main.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
import os
import platform
import time

from allennlp.data.tokenizers.pretrained_transformer_tokenizer import PretrainedTransformerTokenizer
from allennlp.data.token_indexers.single_id_token_indexer import SingleIdTokenIndexer
from allennlp.data.vocabulary import Vocabulary
from allennlp.modules.text_field_embedders.basic_text_field_embedder import BasicTextFieldEmbedder
from allennlp.modules.token_embedders.embedding import Embedding
from allennlp.modules.seq2vec_encoders.cnn_encoder import CnnEncoder
from allennlp.models.archival import archive_model, load_archive
from allennlp_models.rc.modules.seq2seq_encoders.stacked_self_attention import StackedSelfAttentionEncoder
from allennlp.predictors.predictor import Predictor
from allennlp.predictors.text_classifier import TextClassifierPredictor
import gradio as gr
import torch

from project_settings import project_path
from toolbox.allennlp_models.text_classifier.models.hierarchical_text_classifier import HierarchicalClassifier
from toolbox.allennlp_models.text_classifier.dataset_readers.hierarchical_classification_json import HierarchicalClassificationJsonReader


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--cn_archive_file",
        default=(project_path / "trained_models/telemarketing_intent_classification_cn").as_posix(),
        type=str
    )
    parser.add_argument(
        "--en_archive_file",
        default=(project_path / "trained_models/telemarketing_intent_classification_en").as_posix(),
        type=str
    )
    parser.add_argument(
        "--jp_archive_file",
        default=(project_path / "trained_models/telemarketing_intent_classification_jp").as_posix(),
        type=str
    )
    parser.add_argument(
        "--vi_archive_file",
        default=(project_path / "trained_models/telemarketing_intent_classification_vi").as_posix(),
        type=str
    )
    parser.add_argument(
        "--predictor_name",
        default="text_classifier",
        type=str
    )
    args = parser.parse_args()
    return args


def main():
    args = get_args()

    cn_archive = load_archive(archive_file=args.cn_archive_file)
    cn_predictor = Predictor.from_archive(cn_archive, predictor_name=args.predictor_name)
    en_archive = load_archive(archive_file=args.en_archive_file)
    en_predictor = Predictor.from_archive(en_archive, predictor_name=args.predictor_name)
    jp_archive = load_archive(archive_file=args.jp_archive_file)
    jp_predictor = Predictor.from_archive(jp_archive, predictor_name=args.predictor_name)
    vi_archive = load_archive(archive_file=args.vi_archive_file)
    vi_predictor = Predictor.from_archive(vi_archive, predictor_name=args.predictor_name)

    predictor_map = {
        "chinese": cn_predictor,
        "english": en_predictor,
        "japanese": jp_predictor,
        "vietnamese": vi_predictor,
    }

    def fn(text: str, language: str):
        predictor = predictor_map.get(language, cn_predictor)

        json_dict = {'sentence': text}
        outputs = predictor.predict_json(
            json_dict
        )
        outputs = predictor._model.decode(outputs)
        label = outputs['label'][0]
        prob = outputs['prob'][0]
        prob = round(prob, 4)
        return label, prob

    description = """
    电销场景意图识别. 
    语言: 汉语, 英语, 日语, 越南语. 
    数据集是私有的. 
    
    model: selfattention-cnn
    dataset: telemarketing_intent (https://huggingface.co/datasets/qgyd2021/telemarketing_intent)
    
    accuracy:
    chinese: 0.8002
    english: 0.7011
    japanese: 0.8154
    vietnamese: 0.8168
    
    """
    demo = gr.Interface(
        fn=fn,
        inputs=[
            gr.Text(label="text"),
            gr.Dropdown(
                choices=list(sorted(predictor_map.keys())),
                label="language"
            )
        ],
        outputs=[gr.Text(label="intent"), gr.Number(label="prob")],
        examples=[
            ["你找谁", "chinese"],
            ["你是谁啊", "chinese"],
            ["不好意思我现在很忙", "chinese"],
            ["对不起, 不需要哈", "chinese"],
            ["u have got the wrong number", "english"],
            ["sure, thank a lot", "english"],
            ["please leave your message for 95688496", "english"],
            ["yes well", "english"],
            ["失礼の", "japanese"],
            ["ビートいう発表の後に、お名前とご用件をお話ください。", "japanese"],
            ["わかんない。", "japanese"],
            ["に出ることができません", "japanese"],
            ["À không phải em nha.", "vietnamese"],
            ["Dạ nhầm số rồi ạ?", "vietnamese"],
            ["Ừ, cảm ơn em nhá.", "vietnamese"],
            ["Không, chị không có tiền.", "vietnamese"],
        ],
        examples_per_page=50,
        title="Telemarketing Intent Classification",
        description=description,
    )
    demo.launch(share=True if platform.system() == "Windows" else False)

    return


if __name__ == '__main__':
    main()