app.py

import difflib
import os
import json
from tqdm import tqdm
from glob import glob
#
# if not os.path.exists('./evttgr2type.json'):
#     for file_name in glob('data/RAMS_1.0/data/test.jsonlines'):
#         dic = {}
#         with open(file_name,'r',encoding='utf-8') as f:
#             lines = f.readlines()
#             for line in tqdm(lines):
#                 linej = json.loads(line.strip())
#                 evt_triggers = linej['evt_triggers']
#                 # print(evt_triggers)
#                 sentences = linej['sentences']
#                 # print(sentences)
#                 sentences_uni = []
#                 for s in sentences:
#                     sentences_uni += s
#                 print(' '.join(sentences_uni))
#                 triggers = ' '.join(sentences_uni[evt_triggers[0][0]:evt_triggers[0][1]+1])
#                 evt_type = evt_triggers[0][2][0][0]
#                 if triggers in dic:
#                     if dic[triggers] != evt_type:
#                         print('一个触发词有不同的事件类型: {} {} {}'.format(triggers,evt_type,dic[triggers]))
#                 dic[triggers] = evt_type
#                 print(evt_type, triggers)
#                 exit()

import argparse
import jsonlines
import torch

from src.genie.data import my_collate
from src.genie.data_module_w import RAMSDataModule
from src.genie.model import GenIEModel

import gradio as gr

import re
from transformers import BartTokenizer

MAX_LENGTH = 424
MAX_TGT_LENGTH = 72
DOC_STRIDE = 256

class DataModule4():
    def __init__(self, ontology_file):
        super().__init__()
        self.ontology_file = ontology_file
        self.tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
        self.tokenizer.add_tokens([' <arg>', ' <tgr>'])
        self.ontology_dict = self.load_ontology()

    def create_gold_gen(self, context_words, evt_type, trigger):
        # 设置三个总列表、存放输入模板、输出模板
        INPUT = []
        CONTEXT = []
        input_template = self.ontology_dict[evt_type.replace('n/a', 'unspecified')]['template']
        i = len(input_template)
        input_list = []
        for x in range(i):
            str = re.sub(r'<arg\d>', '<arg>', input_template[x])
            input_list.append(str)
        # 其中input_list种存放的是 原始数据中<arg1> 全部替换为 <arg> 之后的模板 下一步应该进行分词
        temp = []
        for x in range(i):
            space_tokenized_template = input_list[x].split(' ')
            temp.append(space_tokenized_template)
        # 其中temp中存放的都是分词后的模板 下一步对temp中的所有元素进行tokenize
        tokenized_input_template = []
        for x in range(len(temp)):
            for w in temp[x]:
                tokenized_input_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True))
            INPUT.append(tokenized_input_template)
            tokenized_input_template = []
        context_words = context_words.split(' ')
        trigger_words = trigger.split(' ')
        trigger_span_start = context_words.index(trigger_words[0])
        trigger_span_end = context_words.index(trigger_words[-1])
        # 触发词之前的单词
        prefix = self.tokenizer.tokenize(' '.join(context_words[:trigger_span_start]), add_prefix_space=True)
        # 触发词短语
        tgt = self.tokenizer.tokenize(trigger, add_prefix_space=True)
        # 触发词之后的单词
        suffix = self.tokenizer.tokenize(' '.join(context_words[trigger_span_end+1:]), add_prefix_space=True)
        context = prefix + [' <tgr>', ] + tgt + [' <tgr>', ] + suffix
        # context = self.tokenizer.tokenize(' '.join(context_words), add_prefix_space=True)

        # 将context放入CONTEXT中
        for w in range(i):
            CONTEXT.append(context)
        return INPUT, CONTEXT

    def load_ontology(self):
        ontology_dict = {}
        with open(self.ontology_file, 'r') as f:
            for lidx, line in enumerate(f):
                if lidx == 0:  # header
                    continue
                fields = line.strip().split(',')
                if len(fields) < 2:
                    break
                evt_type = fields[0]
                if evt_type in ontology_dict.keys():
                    args = fields[2:]
                    ontology_dict[evt_type]['template'].append(fields[1])
                    for i, arg in enumerate(args):
                        if arg != '':
                            ontology_dict[evt_type]['arg{}'.format(i + 1)] = arg
                            ontology_dict[evt_type][arg] = 'arg{}'.format(i + 1)
                else:
                    ontology_dict[evt_type] = {}
                    args = fields[2:]
                    ontology_dict[evt_type]['template'] = []
                    ontology_dict[evt_type]['template'].append(fields[1])
                    for i, arg in enumerate(args):
                        if arg != '':
                            ontology_dict[evt_type]['arg{}'.format(i + 1)] = arg
                            ontology_dict[evt_type][arg] = 'arg{}'.format(i + 1)

