* Add displacy service

services/displacy.py

@@ -0,0 +1,340 @@
+#!/usr/bin/env python
+from __future__ import unicode_literals
+from __future__ import print_function
+import sys
+
+import falcon
+import json
+from os import path
+from collections import defaultdict
+import pprint
+import numpy
+
+import spacy.en
+from spacy.attrs import ORTH, SPACY, TAG, POS, ENT_IOB, ENT_TYPE
+from spacy.parts_of_speech import NAMES as UNIV_POS_NAMES
+
+try:
+  unicode
+except NameError:
+  unicode = str
+
+
+NLU = spacy.en.English()
+
+
+def merge_entities(doc):
+    ents = [(e[0].idx, e[len(e)-1].idx + len(e[len(e)-1]), e.label_, e.text)
+            for e in doc.ents if len(e) >= 2]
+    for start, end, label, lemma in ents:
+        merged = doc.merge(start, end, label, text, label)
+        assert merged != None
+
+
+def merge_nps(doc):
+    nps = [(np[0].idx, np[-1].idx + len(np[-1]), np.root.tag_, np.text)
+            for np in doc.noun_chunks if len(np) >= 2]
+    
+    for start, end, ent_type, lemma in nps:
+        doc.merge(start, end, u'NP', lemma, ent_type)
+
+
+def merge_punct(tokens):
+    # Merge punctuation onto its head
+    collect = False
+    start = None
+    merges = []
+
+    for word in tokens:
+        if word.whitespace_:
+            if collect:
+                span = tokens[start:word.i+1]
+                if len(span) >= 2:
+                    merges.append((
+                        span[0].idx,
+                        span[-1].idx + len(span[-1]),
+                        span.root.tag_,
+                        span.root.lemma_,
+                        span.root.ent_type_))
+                collect = False
+                start = None
+        elif not collect:
+            collect = True
+            start = word.i
+    if collect:
+        span = tokens[start:len(tokens)]
+        merges.append((span[0].idx, span[-1].idx + len(span[-1]),
+                       span.root.tag_, span.root.lemma_, span.root.ent_type_))
+    for merge in merges:
+         tokens.merge(*merge)
+
+
+def get_actions(parse_state, n_actions):
+    actions = []
+    queue = list(sorted(parse_state.queue))
+    stack = list(sorted(parse_state.stack))
+    stack = []
+    actions.append({'label': 'shift', 'key': 'S', 'binding': 38,
+                    'is_valid': NLU.parser.moves.is_valid(parse_state, 'S')})
+    actions.append({'label': 'left', 'key': 'L', 'binding': 37,
+                    'is_valid': NLU.parser.moves.is_valid(parse_state, 'L-det')})
+    actions.append({'label': 'predict', 'key': '_', 'binding': 32,
+                    'is_valid': bool(parse_state.queue or parse_state.stack)})
+    actions.append({'label': 'right', 'key': 'R', 'binding': 39,
+                    'is_valid': NLU.parser.moves.is_valid(parse_state, 'R-dobj')})
+    actions.append({'label': 'undo', 'key': '-', 'binding': 8,
+                    'is_valid': n_actions != 0})
+    actions.append({'label': 'reduce', 'key': 'D', 'binding': 40,
+                    'is_valid': NLU.parser.moves.is_valid(parse_state, 'D')})
+    return actions
+
+
+class Model(object):
+    def to_json(self):
+        return {name: _as_json(value) for name, value in self.__dict__.items()
+                if not name.startswith('_')}
+
+def _as_json(value):
+    if hasattr(value, 'to_json'):
+        return value.to_json()
+    elif isinstance(value, list):
+        return [_as_json(v) for v in value]
+    elif isinstance(value, set):
+        return {key: True for key in value}
+    else:
+        return value
+
+
+def _parse_history(history):
+    if history and history.endswith(','):
+        history = history[:-1]
+    history = history.strip().split(',') if history else tuple()
+    new_hist = []
+    history_length = len(history)
+    for action in history:
