From our internal testing, we found it to be very rare that GitHub Copilot suggestions included personal data verbatim from the training set. As the developer, you are always in charge.īecause the model powering GitHub Copilot was trained on publicly available code, its training set included personal data that was included in that code. Like any other code, code suggested by GitHub Copilot should be carefully tested, reviewed, and vetted. For suggested code, certain languages like Python, JavaScript, TypeScript, and Go might perform better compared to other programming languages. When converting comments written in non-English to code, there may be performance disparities when compared to English. And it may suggest old or deprecated uses of libraries and languages. GitHub Copilot can only hold a very limited context, so it may not make use of helpful functions defined elsewhere in your project or even in the same file. It is designed to generate the best code possible given the context it has access to, but it doesn’t test the code it suggests so the code may not always work, or even make sense. However, GitHub Copilot does not write perfect code. We also found that on average more than 27% of developers’ code files were generated by GitHub Copilot, and in certain languages like Python that goes up to 40%. Ingredients.In a recent evaluation, we found that users accepted on average 26% of all completions shown by GitHub Copilot. # that allows us to use the same index in to the list for # this loop goes from 0 to the number of units instead over Summary = clean(content.find(class_="topnote"))įor unit in content.find_all("span", class_="quantity"):įor name in content.find_all("span", class_="ingredient-name"): # find_all get's all elements with this class, whereas find (above)įor note in content.find_all(class_="recipe-note-description"): Yield_time = clean(yield_.li.next_sibling.next_sibling.span. Yield_servings = clean(yield_.li.span.next_sibling.next_sibling) # can manually move up/down/sideways in the parsed document # the class "recipe-time-yield" contains 2 li elements This is how you Yield_ = content.find(class_="recipe-time-yield") Title = clean(content.find(class_="recipe-title"))Īuthor = clean(content.find(class_="byline-name")) # This means find the first element which has the class "recipe-title" # This tells BeautifulSoup to parse the htmlĬontent = BeautifulSoup(f, "html.parser") # So we use the requests package to get the url this extension is # Pythonista cannot (or does not offer) getting the contents of a # This gets the URL this extension is called on Print('Running in Pythonista app, using test data.\n') # appex is the package which contains all the logic for being a share # This is mostly copied from the URL Extension template from Pythonista # enumerate gives us every item in a list and it's index # python figures out this is a String, so it will only allow you to # it can then be used anywhere inside this function (def()) # f-strings allow you to insert variables inside of strings # types of variables are not explicit in python, but they are enforced, Return unicodedata.normalize("NFKD", txt.get_text().strip()) # Found this online, NYTimes includes '\xa0' which is raw ascii ' ' Hopefully it will be a good reference! import clipboard I've included my script with a lot of comments. But, I encourage you to learn it! Perhaps it’s an answer to doing the same action as you’ve done here on your Mac. So, while you can use it to learn python, it would be very difficult to use without knowing python. Pythonista is just an editor for python with automation capabilities. Pythonista includes a package BeautifulSoup which makes it pretty easy to grab elements from html.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |