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| 1 | +#!/usr/bin/env python |
| 2 | +# -*- coding: utf-8 -*- |
| 3 | + |
| 4 | +# import necessary Python Packages |
| 5 | +import re |
| 6 | +import pickle |
| 7 | +import torch |
| 8 | +import yaml |
| 9 | +from model import BiLSTMCRF |
| 10 | +from utils import * |
| 11 | +import warnings |
| 12 | +import numpy as np |
| 13 | +from flask import Flask, request |
| 14 | +from flasgger import Swagger |
| 15 | + |
| 16 | + |
| 17 | +warnings.filterwarnings("ignore") |
| 18 | +device = torch.device("cpu") |
| 19 | + |
| 20 | + |
| 21 | +app = Flask(__name__) |
| 22 | +swagger = Swagger(app) |
| 23 | + |
| 24 | + |
| 25 | +def load_params(path: str): |
| 26 | + """ |
| 27 | + Load the parameters (data) |
| 28 | + """ |
| 29 | + with open(path + "data.pkl", "rb") as fopen: |
| 30 | + data_map = pickle.load(fopen) |
| 31 | + return data_map |
| 32 | + |
| 33 | + |
| 34 | +def strQ2B(ustring): |
| 35 | + rstring = "" |
| 36 | + for uchar in ustring: |
| 37 | + inside_code=ord(uchar) |
| 38 | + if inside_code == 12288: |
| 39 | + inside_code = 32 |
| 40 | + elif inside_code >= 65281 and inside_code <= 65374: |
| 41 | + inside_code -= 65248 |
| 42 | + rstring += chr(inside_code) |
| 43 | + return rstring |
| 44 | + |
| 45 | + |
| 46 | +def cut_text(text, length): |
| 47 | + textArr = re.findall('.{' + str(length) + '}', text) |
| 48 | + textArr.append(text[(len(textArr) * length):]) |
| 49 | + return textArr |
| 50 | + |
| 51 | + |
| 52 | +def load_config(): |
| 53 | + """ |
| 54 | + Load hyper-parameters from the YAML file |
| 55 | + """ |
| 56 | + fopen = open("config.yml") |
| 57 | + config = yaml.load(fopen, Loader=yaml.FullLoader) |
| 58 | + fopen.close() |
| 59 | + return config |
| 60 | + |
| 61 | + |
| 62 | +class ChineseNER: |
| 63 | + def __init__(self, entry="train"): |
| 64 | + # Load some Hyper-parameters |
| 65 | + config = load_config() |
| 66 | + self.embedding_size = config.get("embedding_size") |
| 67 | + self.hidden_size = config.get("hidden_size") |
| 68 | + self.batch_size = config.get("batch_size") |
| 69 | + self.model_path = config.get("model_path") |
| 70 | + self.dropout = config.get("dropout") |
| 71 | + self.tags = config.get("tags") |
| 72 | + self.learning_rate = config.get("learning_rate") |
| 73 | + self.epochs = config.get("epochs") |
| 74 | + self.weight_decay = config.get("weight_decay") |
| 75 | + self.transfer_learning = config.get("transfer_learning") |
| 76 | + self.lr_decay_step = config.get("lr_decay_step") |
| 77 | + self.lr_decay_rate = config.get("lr_decay_rate") |
| 78 | + self.max_length = config.get("max_length") |
| 79 | + |
| 80 | + # Model Initialization |
| 81 | + self.main_model(entry) |
| 82 | + |
| 83 | + def main_model(self, entry): |
| 84 | + """ |
| 85 | + Model Initialization |
| 86 | + """ |
| 87 | + # The Testing & Inference Process |
| 88 | + if entry == "predict": |
| 89 | + data_map = load_params(path=self.model_path) |
| 90 | + input_size = data_map.get("input_size") |
| 91 | + self.tag_map = data_map.get("tag_map") |
| 92 | + self.vocab = data_map.get("vocab") |
| 93 | + self.model = BiLSTMCRF( |
| 94 | + tag_map=self.tag_map, |
| 95 | + vocab_size=input_size, |
| 96 | + dropout=0.0, |
