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| Microsoft Windows [版本 10.0.19043.1826] (c) Microsoft Corporation。保留所有权利。
D:\python_work\work\auto_interpreter>conda activate py310
(py310) D:\python_work\work\auto_interpreter>interpreter -y
Welcome to Open Interpreter.
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ▌ OpenAI API key not found
To use GPT-4 (recommended) please provide an OpenAI API key.
To use Code-Llama (free but less capable) press enter.
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── OpenAI API key: xxxx
Tip: To save this key for later, run export OPENAI_API_KEY=your_api_key on Mac/Linux or setx OPENAI_API_KEY your_api_keyon Windows.
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────> 请总结一下这篇文章的内容,文章链接为:https://www.bilibili.com/read/cv17233088/ 结果请用中文输出。如果需要通过pip安 装包的话,请在pip install后面添加--proxy http://127.0.0.1:7890设置代理
首先,我们需要制定一个计划来完成这个任务。
1 首先,我们需要安装一些必要的Python包,如requests和beautifulsoup4,这些包可以帮助我们从网页中获取和 解析数据。由于用户需要使用代理,我们将在安装这些包时添加代理设置。 2 然后,我们将使用requests包来获取文章的HTML内容。 3 接下来,我们将使用beautifulsoup4来解析HTML内容,提取文章的主要内容。 4 最后,我们将使用Python的自然语言处理库,如jieba和gensim,来总结文章的内容。如果这些库尚未安装,我们将安装它 们,并在安装时添加代理设置。
现在,我们开始执行第一步,安装必要的Python包。
pip install --proxy http://127.0.0.1:7890 requests beautifulsoup4
Microsoft Windows [版本 10.0.19043.1826] (c) Microsoft Corporation。保留所有权利。 (py310) D:\python_work\work\auto_interpreter>echo "ACTIVE_LINE:1" "ACTIVE_LINE:1" (py310) D:\python_work\work\auto_interpreter>pip install --proxy http://127.0.0.1:7890 requests beautifulsoup4 Requirement already satisfied: requests in d:\downloads\anaconda\envs\py310\lib\site-packages (2.31.0) Collecting beautifulsoup4 Using cached beautifulsoup4-4.12.2-py3-none-any.whl (142 kB) Requirement already satisfied: charset-normalizer<4,>=2 in d:\downloads\anaconda\envs\py310\lib\site-packages (from requests) (3.2.0) Requirement already satisfied: idna<4,>=2.5 in d:\downloads\anaconda\envs\py310\lib\site-packages (from requests) (3.4) Requirement already satisfied: urllib3<3,>=1.21.1 in d:\downloads\anaconda\envs\py310\lib\site-packages (from requests) (2.0.4) Requirement already satisfied: certifi>=2017.4.17 in d:\downloads\anaconda\envs\py310\lib\site-packages (from requests) (2023.7.22) Collecting soupsieve>1.2 (from beautifulsoup4) Obtaining dependency information for soupsieve>1.2 from https://files.pythonhosted.org/packages/4c/f3/038b302fdfbe3be7da016777069f26ceefe11a681055ea1f7817546508e3/soupsieve -2.5-py3-none-any.whl.metadata Downloading soupsieve-2.5-py3-none-any.whl.metadata (4.7 kB) Downloading soupsieve-2.5-py3-none-any.whl (36 kB) Installing collected packages: soupsieve, beautifulsoup4 Successfully installed beautifulsoup4-4.12.2 soupsieve-2.5 (py310) D:\python_work\work\auto_interpreter>
我们已经成功安装了requests和beautifulsoup4这两个Python包。
接下来,我们将执行第二步,使用requests包来获取文章的HTML内容。我们将使用用户提供的URL来获取文章。
import requests
import requests
import requests
import requests
url = 'https://www.bilibili.com/read/cv17233088/' response = requests.get(url) response.text
Output truncated. Showing the last 2000 characters.