        return ontology_dict

    def prepare_data(self, sentences, evt_type, trigger):
        input_template, context = self.create_gold_gen(sentences, evt_type, trigger)
        length = len(input_template)
        # print(input_template)
        # print(context)
        results = []
        for i in range(length):
            input_tokens = self.tokenizer.encode_plus(input_template[i], context[i],
                                                      add_special_tokens=True,
                                                      add_prefix_space=True,
                                                      max_length=MAX_LENGTH,
                                                      truncation='only_second',
                                                      padding='max_length')
            # input_ids 单词在词典中的编码
            results.append(input_tokens['input_ids'])
        temp = self.ontology_dict[evt_type.replace('n/a', 'unspecified')]
        return results, temp

class DataModuleW():
    def __init__(self, ontology_file):
        super().__init__()
        self.ontology_file = ontology_file
        self.tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
        self.tokenizer.add_tokens([' <arg>', ' <tgr>'])
        self.ontology_dict = self.load_ontology()

    def create_gold_gen(self, context_words, evt_type, trigger):
        # 设置三个总列表、存放输入模板、输出模板
        INPUT = []
        CONTEXT = []
        input_template = self.ontology_dict[evt_type.replace('n/a', 'unspecified')]['template']
        i = len(input_template)
        input_list = []
        for x in range(i):
            str = re.sub('<trg>', trigger, input_template[x])
            str = re.sub('<trg>', trigger, str)
            input_list.append(str)
        # 其中input_list种存放的是 原始数据中<arg1> 全部替换为 <arg> 之后的模板 下一步应该进行分词
        temp = []
        for x in range(i):
            space_tokenized_template = input_list[x].split(' ')
            temp.append(space_tokenized_template)
        # 其中temp中存放的都是分词后的模板 下一步对temp中的所有元素进行tokenize
        tokenized_input_template = []
        for x in range(len(temp)):
            for w in temp[x]:
                tokenized_input_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True))
            INPUT.append(tokenized_input_template)
            tokenized_input_template = []
        template = self.ontology_dict[evt_type.replace('n/a', 'unspecified')]['template']
        for y in range(len(template)):
            template[y] = re.sub('<trg>', trigger, template[y])

        context = self.tokenizer.tokenize(context_words, add_prefix_space=True)
        # 将context放入CONTEXT中
        for w in range(i):
            CONTEXT.append(context)

        return INPUT, CONTEXT

    def load_ontology(self):
        ontology_dict = {}
        with open(self.ontology_file, 'r') as f:
            for lidx, line in tqdm(enumerate(f)):
                if lidx == 0:  # header
                    continue
                fields = line.strip().split(',')
                if len(fields) < 2:
                    break
                evt_type = fields[0]
                if evt_type in ontology_dict.keys():
                    args = fields[2:]
                    ontology_dict[evt_type]['template'].append(fields[1])
                    for i, arg in enumerate(args):
                        if arg != '':
                            ontology_dict[evt_type]['arg{}'.format(i + 1)] = arg
                            ontology_dict[evt_type][arg] = 'arg{}'.format(i + 1)
                else:
                    ontology_dict[evt_type] = {}
                    args = fields[2:]
                    ontology_dict[evt_type]['template'] = []
                    ontology_dict[evt_type]['template'].append(fields[1])
                    for i, arg in enumerate(args):
                        if arg != '':
                            ontology_dict[evt_type]['arg{}'.format(i + 1)] = arg
                            ontology_dict[evt_type][arg] = 'arg{}'.format(i + 1)

        return ontology_dict

    def prepare_data(self, sentences, evt_type, trigger):
        input_template, context =  self.create_gold_gen(sentences, evt_type, trigger)
        length = len(input_template)
        # print(input_template)
        # print(output_template)
        # print(context)
        results = []
        for i in range(length):
            input_tokens = self.tokenizer.encode_plus(input_template[i], context[i],
                                                      add_special_tokens=True,
                                                      add_prefix_space=True,
                                                      max_length=MAX_LENGTH,
                                                      truncation='only_second',
                                                      padding='max_length')
            # input_ids 单词在词典中的编码
            results.append(input_tokens['input_ids'])
        temp = self.ontology_dict[evt_type.replace('n/a', 'unspecified')]
        return results, temp

class Runner():
    def __init__(self, load_ckpt = 'checkpoints/gen-RAMS-what-new-span/epoch=2-v0.ckpt'):
        model = 'gen'
        self.ckpt_name = 'gen-RAMS-pred'
        self.load_ckpt = load_ckpt
        self.dataset = 'RAMS'
        self.eval_only = True
        self.train_file = 'data/RAMS_1.0/data/train.jsonlines'
        self.val_file = 'data/RAMS_1.0/data/dev.jsonlines'
        self.test_file = 'data/RAMS_1.0/data/test.jsonlines'
        self.train_batch_size = 2
        self.eval_batch_size = 4
        self.learning_rate = 3e-5
        self.accumulate_grad_batches = 4
        self.num_train_epochs = 3

        parser = argparse.ArgumentParser()

