spaCy/spacy/displacy/__init__.py

"""
spaCy's built in visualization suite for dependencies and named entities.

DOCS: https://spacy.io/api/top-level#displacy
USAGE: https://spacy.io/usage/visualizers
"""
from typing import Union, Iterable, Optional, Dict, Any, Callable
import warnings

from .render import DependencyRenderer, EntityRenderer, SpanRenderer
from ..tokens import Doc, Span
from ..errors import Errors, Warnings
from ..util import is_in_jupyter
from ..util import find_available_port


_html = {}
RENDER_WRAPPER = None


def render(
    docs: Union[Iterable[Union[Doc, Span, dict]], Doc, Span, dict],
    style: str = "dep",
    page: bool = False,
    minify: bool = False,
    jupyter: Optional[bool] = None,
    options: Dict[str, Any] = {},
    manual: bool = False,
) -> str:
    """Render displaCy visualisation.

    docs (Union[Iterable[Union[Doc, Span, dict]], Doc, Span, dict]]): Document(s) to visualise.
        a 'dict' is only allowed here when 'manual' is set to True
    style (str): Visualisation style, 'dep' or 'ent'.
    page (bool): Render markup as full HTML page.
    minify (bool): Minify HTML markup.
    jupyter (bool): Override Jupyter auto-detection.
    options (dict): Visualiser-specific options, e.g. colors.
    manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
    RETURNS (str): Rendered SVG or HTML markup.

    DOCS: https://spacy.io/api/top-level#displacy.render
    USAGE: https://spacy.io/usage/visualizers
    """
    factories = {
        "dep": (DependencyRenderer, parse_deps),
        "ent": (EntityRenderer, parse_ents),
        "span": (SpanRenderer, parse_spans),
    }
    if style not in factories:
        raise ValueError(Errors.E087.format(style=style))
    if isinstance(docs, (Doc, Span, dict)):
        docs = [docs]
    docs = [obj if not isinstance(obj, Span) else obj.as_doc() for obj in docs]
    if not all(isinstance(obj, (Doc, Span, dict)) for obj in docs):
        raise ValueError(Errors.E096)
    renderer_func, converter = factories[style]
    renderer = renderer_func(options=options)
    parsed = [converter(doc, options) for doc in docs] if not manual else docs  # type: ignore
    if manual:
        for doc in docs:
            if isinstance(doc, dict) and "ents" in doc:
                doc["ents"] = sorted(doc["ents"], key=lambda x: (x["start"], x["end"]))
    _html["parsed"] = renderer.render(parsed, page=page, minify=minify).strip()  # type: ignore
    html = _html["parsed"]
    if RENDER_WRAPPER is not None:
        html = RENDER_WRAPPER(html)
    if jupyter or (jupyter is None and is_in_jupyter()):
        # return HTML rendered by IPython display()
        # See #4840 for details on span wrapper to disable mathjax
        from IPython.core.display import display, HTML

        return display(HTML('<span class="tex2jax_ignore">{}</span>'.format(html)))
    return html


def serve(
    docs: Union[Iterable[Doc], Doc],
    style: str = "dep",
    page: bool = True,
    minify: bool = False,
    options: Dict[str, Any] = {},
    manual: bool = False,
    port: int = 5000,
    host: str = "0.0.0.0",
    auto_select_port: bool = False,
) -> None:
    """Serve displaCy visualisation.

    docs (list or Doc): Document(s) to visualise.
    style (str): Visualisation style, 'dep' or 'ent'.
    page (bool): Render markup as full HTML page.
    minify (bool): Minify HTML markup.
    options (dict): Visualiser-specific options, e.g. colors.
    manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
    port (int): Port to serve visualisation.
    host (str): Host to serve visualisation.
    auto_select_port (bool): Automatically select a port if the specified port is in use.

    DOCS: https://spacy.io/api/top-level#displacy.serve
    USAGE: https://spacy.io/usage/visualizers
    """
    from wsgiref import simple_server

    port = find_available_port(port, host, auto_select_port)

    if is_in_jupyter():
        warnings.warn(Warnings.W011)
    render(docs, style=style, page=page, minify=minify, options=options, manual=manual)
    httpd = simple_server.make_server(host, port, app)
    print(f"\nUsing the '{style}' visualizer")
    print(f"Serving on http://{host}:{port} ...\n")
    try:
        httpd.serve_forever()
    except KeyboardInterrupt:
        print(f"Shutting down server on port {port}.")
    finally:
        httpd.server_close()


def app(environ, start_response):
    headers = [("Content-type", "text/html; charset=utf-8")]
    start_response("200 OK", headers)
    res = _html["parsed"].encode(encoding="utf-8")
    return [res]


def parse_deps(
    orig_doc: Union[Doc, Span], options: Dict[str, Any] = {}
) -> Dict[str, Any]:
    """Generate dependency parse in {'words': [], 'arcs': []} format.