"color-purple-03\\"\\u003E脆弱意志\\u003C\\u002Fspan\\u003E击中后的三回合内应手动进食保持血量为满。\\u003C\\u002Fp\\ 03E\\u003Cp\\u003E读条\\u003Cspan class=\\"color-purple-03\\"\\u003E千钧之力\\u003C\\u002Fspan\\u003E时开始疯狂进食,因为手动进食有延迟。\\u003C\\u002 Fp\\u003E\\u003Cfigure class=\\"img-box\\" contenteditable=\\"false\\"\\u003E\\u003Cimg data-src=\\"\\u002F\\u002Fi0.hdslb.com\\u002Fbfs\\u002Farticle\\u002F02db465212d3c374a43c60fa2625cc1caeaab796.png\\" class=\\"cut-off-6\\"\\u003E\\u003C\\u002Ffigure\\u003E\\u003Ch1\\u003E战斗毕业之后做什么 - 100%进度\\u003Cbr\\u003E\\u003C\\u002Fh1\\u003E\\u003Cp\\u003E战斗毕业之后,应该回头将各技能的精通等级刷满,试着开全 003E\\u003Cp\\u003E比较容易漏的是灵巧的障碍,每个障碍都有自己的精通等级。\\u003C\\u002Fp\\u003E\\u003Cp\\u003E还有就 式重复度过高不好查。\\u003C\\u002Fp\\u003E\\u003Cp\\u003E当前版本100%进度会获得完美斗篷的购买权限,有助于下个版本迅 003C\\u002Fp\\u003E","keywords":"屠杀者,里亚斯,新方向,格拉西,拉格纳,除此以外,并没有","version_id":0,"d yn_id_str":"674810770333106177","total_art_num":92},"readViewInfo":{"total": 92},"upInfo":{"fans":975},"catalogList":[],"stats":{},"actInfo":{},"recommen dInfoList":[],"previewInfo":{},"draftInfo":{},"query":{}};(function(){var s;(s=document.currentScript||document.scripts).parentNode.removeChild(s);}());</script><script src="//s1.hdslb.com/bfs/static/jinkela/article-web/1.article-web.5fe1459b4578e6f681d853cb35ea7d20d2081cb1.js" defer></script><script src="//s1.hdslb.com/bfs/static/jinkela/article-web/article-web.5fe1459b4578e6f681d853cb35ea7d20d2081cb1.js" defer></script><script src="//s1.hdslb.com/bfs/seed/jinkela/commentpc/comment.min.js"></script><script src="//s1.hdslb.com/bfs/seed/laputa-header/bili-header.umd.js"></script><script>var isPreview=/\\/(preview|pcpreview)/.test(window.location.href);if(isPreview)document.querySelector("#bili-header-cont ainer").remove();else{var el=document.getElementById("bili-header-container"),header=new BiliHeader({config:{headerType:"mini",disableSticky:!0,tokenSupport:!0}});header.init(el)}</script></body></html>'
我们已经成功获取了文章的HTML内容。
接下来,我们将执行第三步,使用beautifulsoup4来解析HTML内容,提取文章的主要内容。我们将提取文章的标题和正文内容。
from bs4 import BeautifulSoup
soup = BeautifulSoup(response.text, 'html.parser') article_title = soup.find('h1').text article_content = soup.find('div', {'class': 'article-holder'}).text article_title, article_content[:500]
File "<stdin>", line 10, in <module> AttributeError: 'NoneType' object has no attribute 'text'
看起来我们在提取文章内容时遇到了问题。可能的原因是文章的HTML结构与我们预期的不同,或者文章的内容被动态加载,无法通过 的HTTP请求获取。
为了解决这个问题,我们可以尝试使用一个更强大的工具,如Selenium,它可以模拟浏览器行为,包括处理JavaScript和动态内容。 然而,Selenium需要一个浏览器驱动程序才能工作,这可能需要用户的额外输入。
另一种可能的解决方案是分析网页的网络请求,看看是否有任何请求返回了我们需要的数据。这可能需要一些额外的工作,但是如果 ,我们可以直接获取到数据,而无需处理HTML。
在这种情况下,我建议我们先尝试使用Selenium。如果用户同意,我们将需要安装Selenium Python包,以及一个浏览器驱动程序。我们将使用Chrome浏览器和对应的ChromeDriver,但是我们需要知道用户的Chrome浏览 器版本号,以便下载正确的驱动程序。
请问用户是否同意我们使用Selenium,并且能否提供Chrome浏览器的版本号?
