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5d033479ce
* lower python version for google colab * faces working with colab * text for colab * fix dict update bug * final edits for colab * update readme with links * load text models on demand * update test * fix typo; more description in readme * remove optional keys
109 строки
3.4 KiB
Python
109 строки
3.4 KiB
Python
import os
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import pytest
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import spacy
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import misinformation.text as tt
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TESTDICT = {
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"IMG_3755": {
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"filename": "./test/data/IMG_3755.jpg",
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},
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"IMG_3756": {
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"filename": "./test/data/IMG_3756.jpg",
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},
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"IMG_3757": {
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"filename": "./test/data/IMG_3757.jpg",
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},
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}
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LANGUAGES = ["de", "om", "en"]
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os.environ[
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"GOOGLE_APPLICATION_CREDENTIALS"
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] = "../data/seismic-bonfire-329406-412821a70264.json"
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def test_TextDetector():
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for item in TESTDICT:
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test_obj = tt.TextDetector(TESTDICT[item])
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assert test_obj.subdict["text"] is None
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assert test_obj.subdict["text_language"] is None
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assert test_obj.subdict["text_english"] is None
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assert not test_obj.analyse_text
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assert not test_obj.analyse_topic
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@pytest.mark.gcv
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def test_analyse_image():
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for item in TESTDICT:
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test_obj = tt.TextDetector(TESTDICT[item])
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test_obj.analyse_image()
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test_obj = tt.TextDetector(TESTDICT[item], analyse_text=True)
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test_obj.analyse_image()
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test_obj = tt.TextDetector(TESTDICT[item], analyse_topic=True)
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@pytest.mark.gcv
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def test_get_text_from_image():
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for item in TESTDICT:
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test_obj = tt.TextDetector(TESTDICT[item])
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test_obj.get_text_from_image()
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ref_file = "./test/data/text_" + item + ".txt"
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with open(ref_file, "r", encoding="utf8") as file:
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reference_text = file.read()
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assert test_obj.subdict["text"] == reference_text
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def test_translate_text():
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for item, lang in zip(TESTDICT, LANGUAGES):
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test_obj = tt.TextDetector(TESTDICT[item])
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ref_file = "./test/data/text_" + item + ".txt"
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trans_file = "./test/data/text_translated_" + item + ".txt"
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with open(ref_file, "r", encoding="utf8") as file:
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reference_text = file.read()
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with open(trans_file, "r", encoding="utf8") as file:
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translated_text = file.read()
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test_obj.subdict["text"] = reference_text
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test_obj.translate_text()
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assert test_obj.subdict["text_language"] == lang
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assert test_obj.subdict["text_english"] == translated_text
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def test_run_spacy():
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test_obj = tt.TextDetector(TESTDICT["IMG_3755"], analyse_text=True)
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ref_file = "./test/data/text_IMG_3755.txt"
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with open(ref_file, "r") as file:
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reference_text = file.read()
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test_obj.subdict["text_english"] = reference_text
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test_obj._run_spacy()
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assert isinstance(test_obj.doc, spacy.tokens.doc.Doc)
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def test_clean_text():
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nlp = spacy.load("en_core_web_md")
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doc = nlp("I like cats and fjejg")
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test_obj = tt.TextDetector(TESTDICT["IMG_3755"])
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test_obj.doc = doc
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test_obj.clean_text()
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result = "I like cats and"
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assert test_obj.subdict["text_clean"] == result
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def test_correct_spelling():
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mydict = {}
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test_obj = tt.TextDetector(mydict, analyse_text=True)
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test_obj.subdict["text_english"] = "I lik cats ad dogs."
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test_obj.correct_spelling()
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result = "I like cats ad dogs."
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assert test_obj.subdict["text_english_correct"] == result
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def test_sentiment_analysis():
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mydict = {}
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test_obj = tt.TextDetector(mydict, analyse_text=True)
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test_obj.subdict["text_english"] = "I love cats and dogs."
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test_obj._run_spacy()
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test_obj.correct_spelling()
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test_obj.sentiment_analysis()
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assert test_obj.subdict["polarity"] == 0.5
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assert test_obj.subdict["subjectivity"] == 0.6
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