# coding: utf8
from __future__ import unicode_literals

import hug
from hug_middleware_cors import CORSMiddleware
import waitress
import spacy
import plac


MODELS = {}


@plac.annotations(
    models=("Comma-separated list of spaCy models", "positional", None, str),
    host=("Host to serve API", "option", "ho", str),
    port=("Port to serve API", "option", "p", int),
)
def main(models=None, host="0.0.0.0", port=8080):
    if not models:
        models = ["en_core_web_sm"]
    else:
        models = [m.strip() for m in models.split(",")]
    for model in models:
        load_model(model)
    # Serving Hug API
    app = hug.API(__name__)
    app.http.add_middleware(CORSMiddleware(app))
    waitress.serve(__hug_wsgi__, port=port)


def load_model(model):
    print("Loading model '{}'...".format(model))
    MODELS[model] = spacy.load(model)


def doc2json(doc: spacy.tokens.Doc, model: str):
    json_doc = {
        "text": doc.text,
        "text_with_ws": doc.text_with_ws,
        "cats": doc.cats,
        "is_tagged": doc.is_tagged,
        "is_parsed": doc.is_parsed,
        # "is_nered": doc.is_nered,
        "is_sentenced": doc.is_sentenced,
    }
    ents = [
        {"start": ent.start, "end": ent.end, "label": ent.label_} for ent in doc.ents
    ]
    if doc.is_sentenced:
        sents = [{"start": sent.start, "end": sent.end} for sent in doc.sents]
    else:
        sents = []
    if doc.is_tagged and doc.is_parsed:
        noun_chunks = [
            {"start": chunk.start, "end": chunk.end} for chunk in doc.noun_chunks
        ]
    else:
        noun_chunks = []
    tokens = [
        {
            "text": token.text,
            "text_with_ws": token.text_with_ws,
            "whitespace": token.whitespace_,
            "orth": token.orth,
            "i": token.i,
            "ent_type": token.ent_type_,
            "ent_iob": token.ent_iob_,
            "lemma": token.lemma_,
            "norm": token.norm_,
            "lower": token.lower_,
            "shape": token.shape_,
            "prefix": token.prefix_,
            "suffix": token.suffix_,
            "pos": token.pos_,
            "tag": token.tag_,
            "dep": token.dep_,
            "is_alpha": token.is_alpha,
            "is_ascii": token.is_ascii,
            "is_digit": token.is_digit,
            "is_lower": token.is_lower,
            "is_upper": token.is_upper,
            "is_title": token.is_title,
            "is_punct": token.is_punct,
            "is_left_punct": token.is_left_punct,
            "is_right_punct": token.is_right_punct,
            "is_space": token.is_space,
            "is_bracket": token.is_bracket,
            "is_currency": token.is_currency,
            "like_url": token.like_url,
            "like_num": token.like_num,
            "like_email": token.like_email,
            "is_oov": token.is_oov,
            "is_stop": token.is_stop,
            "is_sent_start": token.is_sent_start,
            "head": token.head.i,
        }
        for token in doc
    ]
    return {
        "model": model,
        "doc": json_doc,
        "ents": ents,
        "sents": sents,
        "noun_chunks": noun_chunks,
        "tokens": tokens,
    }


@hug.post("/parse")
def parse(model: str, text: str):
    nlp = MODELS[model]
    doc = nlp(text)
    return doc2json(doc, model)


@hug.post("/similarity")
def similarity(model: str, text1: str, text2: str):
    # We can always create Doc objects here, because the result is the same
    nlp = MODELS[model]
    doc1 = nlp(text1)
    doc2 = nlp(text2)
    return {"similarity": doc1.similarity(doc2)}


if __name__ == "__main__":
    plac.call(main)