spaCy/website/usage/_visualizers/_html.jade

//- 💫 DOCS > USAGE > VISUALIZERS > HTML

p
    |  If you don't need the web server and just want to generate the markup
    |  – for example, to export it to a file or serve it in a custom
    |  way – you can use #[+api("displacy#render") #[code displacy.render]].
    |  It works the same way, but returns a string containing the markup.

+code("Example").
    import spacy
    from spacy import displacy

    nlp = spacy.load('en')
    doc1 = nlp(u'This is a sentence.')
    doc2 = nlp(u'This is another sentence.')
    html = displacy.render([doc1, doc2], style='dep', page=True)

p
    |  #[code page=True] renders the markup wrapped as a full HTML page.
    |  For minified and more compact HTML markup, you can set #[code minify=True].
    |  If you're rendering a dependency parse, you can also export it as an
    |  #[code .svg] file.

+aside("What's SVG?")
    |  Unlike other image formats, the SVG (Scalable Vector Graphics) uses XML
    |  markup that's easy to manipulate
    |  #[+a("https://www.smashingmagazine.com/2014/11/styling-and-animating-svgs-with-css/") using CSS] or
    |  #[+a("https://css-tricks.com/smil-is-dead-long-live-smil-a-guide-to-alternatives-to-smil-features/") JavaScript].
    |  Essentially, SVG lets you design with code, which makes it a perfect fit
    |  for visualizing dependency trees. SVGs can be embedded online in an
    |  #[code <img>] tag, or inlined in an HTML document. They're also
    |  pretty easy to #[+a("https://convertio.co/image-converter/") convert].

+code.
    svg = displacy.render(doc, style='dep')
    output_path = Path('/images/sentence.svg')
    output_path.open('w', encoding='utf-8').write(svg)

+infobox("Important note")
    |  Since each visualization is generated as a separate SVG, exporting
    |  #[code .svg] files only works if you're rendering #[strong one single doc]
    |  at a time. (This makes sense – after all, each visualization should be
    |  a standalone graphic.) So instead of rendering all #[code Doc]s at one,
    |  loop over them and export them separately.


+h(3, "examples-export-svg") Example: Export SVG graphics of dependency parses

+code("Example").
    import spacy
    from spacy import displacy
    from pathlib import Path

    nlp = spacy.load('en')
    sentences = ["This is an example.", "This is another one."]
    for sent in sentences:
        doc = nlp(sentence)
        svg = displacy.render(doc, style='dep')
        file_name = '-'.join([w.text for w in doc if not w.is_punct]) + '.svg'
        output_path = Path('/images/' + file_name)
        output_path.open('w', encoding='utf-8').write(svg)

p
    |  The above code will generate the dependency visualizations as to
    |  two files, #[code This-is-an-example.svg] and #[code This-is-another-one.svg].


+h(3, "manual-usage") Rendering data manually

p
    |  You can also use displaCy to manually render data. This can be useful if
    |  you want to visualize output from other libraries, like
    |  #[+a("http://www.nltk.org") NLTK] or
    |  #[+a("https://github.com/tensorflow/models/tree/master/research/syntaxnet") SyntaxNet].
    |  If you set #[code manual=True] on either #[code render()] or
    |  #[code serve()], you can pass in data in displaCy's format (instead of
    |  #[code Doc] objects). When setting #[code ents] manually, make sure to
    |  supply them in the right order, i.e. starting with the lowest start
    |  position.

+aside-code("Example").
    ex = [{'text': 'But Google is starting from behind.',
           'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
           'title': None}]
    html = displacy.render(ex, style='ent', manual=True)

+code("DEP input").
    {
        'words': [
            {'text': 'This', 'tag': 'DT'},
            {'text': 'is', 'tag': 'VBZ'},
            {'text': 'a', 'tag': 'DT'},
            {'text': 'sentence', 'tag': 'NN'}],
        'arcs': [
            {'start': 0, 'end': 1, 'label': 'nsubj', 'dir': 'left'},
            {'start': 2, 'end': 3, 'label': 'det', 'dir': 'left'},
            {'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}]
    }

+code("ENT input").
    {
        'text': 'But Google is starting from behind.',
        'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
        'title': None
    }

+h(3, "webapp") Using displaCy in a web application

p
    |  If you want to use the visualizers as part of a web application, for
    |  example to create something like our
    |  #[+a(DEMOS_URL + "/displacy") online demo], it's not recommended to
    |  only wrap and serve the displaCy renderer. Instead, you should only
    |  rely on the server to perform spaCy's processing capabilities, and use
    |  #[+a(gh("displacy")) displaCy.js] to render the JSON-formatted output.

+aside("Why not return the HTML by the server?")
    |  It's certainly possible to just have your server return the markup.
    |  But outputting raw, unsanitised HTML is risky and makes your app vulnerable to
    |  #[+a("https://en.wikipedia.org/wiki/Cross-site_scripting") cross-site scripting]
    |  (XSS). All your user needs to do is find a way to make spaCy return text
    |  like #[code <script src="malicious-code.js"><script>], which
    |  is pretty easy in NER mode. Instead of relying on the server to render
    |  and sanitise HTML, you can do this on the client in JavaScript.
    |  displaCy.js creates the markup as DOM nodes and will never insert raw
    |  HTML.

p
    |  The #[code parse_deps] function takes a #[code Doc] object and returns
    |  a dictionary in a format that can be rendered by displaCy.

+code("Example").
    import spacy
    from spacy import displacy

    nlp = spacy.load('en')

    def displacy_service(text):
        doc = nlp(text)
        return displacy.parse_deps(doc)

p
    |  Using a library like #[+a("https://falconframework.org/") Falcon] or
    |  #[+a("http://www.hug.rest/") Hug], you can easily turn the above code
    |  into a simple REST API that receives a text and returns a JSON-formatted
    |  parse. In your front-end, include #[+a(gh("displacy")) displacy.js] and
    |  initialise it with the API URL and the ID or query selector of the
    |  container to render the visualisation in, e.g. #[code '#displacy'] for
    |  #[code <div id="displacy">].

+code("script.js", "javascript").
    var displacy = new displaCy('http://localhost:8080', {
        container: '#displacy'
    })

    function parse(text) {
        displacy.parse(text);
    }

p
    |  When you call #[code parse()], it will make a request to your API,
    |  receive the JSON-formatted parse and render it in your container. To
    |  create an interactive experience, you could trigger this function by
    |  a button and read the text from an #[code <input>] field.