    orig_doc (Union[Doc, Span]): Document to parse.
    options (Dict[str, Any]): Dependency parse specific visualisation options.
    RETURNS (dict): Generated dependency parse keyed by words and arcs.
    """
    if isinstance(orig_doc, Span):
        orig_doc = orig_doc.as_doc()
    doc = Doc(orig_doc.vocab).from_bytes(
        orig_doc.to_bytes(exclude=["user_data", "user_hooks"])
    )
    if not doc.has_annotation("DEP"):
        warnings.warn(Warnings.W005)
    if options.get("collapse_phrases", False):
        with doc.retokenize() as retokenizer:
            for np in list(doc.noun_chunks):
                attrs = {
                    "tag": np.root.tag_,
                    "lemma": np.root.lemma_,
                    "ent_type": np.root.ent_type_,
                }
                retokenizer.merge(np, attrs=attrs)  # type: ignore[arg-type]
    if options.get("collapse_punct", True):
        spans = []
        for word in doc[:-1]:
            if word.is_punct or not word.nbor(1).is_punct:
                continue
            start = word.i
            end = word.i + 1
            while end < len(doc) and doc[end].is_punct:
                end += 1
            span = doc[start:end]
            spans.append((span, word.tag_, word.lemma_, word.ent_type_))
        with doc.retokenize() as retokenizer:
            for span, tag, lemma, ent_type in spans:
                attrs = {"tag": tag, "lemma": lemma, "ent_type": ent_type}
                retokenizer.merge(span, attrs=attrs)  # type: ignore[arg-type]
    fine_grained = options.get("fine_grained")
    add_lemma = options.get("add_lemma")
    words = [
        {
            "text": w.text,
            "tag": w.tag_ if fine_grained else w.pos_,
            "lemma": w.lemma_ if add_lemma else None,
        }
        for w in doc
    ]
    arcs = []
    for word in doc:
        if word.i < word.head.i:
            arcs.append(
                {"start": word.i, "end": word.head.i, "label": word.dep_, "dir": "left"}
            )
        elif word.i > word.head.i:
            arcs.append(
                {
                    "start": word.head.i,
                    "end": word.i,
                    "label": word.dep_,
                    "dir": "right",
                }
            )
    return {"words": words, "arcs": arcs, "settings": get_doc_settings(orig_doc)}


def parse_ents(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
    """Generate named entities in [{start: i, end: i, label: 'label'}] format.

    doc (Doc): Document to parse.
    options (Dict[str, Any]): NER-specific visualisation options.
    RETURNS (dict): Generated entities keyed by text (original text) and ents.
    """
    kb_url_template = options.get("kb_url_template", None)
    ents = [
        {
            "start": ent.start_char,
            "end": ent.end_char,
            "label": ent.label_,
            "kb_id": ent.kb_id_ if ent.kb_id_ else "",
            "kb_url": kb_url_template.format(ent.kb_id_) if kb_url_template else "#",
        }
        for ent in doc.ents
    ]
    if not ents:
        warnings.warn(Warnings.W006)
    title = doc.user_data.get("title", None) if hasattr(doc, "user_data") else None
    settings = get_doc_settings(doc)
    return {"text": doc.text, "ents": ents, "title": title, "settings": settings}


def parse_spans(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
    """Generate spans in [{start_token: i, end_token: i, label: 'label'}] format.

    doc (Doc): Document to parse.
    options (Dict[str, any]): Span-specific visualisation options.
    RETURNS (dict): Generated span types keyed by text (original text) and spans.
    """
    kb_url_template = options.get("kb_url_template", None)
    spans_key = options.get("spans_key", "sc")
    spans = [
        {
            "start": span.start_char,
            "end": span.end_char,
            "start_token": span.start,
            "end_token": span.end,
            "label": span.label_,
            "kb_id": span.kb_id_ if span.kb_id_ else "",
            "kb_url": kb_url_template.format(span.kb_id_) if kb_url_template else "#",
        }
        for span in doc.spans.get(spans_key, [])
    ]
    tokens = [token.text for token in doc]

    if not spans:
        keys = list(doc.spans.keys())
        warnings.warn(Warnings.W117.format(spans_key=spans_key, keys=keys))
    title = doc.user_data.get("title", None) if hasattr(doc, "user_data") else None
    settings = get_doc_settings(doc)
    return {
        "text": doc.text,
        "spans": spans,
        "title": title,
        "settings": settings,
        "tokens": tokens,
    }


def set_render_wrapper(func: Callable[[str], str]) -> None:
    """Set an optional wrapper function that is called around the generated
    HTML markup on displacy.render. This can be used to allow integration into
    other platforms, similar to Jupyter Notebooks that require functions to be
    called around the HTML. It can also be used to implement custom callbacks
    on render, or to embed the visualization in a custom page.

    func (callable): Function to call around markup before rendering it. Needs
        to take one argument, the HTML markup, and should return the desired
        output of displacy.render.
    """
    global RENDER_WRAPPER
    if not hasattr(func, "__call__"):
        raise ValueError(Errors.E110.format(obj=type(func)))
    RENDER_WRAPPER = func


def get_doc_settings(doc: Doc) -> Dict[str, Any]:
    return {
        "lang": doc.lang_,
        "direction": doc.vocab.writing_system.get("direction", "ltr"),
    }