| 97 | + embedding_dim=self.embedding_size, |
| 98 | + hidden_dim=self.hidden_size, |
| 99 | + max_length=self.max_length |
| 100 | + ) |
| 101 | + self.restore_model() |
| 102 | + |
| 103 | + def restore_model(self): |
| 104 | + """ |
| 105 | + Restore the model if there is one |
| 106 | + """ |
| 107 | + try: |
| 108 | + self.model.load_state_dict(torch.load(self.model_path + "params.pkl")) |
| 109 | + print("Model Successfully Restored!") |
| 110 | + except Exception as error: |
| 111 | + print("Model Failed to restore! {}".format(error)) |
| 112 | + |
| 113 | + def predict(self, input_str): |
| 114 | + """ |
| 115 | + Prediction & Inference Stage |
| 116 | + :param input_str: Input Chinese sentence |
| 117 | + :return entities: Predicted entities |
| 118 | + """ |
| 119 | + if len(input_str) != 0: |
| 120 | + # Full-width to half-width |
| 121 | + input_str = strQ2B(input_str) |
| 122 | + input_str = re.sub(pattern='。', repl='.', string=input_str) |
| 123 | + text = cut_text(text=input_str, length=self.max_length) |
| 124 | + |
| 125 | + cut_out = [] |
| 126 | + for cuttext in text: |
| 127 | + # Get the embedding vector (Input Vector) from vocab |
| 128 | + input_vec = [self.vocab.get(i, 0) for i in cuttext] |
| 129 | + |
| 130 | + # convert it to tensor and run the model |
| 131 | + sentences = torch.tensor(input_vec).view(1, -1) |
| 132 | + |
| 133 | + length = np.expand_dims(np.shape(sentences)[1], axis=0) |
| 134 | + length = torch.tensor(length, dtype=torch.int64, device=device) |
| 135 | + |
| 136 | + _, paths = self.model(sentences=sentences, real_length=length, lengths=None) |
| 137 | + |
| 138 | + # Get the entities from the model |
| 139 | + entities = [] |
| 140 | + for tag in self.tags: |
| 141 | + tags = get_tags(paths[0], tag, self.tag_map) |
| 142 | + entities += format_result(tags, cuttext, tag) |
| 143 | + |
| 144 | + # Get all the entities |
| 145 | + all_start = [] |
| 146 | + for entity in entities: |
| 147 | + start = entity.get('start') |
| 148 | + all_start.append([start, entity]) |
| 149 | + |
| 150 | + # Sort the results by the "start" index |
| 151 | + sort_d = [value for index, value in sorted(enumerate(all_start), key=lambda all_start: all_start[1])] |
| 152 | + |
| 153 | + if len(sort_d) == 0: |
| 154 | + return print("There was no entity in this sentence!!") |
| 155 | + else: |
| 156 | + sort_d = np.reshape(np.array(sort_d)[:, 1], [np.shape(sort_d)[0], 1]) |
| 157 | + cut_out.append(sort_d) |
| 158 | + return cut_out |
| 159 | + else: |
| 160 | + return print('Invalid input! Please re-input!!\n') |
| 161 | + |
| 162 | + |
| 163 | +@app.route('/predict', methods=["GET"]) |
| 164 | +def predict_iris_file(): |
| 165 | + """Named Entity Recognition (NER) Prediction for Medical Services |
| 166 | + --- |
| 167 | + parameters: |
| 168 | + - name: input_str |
| 169 | + in: query |
| 170 | + type: string |
| 171 | + required: true |
| 172 | + """ |
| 173 | + input_str = request.args.get("input_str") |
| 174 | + cn = ChineseNER("predict") |
| 175 | + prediction = cn.predict(input_str) |
| 176 | + return str(prediction) |
| 177 | + |
| 178 | + |
| 179 | +# main function |
| 180 | +if __name__ == "__main__": |
| 181 | + app.run(host='0.0.0.0') |
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