├── models ├── cv.pkl ├── mnb.pkl ├── pac.pkl ├── tfv.pkl ├── tfv_vec.pkl ├── mnb_clf_joblib └── mnb_clf_joblib.pkl ├── app.js ├── requirements.txt ├── .gitignore ├── README.md ├── machine_learning_news_scrapper.py └── LICENSE /models/cv.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/metacall/ml-news-article-scraper-example/HEAD/models/cv.pkl -------------------------------------------------------------------------------- /models/mnb.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/metacall/ml-news-article-scraper-example/HEAD/models/mnb.pkl -------------------------------------------------------------------------------- /models/pac.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/metacall/ml-news-article-scraper-example/HEAD/models/pac.pkl -------------------------------------------------------------------------------- /models/tfv.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/metacall/ml-news-article-scraper-example/HEAD/models/tfv.pkl -------------------------------------------------------------------------------- /models/tfv_vec.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/metacall/ml-news-article-scraper-example/HEAD/models/tfv_vec.pkl -------------------------------------------------------------------------------- /models/mnb_clf_joblib: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/metacall/ml-news-article-scraper-example/HEAD/models/mnb_clf_joblib -------------------------------------------------------------------------------- /models/mnb_clf_joblib.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/metacall/ml-news-article-scraper-example/HEAD/models/mnb_clf_joblib.pkl -------------------------------------------------------------------------------- /app.js: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env node 2 | 3 | const readline = require("readline-sync"); 4 | const { similarNews } = require('./machine_learning_news_scrapper.py'); 5 | 6 | console.log("Enter the News URL:"); 7 | url = String(readline.question()); 8 | 9 | console.table(similarNews(url)); 10 | 11 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | beautifulsoup4==4.9.1 2 | bs4==0.0.1 3 | certifi==2020.6.20 4 | chardet==3.0.4 5 | click==7.1.2 6 | cssselect==1.1.0 7 | feedfinder2==0.0.4 8 | feedparser==6.0.2 9 | filelock==3.0.12 10 | google==3.0.0 11 | idna==2.10 12 | jieba3k==0.35.1 13 | joblib==1.0.1 14 | lxml==4.6.3 15 | newspaper3k==0.2.8 16 | nltk==3.5 17 | numpy==1.19.5 18 | pandas==1.1.5 19 | Pillow==8.1.2 20 | python-dateutil==2.8.1 21 | pytz==2021.1 22 | PyYAML==5.4.1 23 | regex==2021.3.17 24 | requests==2.24.0 25 | requests-file==1.5.1 26 | scikit-learn==0.22.1 27 | scipy==1.5.4 28 | sgmllib3k==1.0.0 29 | six==1.15.0 30 | sklearn==0.0 31 | soupsieve==2.0.1 32 | threadpoolctl==2.1.0 33 | tinysegmenter==0.3 34 | tldextract==3.1.0 35 | tqdm==4.59.0 36 | urllib3==1.25.9 37 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | ###################### 2 | # Python # 3 | ###################### 4 | 5 | # Byte-compiled / optimized / DLL files 6 | __pycache__/ 7 | *.py[cod] 8 | *$py.class 9 | 10 | # C extensions 11 | *.so 12 | 13 | # Distribution / packaging 14 | .Python 15 | env/ 16 | #build/ 17 | develop-eggs/ 18 | dist/ 19 | downloads/ 20 | eggs/ 21 | .eggs/ 22 | lib/ 23 | lib64/ 24 | parts/ 25 | sdist/ 26 | var/ 27 | *.egg-info/ 28 | .installed.cfg 29 | *.egg 30 | 31 | # PyInstaller 32 | # Usually these files are written by a python script from a template 33 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 34 | *.manifest 35 | *.spec 36 | 37 | # Installer logs 38 | pip-log.txt 39 | pip-delete-this-directory.txt 40 | 41 | # Unit test / coverage reports 42 | htmlcov/ 43 | .tox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *,cover 50 | .hypothesis/ 51 | 52 | # Translations 53 | *.mo 54 | *.pot 55 | 56 | # Django stuff: 57 | *.log 58 | local_settings.py 59 | 60 | # Flask instance folder 61 | instance/ 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # IPython Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # dotenv 82 | .env 83 | 84 | # virtualenv 85 | venv/ 86 | ENV/ 87 | 88 | # Spyder project settings 89 | .spyderproject 90 | 91 | # Others 92 | .project 93 | .settings 94 | .classpath 95 | .idea 96 | 97 | ###################### 98 | # Node # 99 | ###################### 100 | 101 | # Logs 102 | logs 103 | *.log 104 | npm-debug.log* 105 | 106 | # Runtime data 107 | pids 108 | *.pid 109 | *.seed 110 | 111 | # Directory for instrumented libs generated by jscoverage/JSCover 112 | lib-cov 113 | 114 | # Coverage directory used by tools like istanbul 115 | coverage 116 | 117 | # Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files) 118 | .grunt 119 | 120 | # node-waf configuration 121 | .lock-wscript 122 | 123 | # Compiled binary addons (http://nodejs.org/api/addons.html) 124 | build/Release 125 | 126 | # Dependency directories 127 | node_modules 128 | jspm_packages 129 | 130 | # Optional npm cache directory 131 | .npm 132 | 133 | # Optional REPL history 134 | .node_repl_history 135 | 136 | ###################### 137 | # OS generated files # 138 | ###################### 139 | 140 | __MACOSX 141 | .DS_Store 142 | ._* 143 | 144 | .Spotlight-V100 145 | .Trashes 146 | 147 | ehthumbs.db 148 | Thumbs.db 149 | 150 | ############ 151 | # Packages # 152 | ############ 153 | # it's better to unpack these files and commit the raw source 154 | # git has its own built in compression methods 155 | *.7z 156 | *.dmg 157 | *.gz 158 | *.iso 159 | *.jar 160 | *.rar 161 | *.tar 162 | *.zip 163 | .DS_Store 164 | 165 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Polyglot Machine Learning example for scraping similar news articles 2 | 3 |
4 | Machine Learning Polyglot with Python and NodeJS 5 |
6 | 7 | In this example, we will see how we can work with Machine Learning applications written in Python with a NodeJS Script, to build a **Polyglot Machine Learning application for scraping similar news articles**. 8 | 9 | ## Install 10 | 11 | Install MetaCall CLI: 12 | 13 | ```sh 14 | $ curl -sL https://raw.githubusercontent.com/metacall/install/master/install.sh | sh 15 | ``` 16 | 17 | Install application dependencies: 18 | 19 | - For Python: `metacall pip3 install -r requirements.txt` 20 | - For NodeJS: `metacall npm i readline-sync` 21 | 22 | ## Run the Example 23 | 24 | ```sh 25 | $ metacall app.js 26 | ``` 27 | 28 | Once the application is kick-started, you will be prompted to enter a News Article which you would like to find similar articles for. Let's use this sample article for testing our application: **https://www.nytimes.com/2021/03/23/business/teslas-autopilot-safety-investigations.html** 29 | 30 | Here is the application output: 31 | 32 | ``` 33 | $ metacall app.js 34 | Information: Global configuration loaded from /gnu/store/5cxmq6y8z24ijnvhh6lndgpriwnhf3jl-metacall-0.3.17/configurations/global.json 35 | Enter the News URL: 36 | https://www.nytimes.com/2021/03/23/business/teslas-autopilot-safety-investigations.html 37 | ┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┬───────────────┐ 38 | │ (index) │ Values │ 39 | ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┼───────────────┤ 40 | │ https://auto.timesofindia.com/news/others/teslas-autopilot-technology-faces-fresh-scrutiny/articleshow/81652823.cms │ '83.68405286' │ 41 | │ https://www.autosafety.org/teslas-autopilot-technology-faces-fresh-scrutiny/ │ '60.35694007' │ 42 | │ https://www.anandmarket.in/teslas-autopilot-technology-faces-fresh-scrutiny/ │ '94.97681053' │ 43 | │ https://www.entrepreneur.com/article/367724 │ '60.67538891' │ 44 | │ http://www.newsnetworks.in/india/teslas-autopilot-technology-faces-fresh-scrutiny/ │ '0.' │ 45 | └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┴───────────────┘ 46 | Script (app.js) loaded correctly 47 | ``` 48 | ## Deployment using MetaCall FaaS 49 | 50 | After deploying the application into the FaaS https://dashboard.metacall.io, it can be accessed with (change `` by the alias you used to sign up): 51 | 52 | ```sh 53 | curl -X POST https://api.metacall.io//ml-news-article-scraper-example/v1/call/similarNews -X POST --data '{ "url": "https://www.nytimes.com/2021/03/23/business/teslas-autopilot-safety-investigations.html" }' 54 | ``` 55 | ## LICENSE 56 | [Apache License 2.0](./LICENSE) 57 | 58 | 59 | -------------------------------------------------------------------------------- /machine_learning_news_scrapper.