backend.py

import cherrypy
import cherrypy_cors
import json
import os
from util import *


# import < your_code >


def Get_CogComp_SRL_results(input_sentence):


    # We then work on Celine's SRL system.
    # print('Extracting the events.')
    SRL_tokens = list()
    SRL_sentences = list()
    SRL_response = requests.get('http://dickens.seas.upenn.edu:4039/annotate', data=input_sentence)
    if SRL_response.status_code != 200:
        return None, None
    SRL_result = json.loads(SRL_response.text)
    SRL_tokens = SRL_result['tokens']
    SRL_sentences = SRL_result['sentences']
    return SRL_tokens, SRL_sentences
    # print('Match tokens.')



class MyWebService(object):

    @cherrypy.expose
    def index(self):
        return open('html/index.html', encoding='utf-8')

    @cherrypy.expose
    @cherrypy.tools.json_out()
    @cherrypy.tools.json_in()
    def info(self, **params):
        return {"status":"online"}

    @cherrypy.expose
    def halt(self, **params):
        cherrypy.engine.exit()

    @cherrypy.expose
    @cherrypy.tools.json_out()
    @cherrypy.tools.json_in()
    def annotate(self):

        hasJSON = True
        result = {"status": "false"}
        try:
            # get input JSON
            data = cherrypy.request.json
        except:
            hasJSON = False
            result = {"error": "invalid input"}

        if hasJSON:
            # process input
            input_paragraph = data['text']
            headers = {'Content-type': 'application/json'}
            NER_response = requests.post('http://dickens.seas.upenn.edu:4022/ner/',
                                         json={"task": "ner", "text": input_paragraph}, headers=headers)
            if NER_response.status_code != 200:
                return {'error': 'The NER service is down.'}
            # SRL_response = requests.get('http://dickens.seas.upenn.edu:4039/annotate', data=input_paragraph)
            SRL_response = requests.post('http://dickens.seas.upenn.edu:4039/annotate',
                                         json={'sentence': input_paragraph})

            if SRL_response.status_code != 200:
                return {'error': 'The SRL service is down.'}
            SRL_tokens, SRL_sentences = Get_CogComp_SRL_results(input_paragraph)
            print(SRL_sentences['sentenceEndPositions'])
            sentences = list()
            sentences_by_char = list()
            for i, tmp_s_end_token in enumerate(SRL_sentences['sentenceEndPositions']):
                if i == 0:
                    sentences.append(' '.join(SRL_tokens[:tmp_s_end_token]))
                    sentences_by_char.append(SRL_tokens[:tmp_s_end_token])
                else:
                    sentences.append(' '.join(SRL_tokens[SRL_sentences['sentenceEndPositions'][i-1]:tmp_s_end_token]))
                    sentences_by_char.append(SRL_tokens[SRL_sentences['sentenceEndPositions'][i-1]:tmp_s_end_token])
            if SRL_sentences['sentenceEndPositions'][-1] < len(SRL_tokens):
                sentences.append(' '.join(SRL_tokens[SRL_sentences['sentenceEndPositions'][-1]:]))
                sentences_by_char.append(SRL_tokens[SRL_sentences['sentenceEndPositions'][-1]:])
            # sentences = input_paragraph.split('\n')
            print('Number of sentences:', len(sentences))
            previous_char = 0
            tmp_view_data = dict()
            tmp_view_data['viewType'] = 'edu.illinois.cs.cogcomp.core.datastructures.textannotation.PredicateArgumentView'
            tmp_view_data['viewName'] = 'event_extraction'
            tmp_view_data['generator'] = 'cogcomp_kairos_event_ie_v1.0'
            tmp_view_data['score'] = 1.0
            tmp_view_data['constituents'] = list()
            tmp_view_data['relations'] = list()
            all_tokens = list()
            sentence_positions = list()
            previous_char = 0
            for s_id, tmp_s in enumerate(sentences):
                extracted_events = extractor.extract(tmp_s)
                print(extracted_events)
                if len(extracted_events) > 0:
                    tmp_tokens = extracted_events[0]['tokens']
                else:
                    tmp_tokens = tmp_s.split(' ')
                all_tokens += tmp_tokens
                sentence_positions.append(len(all_tokens))
                for tmp_event in extracted_events:
                    trigger_start_token_id = tmp_event['trigger']['position'][0] + previous_char
                    trigger_end_token_id = tmp_event['trigger']['position'][1] + previous_char