        # Required parameters
        parser.add_argument(
            "--model",
            type=str,
            default=model
        )
        parser.add_argument(
            "--dataset",
            type=str,
            default=self.dataset
        )
        parser.add_argument('--tmp_dir', type=str)
        parser.add_argument(
            "--ckpt_name",
            default=self.ckpt_name,
            type=str,
            help="The output directory where the model checkpoints and predictions will be written.",
        )
        parser.add_argument(
            "--load_ckpt",
            default=self.load_ckpt,
            type=str,
        )
        parser.add_argument(
            "--train_file",
            default=self.train_file,
            type=str,
            help="The input training file. If a data dir is specified, will look for the file there"
                 + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
        )
        parser.add_argument(
            "--val_file",
            default=self.val_file,
            type=str,
            help="The input evaluation file. If a data dir is specified, will look for the file there"
                 + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
        )
        parser.add_argument(
            '--test_file',
            type=str,
            default=self.test_file,
        )
        parser.add_argument('--input_dir', type=str, default=None)
        parser.add_argument('--coref_dir', type=str, default='data/kairos/coref_outputs')
        parser.add_argument('--use_info', action='store_true', default=False,
                            help='use informative mentions instead of the nearest mention.')
        parser.add_argument('--mark_trigger', action='store_true')
        parser.add_argument('--sample-gen', action='store_true', help='Do sampling when generation.')
        parser.add_argument("--train_batch_size", default=self.train_batch_size, type=int,
                            help="Batch size per GPU/CPU for training.")
        parser.add_argument(
            "--eval_batch_size", default=self.eval_batch_size, type=int, help="Batch size per GPU/CPU for evaluation."
        )
        parser.add_argument("--learning_rate", default=self.learning_rate, type=float,
                            help="The initial learning rate for Adam.")
        parser.add_argument(
            "--accumulate_grad_batches",
            type=int,
            default=self.accumulate_grad_batches,
            help="Number of updates steps to accumulate before performing a backward/update pass.",
        )
        parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
        parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
        parser.add_argument("--gradient_clip_val", default=1.0, type=float, help="Max gradient norm.")
        parser.add_argument(
            "--num_train_epochs", default=self.num_train_epochs, type=int,
            help="Total number of training epochs to perform."
        )
        parser.add_argument(
            "--max_steps",
            default=-1,
            type=int,
            help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
        )
        parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")

        parser.add_argument("--gpus", default=None, help='-1 means train on all gpus')
        parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
        parser.add_argument(
            "--fp16",
            action="store_true",
            help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
        )
        parser.add_argument("--threads", type=int, default=1,
                            help="multiple threads for converting example to features")
        self.args = parser.parse_args()

        self.model = GenIEModel(self.args)
        self.model.load_state_dict(torch.load(self.args.load_ckpt, map_location=self.model.device)['state_dict'])

    def pred(self,input):
        x = torch.stack([torch.LongTensor(u) for u in input])
        return self.model.pred(x)

print('Loading data...')
dm1 = DataModule4('aida_ontology_cleaned.csv')
dm2 = DataModuleW('aida_ontology_fj-w-2.csv')
dm3 = DataModuleW('aida_ontology_fj-w-3.csv')
dm4 = DataModule4('aida_ontology_fj-5.csv')

print('Loading Model 1...')
runner1 = Runner('checkpoints/gen-RAMS-1-span/epoch=2-v1.ckpt')
print('Loading Model 2...')
runner2 = Runner('checkpoints/gen-RAMS-2-span/epoch=2-v0.ckpt')
print('Loading Model 3...')
runner3 = Runner('checkpoints/gen-RAMS-3-span/epoch=2-v0.ckpt')
print('Loading Model 4...')
runner4 = Runner('checkpoints/gen-RAMS-4-span/epoch=2-v0.ckpt')

def handle(sentences,trigger, temp=3, evt_type='contact.prevarication.broadcast'):
    x, argnames = eval('dm{}.prepare_data(sentences,evt_type,trigger)'.format(temp+1))
    ys = eval('runner{}.pred(x)'.format(temp+1))
    print(ys)
    results = []
    for y in ys:
        while '  ' in y:
            y = y.replace('  ', ' ')
        result = y.strip(' ').split(' ')
        results.append(result)
    print(results)
    argss = []
    temp = 'trigger: ' + trigger
    argss.append(temp)
    # print(argnames)
    for n,template in enumerate(argnames['template']):
        template = template.split(' ')
        # print(template)
        args = []
        for i, w in enumerate(template):
            if '<arg' in w:
                m = re.match(r'evt\d+arg\d+(\w+)', argnames[re.match(r'<(\w+)>', w).group(1)])
                if m:
                    label = m.group(1)
                    if results[n][i] == '<arg>':
                        args.append(label+': None')
                    else:
                        args.append(label+': '+results[n][i])
        argss.append(', '.join(args))
    #print(argss)
    if len(argnames['template']) == 1:
        temp = argss[1].split(',')
        rs = []
        rs.append(argss[0])
        for i in temp:
            rs.append(i)
            argss = rs
    return '\n'.join(argss)

