Can not access to AZURE Text Analytics API. "Time out" is raised

Question

I was using sentiment analysis function by Azure Text Analytics Client Library for Python-Version 5.1.0. The sample program ran successfully. And then I embedded it in my own code, and tried it. And I found it ran several times and got stuck. Sometimes it raise 'Time out', but in most cases it didn't return anything. I have tried east Asia and some other servers, and it doesn't work.

Answer 1

import os.path
from glob import glob
from tqdm import tqdm
import csv
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient


endpoint = ''  # Here remove the endpoint and key.
key = ''


class DocUnit(object):
    meta_header = list()
    meta_list = list()
    senti_dict = dict()

    def __init__(self, meta_path):
        self._nlp = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

        if not DocUnit.meta_list:
            with open(meta_path, 'r') as f:
                f_csv = csv.reader(f)
                DocUnit.meta_header = next(f_csv)
                for row in f_csv:
                    DocUnit.meta_list.append(row)

    def __call__(self, file_name):
        with open(file_name, 'r') as f:
            doc = f.read()

        senti_dict = {'positive': 1, 'negative': -1, 'neutral': 0}
        docs_split = [i for i in doc.split('\n') if i != '']
        scores = list()
        idx = 0
        while True:
            try:
                docs = self._nlp.analyze_sentiment(docs_split[idx * 10: (idx + 1) * 10])
                scores += [senti_dict[sentence.sentiment] for doc in docs for sentence in doc.sentences]
                idx += 1
            except Exception as es:
                print('Time out!!!')
                # raise es

            if idx >= (len(docs_split) - 1) // 10 + 1:
                break

        num_neu = scores.count(0)
        num_neg = scores.count(-1)
        num_pos = scores.count(1)
        mean_score = sum(scores) / len(scores)

        doc_name = os.path.splitext(os.path.split(file_name)[1])[0]

        DocUnit.senti_dict[doc_name] = [num_neu, num_pos, num_neg, mean_score]


if __name__ == '__main__':
    doc_unit = DocUnit('sentiment_paragraph.csv')
    file_names = glob('./texts/*.txt')
    for file_name in tqdm(file_names):
        doc_unit(file_name)