                    trigger_consituent_position = len(tmp_view_data['constituents'])
                    tmp_view_data['constituents'].append(
                        {'label': tmp_event['trigger']['type'], 'score': 1.0, 'start': trigger_start_token_id,
                         'end': trigger_end_token_id, 'properties': {
                            'SenseNumber': '01', 'sentence_id': s_id,
                                                                     'predicate': tmp_tokens[
                                                                                  trigger_start_token_id:trigger_end_token_id]}})
                    for tmp_argument in tmp_event['arguments']:
                        argument_start_token_id = tmp_argument['position'][0] + previous_char
                        argument_end_token_id = tmp_argument['position'][1] + previous_char
                        tmp_view_data['relations'].append(
                            {'relationName': tmp_argument['role'], 'srcConstituent': trigger_consituent_position,
                             'targetConstituent': len(tmp_view_data['constituents'])})
                        tmp_view_data['constituents'].append(
                            {'label': tmp_argument['role'], 'score': 1.0, 'start': argument_start_token_id,
                             'end': argument_end_token_id, 'entity_type': tmp_argument['entity_type']})
                previous_char += len(sentences_by_char[s_id])

            event_ie_view = dict()
            event_ie_view['viewName'] = 'Event_extraction'
            event_ie_view['viewData'] = [tmp_view_data]

            token_view = dict()
            token_view['viewName'] = 'TOKENS'
            tmp_token_view_data = dict()
            tmp_token_view_data['viewType'] = 'edu.illinois.cs.cogcomp.core.datastructures.textannotation.TokenLabelView'
            tmp_token_view_data['viewName'] = 'TOKENS'
            tmp_token_view_data['generator'] = 'Cogcomp-SRL'
            tmp_token_view_data['score'] = 1.0
            tmp_token_view_data['constituents'] = list()
            for i, tmp_token in enumerate(all_tokens):
                tmp_token_view_data['constituents'].append({'label': tmp_token, 'score': 1.0, 'start': i, 'end': i+1})
            token_view['viewData'] = tmp_token_view_data

            result = dict()
            result['corpusId'] = ''
            result['id'] = ''
            result['text'] = data['text']
#             result['tokens'] = SRL_tokens
#             result['sentences'] = SRL_sentences
            result['views'] = [token_view, event_ie_view]

            # debug on view:
            result['tokens'] = all_tokens
            result['sentences'] = {'generator': 'srl_pipeline', 'score': 1.0, 'sentenceEndPositions': sentence_positions}
    # return resulting JSON
        return result


if __name__ == '__main__':
    print("")
    # INITIALIZE YOUR MODEL HERE
    parser = argparse.ArgumentParser()
    parser.add_argument("--gpu", default='1', type=str, required=False,
                        help="choose which gpu to use")
    parser.add_argument("--representation_source", default='nyt', type=str, required=False,
                        help="choose which gpu to use")
    parser.add_argument("--model", default='bert-large', type=str, required=False,
                        help="choose which gpu to use")
    parser.add_argument("--pooling_method", default='final', type=str, required=False,
                        help="choose which gpu to use")
    parser.add_argument("--weight", default=100, type=float, required=False,
                        help="weight assigned to triggers")
    parser.add_argument("--argument_matching", default='exact', type=str, required=False,
                        help="weight assigned to triggers")
    parser.add_argument("--eval_model", default='joint', type=str, required=False,
                        help="weight assigned to triggers")

    args = parser.parse_args()
    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
    os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print('current device:', device)

    extractor = CogcompKairosEventExtractorTest(device, 'mbert')
    # IN ORDER TO KEEP IT IN MEMORY
    print("Starting rest service...")
    cherrypy_cors.install()
    config = {
        'global': {
            'server.socket_host': 'leguin.seas.upenn.edu',
            'server.socket_port': 4023,
            'cors.expose.on': True
        },
        '/': {
            'tools.sessions.on': True,
            'cors.expose.on': True,
            'tools.staticdir.root': os.path.abspath(os.getcwd())

        },
        '/static': {
            'tools.staticdir.on': True,
            'tools.staticdir.dir': './html'
        },
        '/html': {
            'tools.staticdir.on': True,
            'tools.staticdir.dir': './html',
            'tools.staticdir.index': 'index.html',
            'tools.gzip.on': True
        }
    }
    cherrypy.config.update(config)
    # cherrypy.config.update({'server.socket_port': 4036})
    cherrypy.quickstart(MyWebService(), '/', config)

# import cherrypy
#
#
# class demoExample:
#     @cherrypy.expose
#     def index(self):
#
#         return "Hello World!!!"
#
#
#
# cherrypy.quickstart(demoExample())