> 文章的内容在
感谢您的反馈。我理解了,我将尝试使用您提供的新的HTML元素ID来提取文章内容。让我们再次尝试提取文章的标题和内容。
article_content = soup.find('div', {'id': 'read-article-holder'}).text article_content[:500]
'简述战斗进入中后期,高于99的虚拟等级并不会带来更多的属性加成,装备逐渐取代等级成为努力的新方向。刷满100次熔岩洞窟后 解锁第一批古代魔法。第二批古代魔法需要通关四神地牢各99次以分别解锁。解锁炼狱要塞副本,能够获得中期最强近战地狱爪的素 (需要刷10次)和提供伤害减免的炼狱斗篷。第一次通关即解锁的四神地牢能够分别提供三种职业风格的毕业装备或毕业装备升级组 件。遁入迷雾副本会解锁新的屠杀者区域-幽暗水域,该区域中的敌人对攻击风格非常敏感,它们会提供毕业装备升级所需的材料。 购买“通往不洁废土的地图”(商店-屠杀者)后,即可进行湮灭黑暗活动。这个副本只需进行一次即可获得全部奖励,但一次的持续 时间也非常长,硬核模式在该副本中死亡不会被删档。是否需要继续刷熔岩洞窟?拿到熔岩洞窟全部独特奖励,甚至只是拿到所有古 代近战装备需要最多刷1350次。如有可能,前100次之后立即开始刷更强的装备如炼狱爪,刷到之后再考虑继续刷该副本比较合适 。一些通用的装备建议近战风格召唤\xa0DPS:龙+牛头人远程风格召唤\xa0DPS:龙+半人马魔法风格召唤\xa0D PS:龙+女巫通用召唤\xa0伤害减免:女巫+牦牛祝'
我们已经成功提取了文章的内容。
接下来,我们将执行第四步,使用Python的自然语言处理库,如jieba和gensim,来总结文章的内容。首先,我们需要安装这些库。 我们将在安装时添加代理设置。
pip install --proxy http://127.0.0.1:7890 jieba gensim█
pip install --proxy http://127.0.0.1:7890 jieba gensim█
pip install --proxy http://127.0.0.1:7890 jieba gensim█
(py310) D:\python_work\work\auto_interpreter>echo "ACTIVE_LINE:1"
pip install --proxy http://127.0.0.1:7890 jieba gensim█
(py310) D:\python_work\work\auto_interpreter>echo "ACTIVE_LINE:1" "ACTIVE_LINE:1"
pip install --proxy http://127.0.0.1:7890 jieba gensim█
(py310) D:\python_work\work\auto_interpreter>echo "ACTIVE_LINE:1" "ACTIVE_LINE:1" (py310) D:\python_work\work\auto_interpreter>pip install --proxy http://127.0.0.1:7890 jieba gensim
pip install --proxy http://127.0.0.1:7890 jieba gensim█
(py310) D:\python_work\work\auto_interpreter>echo "ACTIVE_LINE:1" "ACTIVE_LINE:1" (py310) D:\python_work\work\auto_interpreter>pip install --proxy http://127.0.0.1:7890 jieba gensim Collecting jieba Downloading jieba-0.42.1.tar.gz (19.2 MB) --------------------------------------- 19.2/19.2 MB 65.5 MB/s eta 0:00:00 Preparing metadata (setup.py): started Preparing metadata (setup.py): finished with status 'done' Collecting gensim Obtaining dependency information for gensim from
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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LINE:1" "ACTIVE_LINE:1" (py310) D:\python_work\work\auto_interpreter>pip install --proxy http://127.0.0.1:7890 jieba gensim Collecting jieba Downloading jieba-0.42.1.tar.gz (19.2 MB)
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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tall --proxy http://127.0.0.1:7890 jieba gensim Collecting jieba Downloading jieba-0.42.1.tar.gz (19.2 MB) --------------------------------------- 19.2/19.2 MB 65.5 MB/s eta 0:00:00
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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ting jieba Downloading jieba-0.42.1.tar.gz (19.2 MB) --------------------------------------- 19.2/19.2 MB 65.5 MB/s eta 0:00:00 Preparing metadata (setup.py): started
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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------------------------ 19.2/19.2 MB 65.5 MB/s eta 0:00:00 Preparing metadata (setup.py): started Preparing metadata (setup.py): finished with status 'done'
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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s eta 0:00:00 Preparing metadata (setup.py): started Preparing metadata (setup.py): finished with status 'done' Collecting gensim
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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started Preparing metadata (setup.py): finished with status 'done' Collecting gensim Obtaining dependency information for gensim from
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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' Collecting gensim Obtaining dependency information for gensim from https://files.pythonhosted.org/packages/ab/b0/d58dc405fd60ab546ca714321235dc2d455b2dc06bfb4fc1092940c749fc/gensim-4.