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | import re 3 | from newspaper import Article 4 | from sklearn.feature_extraction.text import TfidfVectorizer 5 | from sklearn.metrics.pairwise import cosine_similarity 6 | from sklearn.model_selection import train_test_split 7 | import joblib 8 | from googlesearch import search 9 | from urllib.parse import urlparse 10 | warnings.filterwarnings("ignore") 11 | 12 | def extractor(url): 13 | """ 14 | Extractor function that gets the article body from the URL 15 | Args: 16 | url: URL of the News Article/Source 17 | Returns: 18 | article: Raw Article Body 19 | article_title: Title of the Article that has been extracted 20 | """ 21 | 22 | article = Article(url) 23 | try: 24 | article.download() 25 | article.parse() 26 | except: 27 | pass 28 | 29 | #Get the article title and convert them to lower-case 30 | article_title = article.title 31 | article = article.text.lower() 32 | article = [article] 33 | return (article, article_title) 34 | 35 | 36 | def text_area_extractor(text): 37 | """ 38 | Textbox extractor function to preprocess and extract text 39 | Args: 40 | text: Raw Extracted Text from the News Article 41 | Returns: 42 | text: Preprocessed and clean text ready for analysis 43 | """ 44 | text = text.lower() 45 | text = re.sub(r'[^a-zA-Z0-9\s]', ' ', text) 46 | text = re.sub("(\\r|\r|\n)\\n$", " ", text) 47 | text = [text] 48 | return text 49 | 50 | def google_search(title, url): 51 | """ 52 | Function to perform a Google Search with the specified title and URL 53 | Args: 54 | title: Title of the Article 55 | url: URL of the specified article 56 | Returns: 57 | search_urls: Similar News Articles found over the Web 58 | source_sites: Hostname of the Articles founder over the Web 59 | """ 60 | target = url 61 | domain = urlparse(target).hostname 62 | search_urls = [] 63 | source_sites = [] 64 | for i in search(title, tld = "com", num = 10, start = 1, stop = 6): 65 | if "youtube" not in i and domain not in i: 66 | source_sites.append(urlparse(i).hostname) 67 | search_urls.append(i) 68 | return search_urls, source_sites 69 | 70 | def similarity(url_list, article): 71 | """ 72 | Function to check the similarity of the News Article through Cosine Similarity 73 | Args: 74 | url_list: List of the URLs similar to the news article 75 | article: Preprocessed article which would be vectorized 76 | Returns: 77 | cosine_cleaned: Cosine Similarity Scores of each URL passed 78 | average_score: Average value of the cosine similarity scores fetched 79 | """ 80 | article = article 81 | sim_tfv = TfidfVectorizer(stop_words ="english") 82 | sim_transform1 = sim_tfv.fit_transform(article) 83 | cosine = [] 84 | cosine_cleaned = [] 85 | cosine_average = 0 86 | count = 0 87 | 88 | for i in url_list: 89 | test_article, test_title = extractor(i) 90 | test_article = [test_article] 91 | sim_transform2 = sim_tfv.transform(test_article[0]) 92 | score = cosine_similarity(sim_transform1, sim_transform2) 93 | cosine.append(score*100) 94 | count+=1 95 | for i in cosine: 96 | x = str(i).replace('[','').replace(']','') 97 | cosine_cleaned.append(x) 98 | 99 | for i in cosine: 100 | if i !=0: 101 | cosine_average = cosine_average + i 102 | else: 103 | count-=1 104 | 105 | average_score = cosine_average/count 106 | average_score = str(average_score).replace('[','').replace(']','') 107 | average_score = float(average_score) 108 | return cosine_cleaned, average_score 109 | 110 | def handlelink(article_link): 111 | """ 112 | Classification function to take the article link and predict the similar news articles 113 | Args: 114 | article_link: URL of the article 115 | Returns: 116 | pred: Predicted news sources from the machine learning model 117 | article_title: Title of the Article 118 | article: Article fetched from the URL 119 | url: URL of the article 120 | """ 121 | 122 | job_pac = joblib.load('models/pac.pkl') 123 | job_vec = joblib.load('models/tfv.pkl') 124 | url = (article_link) 125 | article, article_title = extractor(article_link) 126 | pred = job_pac.predict(job_vec.transform(article)) 127 | return pred, article_title, article, url 128 | 129 | 130 | def similarNews(url): 131 | """ 132 | Driver function to return a dictionary with all the similar news and their similarity score 133 | Args: 134 | url: URL of the article 135 | Returns: 136 | dictionary: Dictionary containing all the similar news articles and their similarity score 137 | """ 138 | prediction, article_title, article, url = handlelink(article_link=url) 139 | url_list, sitename = google_search(article_title, url) 140 | similarity_score, avgScore = similarity(url_list, article) 141 | dictionary = dict(zip(url_list, similarity_score)) 142 | return dictionary 143 | 144 | 145 | 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