+        if action == '-':
+            if new_hist:
+                new_hist.pop()
+        else:
+            new_hist.append(action)
+    return new_hist, history_length
+
+
+def apply_edits(tokens, word_edits, tag_edits):
+    new_words = []
+    attrs = (POS, ENT_TYPE, ENT_IOB)
+    new_analysis = numpy.zeros(shape=(len(tokens), len(attrs)), dtype=numpy.int32)
+    for word in tokens:
+        key = str(word.i)
+        new_words.append(word_edits.get(key, word.orth_))
+        tag = tag_edits.get(key, word.pos_)
+        if tag in UNIV_POS_NAMES:
+            new_analysis[word.i, 0] = UNIV_POS_NAMES[tag]
+            # Set ent_type=0 and IOB="O"
+            new_analysis[word.i, 1] = 0
+            new_analysis[word.i, 2] = 2
+        else:
+            new_analysis[word.i, 0] = word.pos
+            new_analysis[word.i, 1] = NLU.vocab.strings[tag]
+            new_analysis[word.i, 2] = 3
+
+    doc = NLU.tokenizer.tokens_from_list(new_words)
+    doc.from_array(attrs, new_analysis)
+    NLU.parser(doc)
+    return doc
+
+
+class Parse(Model):
+    def __init__(self, doc, states, actions, **kwargs):
+        word_edits = kwargs.get('words', {})
+        tag_edits = kwargs.get('tags', {})
+        if word_edits or tag_edits:
+            doc = apply_edits(doc, word_edits, tag_edits)
+        notes = kwargs.get('notes', {})
+        self.actions = actions
+        self.words = [Word(w, w.i in word_edits, w.i in tag_edits) for w in doc]
+        self.states = states
+        self.notes = notes
+        for word in doc:
+            print(word.orth_, word.head.orth_)
+
+    @classmethod
+    def from_text(cls, text, **kwargs):
+        tokens = NLU(text)
+        #merge_entities(tokens)
+        merge_nps(tokens)
+        #merge_punct(tokens)
+        return cls(tokens, [State.from_doc(tokens)], [], **kwargs)
+
+    @classmethod
+    def from_history(cls, text, history, **kwargs):
+        if not isinstance(text, unicode):
+            text = text.decode('utf8')
+        text = text.replace('-SLASH-', '/')
+        history, history_length = _parse_history(history)
+
+        tokens = NLU.tokenizer(text)
+        NLU.tagger(tokens)
+        NLU.matcher(tokens)
+
+        with NLU.parser.step_through(tokens) as state:
+            for action in history:
+                state.transition(action)
+
+        NLU.entity(tokens)
+        actions = get_actions(state.stcls, len(history))
+        return Parse(tokens, [State(state.heads, state.deps, state.stack, state.queue)],
+                     actions, **kwargs)
+
+    @classmethod
+    def with_history(cls, text):
+        tokens = NLU.tokenizer(text)
+        NLU.tagger(tokens)
+        NLU.matcher(tokens)
+
+        with NLU.parser.step_through(tokens) as state:
+            states = []
+            while not state.is_final:
+                action = state.predict()
+                state.transition(action)
+                states.append(State(state.heads, state.deps, state.stack, state.queue))
+        actions = [
+            {'label': 'prev', 'key': 'P', 'binding': 37, 'is_valid': True},
+            {'label': 'next', 'key': 'N', 'binding': 39, 'is_valid': True}
+        ]
+        return Parse(state.doc, states, actions)
+
+
+class Word(Model):
+    def __init__(self, token, is_w_edit=False, is_t_edit=False):
+        self.word = token.orth_
+        self.tag = token.pos_
+        self.tag = token.pos_ if not token.ent_type_ else token.ent_type_
+        self.is_entity = token.ent_iob in (1, 3)
+        self.is_w_edit = is_w_edit
+        self.is_t_edit = is_t_edit
+        self.prob = token.prob
+
+
+class State(Model):
+    def __init__(self, heads, deps, stack, queue):
+        Model.__init__(self)
+
+        queue = [w for w in queue if w >= 0]
+        self.focus = min(queue) if queue else -1