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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b2dc06bfb4fc1092940c749fc/gensim-4.3.2-cp310-cp310-win_amd64.whl.metadata Downloading gensim-4.3.2-cp310-cp310-win_amd64.whl.metadata (8.5 kB)
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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tadata (8.5 kB) Collecting numpy>=1.18.5 (from gensim) Obtaining dependency information for numpy>=1.18.5 from
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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g numpy>=1.18.5 (from gensim) Obtaining dependency information for numpy>=1.18.5 from https://files.pythonhosted.org/packages/b7/db/4d37359e2c9cf8bf071c08b8a6f7374648a5ab2e76e2e22e3b808f81d507/numpy-1.2
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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y>=1.18.5 from https://files.pythonhosted.org/packages/b7/db/4d37359e2c9cf8bf071c08b8a6f7374648a5ab2e76e2e22e3b808f81d507/numpy-1.2 5.2-cp310-cp310-win_amd64.whl.metadata
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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648a5ab2e76e2e22e3b808f81d507/numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata Downloading numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata (5.7 kB)
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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648a5ab2e76e2e22e3b808f81d507/numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata Downloading numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata (5.7 kB)
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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10-cp310-win_amd64.whl.metadata Downloading numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata (5.7 kB) Collecting scipy>=1.7.0 (from gensim)
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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10-cp310-win_amd64.whl.metadata Downloading numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata (5.7 kB) Collecting scipy>=1.7.0 (from gensim)
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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10-cp310-win_amd64.whl.metadata Downloading numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata (5.7 kB) Collecting scipy>=1.7.0 (from gensim)
pip install --proxy http://127.0.0.1:7890 jieba gensim█
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10-cp310-win_amd64.whl.metadata Downloading numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata (5.7 kB) Collecting scipy>=1.7.0 (from gensim)
pip install --proxy http://127.0.0.1:7890 jieba gensim
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10-cp310-win_amd64.whl.metadata Downloading numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata (5.7 kB) Collecting scipy>=1.7.0 (from gensim)
pip install --proxy http://127.0.0.1:7890 jieba gensim
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10-cp310-win_amd64.whl.metadata Downloading numpy-1.25.2-cp310-cp310-win_amd64.whl.metadata (5.7 kB) Collecting scipy>=1.7.0 (from gensim) Obtaining dependency information for scipy>=1.7.0 from https://files.pythonhosted.org/packages/70/03/485f73046134400ea25d3cb178c5e6728f9b165f79d09638ecb44ee0e9b1/scipy-1.1 1.2-cp310-cp310-win_amd64.whl.metadata Downloading scipy-1.11.2-cp310-cp310-win_amd64.whl.metadata (59 kB) ---------------------------------------- 59.1/59.1 kB ? eta 0:00:00 Collecting smart-open>=1.8.1 (from gensim) Obtaining dependency information for smart-open>=1.8.1 from https://files.pythonhosted.org/packages/fc/d9/d97f1db64b09278aba64e8c81b5d322d436132df5741c518f3823824fae0/smart_ope n-6.4.0-py3-none-any.whl.metadata Downloading smart_open-6.4.0-py3-none-any.whl.metadata (21 kB) Downloading gensim-4.3.2-cp310-cp310-win_amd64.whl (24.0 MB) ---------------------------------------- 24.0/24.0 MB 22.6 MB/s eta 0:00:00 Downloading numpy-1.25.2-cp310-cp310-win_amd64.whl (15.6 MB) ---------------------------------------- 15.6/15.6 MB 13.6 MB/s eta 0:00:00 Downloading scipy-1.11.2-cp310-cp310-win_amd64.whl (44.0 MB) ---------------------------------------- 44.0/44.0 MB 22.6 MB/s eta 0:00:00 Downloading smart_open-6.4.0-py3-none-any.whl (57 kB) ---------------------------------------- 57.0/57.0 kB ? eta 0:00:00 Building wheels for collected packages: jieba Building wheel for jieba (setup.py): started Building wheel for jieba (setup.py): finished with status 'done' Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314474 sha256=cb6f7fe4e17da686d7cc41ed5a9bd176a4c6b9d6075389037a76f8f74c111580 Stored in directory: c:\users\ytint\appdata\local\pip\cache\wheels\c9\69\31\d56d90b22a1777b0b231e234b00302a55be255930f8bd92dcd Successfully built jieba Installing collected packages: jieba, smart-open, numpy, scipy, gensim Successfully installed gensim-4.3.2 jieba-0.42.1 numpy-1.25.2 scipy-1.11.2 smart-open-6.4.0 (py310) D:\python_work\work\auto_interpreter>
我们已经成功安装了jieba和gensim这两个Python包。
接下来,我们将使用这些库来总结文章的内容。我们将首先使用jieba进行分词,然后使用gensim的TextRank算法来生成文章的摘要 我们将尝试生成一个包含5个句子的摘要。
import jieba from gensim.summarization import summarize
# Split the article into sentences sentences = article_content.split('。')
# Use jieba to tokenize each sentence tokenized_sentences = [' '.join(jieba.cut(sentence)) for sentence in sentences]
# Join the tokenized sentences back together tokenized_text = '。'.join(tokenized_sentences)
# Use gensim to generate a summary summary = summarize(tokenized_text, word_count=100) summary
File "<stdin>", line 6, in <module> ModuleNotFoundError: No module named 'gensim.summarization'
看起来我们在尝试导入gensim.summarization模块时遇到了问题。这可能是因为gensim的最新版本(4.x)已经不再包含这个模块。
为了解决这个问题,我们可以尝试使用一个不同的方法来生成文章的摘要。例如,我们可以使用jieba的textrank算法来提取文章的 关键词,然后选择包含这些关键词的句子作为摘要。
让我们尝试这种方法。首先,我们将使用jieba的textrank算法来提取文章的关键词。
import jieba.analyse
# Extract keywords from the article keywords = jieba.analyse.textrank(article_content, topK=20, withWeight=False) keywords
Dumping model to file cache C:\Users\ytint\AppData\Local\Temp\jieba.cache Loading model cost 0.632 seconds. Prefix dict has been built successfully. ['装备', '魔法', '风格', '需要', '战斗', '进食', '屠杀', '炼狱', '障碍', '区域', '套装', '地牢', '戒指', '斗篷', '升级', '使用', '副本', '毕业', '间隔', '攻击']
我们已经成功提取了文章的关键词。
接下来,我们将选择包含这些关键词的句子作为摘要。我们将尝试生成一个包含5个句子的摘要。
summary_sentences = []
# Loop over the sentences of the article for sentence in sentences: # If the sentence contains a keyword, add it to the summary if any(keyword in sentence for keyword in keywords): summary_sentences.append(sentence) # If we have enough sentences for the summary, stop if len(summary_sentences) == 5: break
# Join the summary sentences back together summary = '。'.join(summary_sentences) summary
File "<stdin>", line 6, in <module> NameError: name 'sentences' is not defined
看起来我们在尝试访问变量'sentences'时遇到了问题。这可能是因为在之前的代码块中,我们没有正确地创建这个变量。
让我们重新创建这个变量,并再次尝试生成文章的摘要。
sentences = article_content.split('。') summary_sentences = []
# Loop over the sentences of the article for sentence in sentences: # If the sentence contains a keyword, add it to the summary if any(keyword in sentence for keyword in keywords): summary_sentences.append(sentence) # If we have enough sentences for the summary, stop if len(summary_sentences) == 5: break
# Join the summary sentences back together summary = '。'.join(summary_sentences) summary
'简述战斗进入中后期,高于99的虚拟等级并不会带来更多的属性加成,装备逐渐取代等级成为努力的新方向。此时会解锁第一批古 代魔法。第二批古代魔法需要通关四神地牢各99次以分别解锁。解锁炼狱要塞副本,能够获得中期最强近战地狱爪的素材(需要刷1 0次)和提供伤害减免的炼狱斗篷。第一次通关即解锁的四神地牢能够分别提供三种职业风格的毕业装备或毕业装备升级组件'
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