if __name__ == "__main__":
    # trigger = 'deceive'
    # sentences = """We are ashamed of them . " However , Mutko stopped short of admitting the doping scandal was state sponsored . " We are very sorry that athletes who tried to deceive us , and the world , were not caught sooner . We are very sorry because Russia is committed to upholding the highest standards in sport and is opposed to anything that threatens the Olympic values , " he said . English former heptathlete and Athens 2004 bronze medallist Kelly Sotherton was unhappy with Mutko 's plea for Russia 's ban to be lifted for Rio"""
    # print(handle(sentences, trigger))

    dm_key = list(dm1.ontology_dict.keys())
    print(len(dm_key))
    def get_tmp(index,evt_type):
        if index is None or evt_type is None:
            return ''
        input_template = eval("dm{}.ontology_dict[evt_type.replace('n/a', 'unspecified')]['template']".format(index+1))
        return '\n'.join(input_template)

    with gr.Blocks() as demo:
        with gr.Row().style(equal_height=False):
            with gr.Column(variant="panel"):
                stens = gr.Text(label='文档')
                evt_type  = gr.Dropdown(choices=dm_key, label='事件类型')
                trigger = gr.Text(label='触发词')
                temp = gr.Dropdown(choices=['基础模板', '简单子模板', '融入语义信息的子模板', '融入论元信息的子模板'],
                        type='index', value='基础模板', label='模板')
                
                output_tmp = gr.Text(label='模板内容')
                btn = gr.Button("Run")
                input_examples = gr.Examples(examples=[["We are ashamed of them.\" However , Mutko stopped short of admitting the doping scandal was state sponsored . \"We are very sorry that athletes who tried to deceive us, and the world, were not caught sooner.We are very sorry because Russia is committed to upholding the highest standards in sport and is opposed to anything that threatens the Olympic values, \" he said . English former heptathlete and Athens 2004 bronze medallist Kelly Sotherton was unhappy with Mutko 's plea for Russia 's ban to be lifted for Rio","deceive", "基础模板","contact.prevarication.broadcast"],["We are ashamed of them.\" However , Mutko stopped short of admitting the doping scandal was state sponsored . \"We are very sorry that athletes who tried to deceive us, and the world, were not caught sooner.We are very sorry because Russia is committed to upholding the highest standards in sport and is opposed to anything that threatens the Olympic values, \" he said . English former heptathlete and Athens 2004 bronze medallist Kelly Sotherton was unhappy with Mutko 's plea for Russia 's ban to be lifted for Rio","deceive", "简单子模板", "contact.prevarication.broadcast"],["We are ashamed of them.\" However , Mutko stopped short of admitting the doping scandal was state sponsored . \"We are very sorry that athletes who tried to deceive us, and the world, were not caught sooner.We are very sorry because Russia is committed to upholding the highest standards in sport and is opposed to anything that threatens the Olympic values, \" he said . English former heptathlete and Athens 2004 bronze medallist Kelly Sotherton was unhappy with Mutko 's plea for Russia 's ban to be lifted for Rio","deceive", "融入语义信息的子模板", "contact.prevarication.broadcast"],["We are ashamed of them.\" However , Mutko stopped short of admitting the doping scandal was state sponsored . \"We are very sorry that athletes who tried to deceive us, and the world, were not caught sooner.We are very sorry because Russia is committed to upholding the highest standards in sport and is opposed to anything that threatens the Olympic values, \" he said . English former heptathlete and Athens 2004 bronze medallist Kelly Sotherton was unhappy with Mutko 's plea for Russia 's ban to be lifted for Rio","deceive", "融入论元信息的子模板", "contact.prevarication.broadcast"]],inputs=[stens, trigger, temp, evt_type])

                #btn = gr.Button("Run")
            with gr.Column(variant="panel"):
                result  = gr.Text(label='输出论元生成结果')
            evt_type.change(get_tmp,inputs=[temp,evt_type],outputs=[output_tmp])
            temp.change(get_tmp,inputs=[temp,evt_type],outputs=[output_tmp])
            btn.click(fn=handle, inputs=[stens,trigger,temp,evt_type], outputs=[result])
    demo.launch(server_name='0.0.0.0',server_port=6006,share=True)