Scrapy Example
import scrapyclass ExampleSpider(scrapy.Spider):name = 'example'start_urls = ['https://example.com']def parse(self, response):title = response.css('title::text').get()print(title)

1、ScrapeGraphAI 简介

ScrapeGraphAI 是一款开创性的 Python 库，可重塑网络抓取格局。这款创新工具利用大型语言模型 (LLM) 和直接图形逻辑的强大功能来简化数据收集。与前代产品不同，ScrapeGraphAI 使用户能够表达他们的数据需求，从而消除网络抓取的复杂性。

%%capture
!apt install chromium-chromedriver
!pip install nest_asyncio
!pip install scrapegraphai
!playwright install# if you plan on using text_to_speech and GPT4-Vision models be sure to use the
# correct APIKEY
OPENAI_API_KEY = "YOUR API KEY"
GOOGLE_API_KEY = "YOUR API KEY"from scrapegraphai.graphs import SmartScraperGraphgraph_config = {"llm": {"api_key": OPENAI_API_KEY,"model": "gpt-3.5-turbo",},
}smart_scraper_graph = SmartScraperGraph(prompt="List me all the projects with their descriptions.",# also accepts a string with the already downloaded HTML codesource="https://perinim.github.io/projects/",config=graph_config
)result = smart_scraper_graph.run()

import jsonoutput = json.dumps(result, indent=2)line_list = output.split("\n")  # Sort of line replacing "\n" with a new linefor line in line_list:print(line)

2、SpeechGraph

SpeechGraph 是一个类，代表默认抓取管道之一，可生成答案和音频文件。与 SmartScraperGraph 类似，但添加了 TextToSpeechNode 节点。

from scrapegraphai.graphs import SpeechGraph# Define the configuration for the graph
graph_config = {"llm": {"api_key": OPENAI_API_KEY,"model": "gpt-3.5-turbo",},"tts_model": {"api_key": OPENAI_API_KEY,"model": "tts-1","voice": "alloy"},"output_path": "website_summary.mp3",
}# Create the SpeechGraph instance
speech_graph = SpeechGraph(prompt="Create a summary of the website",source="https://perinim.github.io/projects/",config=graph_config,
)result = speech_graph.run()
answer = result.get("answer", "No answer found")

import jsonoutput = json.dumps(answer, indent=2)line_list = output.split("\n")  # Sort of line replacing "\n" with a new linefor line in line_list:print(line)

from IPython.display import Audio
wn = Audio("website_summary.mp3", autoplay=True)
display(wn)

3、GraphBuilder（实验性）

GraphBuilder 根据用户提示从头开始创建抓取管道。它返回包含节点和边的图形。

GraphBuilder 是一个实验性类，可帮助您根据提示创建自定义图形。它创建一个包含标识图形的基本元素的 json，并允许您使用 graphviz 对其进行可视化。它知道库默认提供的节点类型，并将它们连接起来以帮助您实现目标。

from scrapegraphai.builders import GraphBuilder# Define the configuration for the graph
graph_config = {"llm": {"api_key": OPENAI_API_KEY,"model": "gpt-3.5-turbo",},
}# Example usage of GraphBuilder
graph_builder = GraphBuilder(user_prompt="Extract the news and generate a text summary with a voiceover.",config=graph_config
)graph_json = graph_builder.build_graph()# Convert the resulting JSON to Graphviz format
graphviz_graph = graph_builder.convert_json_to_graphviz(graph_json)# Save the graph to a file and open it in the default viewer
graphviz_graph.render('ScrapeGraphAI_generated_graph', view=True)