+        self.is_final = bool(not stack and not queue)
+        self.stack = set(stack)
+        self.arrows = self._get_arrows(heads, deps)
+
+    @classmethod
+    def from_doc(cls, doc):
+        return cls([w.head.i for w in doc], [w.dep_ for w in doc], [], [])
+
+    def _get_arrows(self, heads, deps):
+        arcs = defaultdict(dict)
+        for i, (head, dep) in enumerate(zip(heads, deps)):
+            if i < head:
+                arcs[head - i][i] = Arrow(i, head, dep)
+            elif i > head:
+                arcs[i - head][head] = Arrow(i, head, dep)
+        output = []
+        for level in range(1, len(heads)):
+            level_arcs = []
+            for i in range(len(heads) - level):
+                level_arcs.append(arcs[level].get(i))
+            output.append(level_arcs)
+        while output and all(arc is None for arc in output[-1]):
+            output.pop()
+        return output
+
+
+class Arrow(Model):
+    def __init__(self, word, head, label):
+        self.dir = 'left' if head > word else 'right'
+        self.label = label
+
+
+class Endpoint(object):
+    def set_header(self, resp):
+        resp.content_type = 'text/string'
+        resp.append_header('Access-Control-Allow-Origin', "*")
+        resp.status = falcon.HTTP_200
+
+    def set_body(self, resp, parse):
+        resp.body = json.dumps(parse.to_json(), indent=4)
+
+    def on_get(self, req, resp, text):
+        if not isinstance(text, unicode):
+            text = text.decode('utf8')
+        self.set_body(resp, self.get_parse(text))
+        self.set_header(resp)
+
+    def on_post(self, req, resp):
+        try:
+            body_bytes = req.stream.read()
+            json_data = json.loads(body_bytes.decode('utf8'))
+            text = json_data['text']
+            if not isinstance(text, unicode):
+                text = text.decode('utf8')
+            self.set_body(resp, self.get_parse(text))
+            self.set_header(resp)
+        except:
+            pass
+
+
+class ParseEP(Endpoint):
+    def get_parse(self, text, **kwargs):
+        return Parse.from_text(text, **kwargs)
+
+
+class StepsEP(Endpoint):
+    def get_parse(self, text):
+        print('Step=', repr(text))
+        return Parse.with_history(text)
+
+
+class ManualEP(Endpoint):
+    def get_parse(self, text, **kwargs):
+        print('Manual=', repr(text))
+        if '/' in text:
+            text, actions = text.rsplit('/', 1)
+        else:
+            actions = ''
+        return Parse.from_history(text, actions, **kwargs)
+
+    def on_get(self, req, resp, text, actions=''):
+        if not isinstance(text, unicode):
+            text = text.decode('utf8')
+        self.set_body(resp, self.get_parse(text + '/' + actions))
+        self.set_header(resp)
+
+    def on_post(self, req, resp):
+        self.set_header(resp)
+        body_bytes = req.stream.read()
+        json_data = json.loads(body_bytes.decode('utf8'))
+        print(json_data)
+        params = json_data.get('params', {})
+        self.set_body(resp, self.get_parse(json_data['text'], **params))
+
+
+app = falcon.API()
+
+remote_man = ManualEP()
+remote_parse = ParseEP()
+remote_steps = StepsEP()
+
+app.add_route('/api/displacy/parse/', remote_parse)
+app.add_route('/api/displacy/parse/{text}/', remote_parse)
+
+app.add_route('/api/displacy/steps/', remote_steps)
+app.add_route('/api/displacy/steps/{text}/', remote_steps)
+
+app.add_route('/api/displacy/manual/', remote_man)
+app.add_route('/api/displacy/manual/{text}/', remote_man)
+app.add_route('/api/displacy/manual/{text}/{actions}', remote_man)
+
+
+if __name__ == '__main__':
+    text, actions = open(sys.argv[1]).read().strip().split('\n')
+    parse = Parse.from_text(text)
+    pprint.pprint(parse.to_json())