import os import pytest import spacy import ammico.text as tt @pytest.fixture def set_testdict(get_path): testdict = { "IMG_3755": { "filename": get_path + "IMG_3755.jpg", }, "IMG_3756": { "filename": get_path + "IMG_3756.jpg", }, "IMG_3757": { "filename": get_path + "IMG_3757.jpg", }, } return testdict LANGUAGES = ["de", "om", "en"] def test_TextDetector(set_testdict): for item in set_testdict: test_obj = tt.TextDetector(set_testdict[item]) assert test_obj.subdict["text"] is None assert test_obj.subdict["text_language"] is None assert test_obj.subdict["text_english"] is None assert not test_obj.analyse_text @pytest.mark.gcv def test_analyse_image(set_testdict, set_environ): for item in set_testdict: test_obj = tt.TextDetector(set_testdict[item]) test_obj.analyse_image() test_obj = tt.TextDetector(set_testdict[item], analyse_text=True) test_obj.analyse_image() @pytest.mark.gcv def test_get_text_from_image(set_testdict, get_path, set_environ): for item in set_testdict: test_obj = tt.TextDetector(set_testdict[item]) test_obj.get_text_from_image() ref_file = get_path + "text_" + item + ".txt" with open(ref_file, "r", encoding="utf8") as file: reference_text = file.read() assert test_obj.subdict["text"] == reference_text def test_translate_text(set_testdict, get_path): for item, lang in zip(set_testdict, LANGUAGES): test_obj = tt.TextDetector(set_testdict[item]) ref_file = get_path + "text_" + item + ".txt" trans_file = get_path + "text_translated_" + item + ".txt" with open(ref_file, "r", encoding="utf8") as file: reference_text = file.read() with open(trans_file, "r", encoding="utf8") as file: translated_text = file.read() test_obj.subdict["text"] = reference_text test_obj.translate_text() assert test_obj.subdict["text_language"] == lang assert test_obj.subdict["text_english"] == translated_text def test_remove_linebreaks(): test_obj = tt.TextDetector({}) test_obj.subdict["text"] = "This is \n a test." test_obj.subdict["text_english"] = "This is \n another\n test." test_obj.remove_linebreaks() assert test_obj.subdict["text"] == "This is a test." assert test_obj.subdict["text_english"] == "This is another test." def test_run_spacy(set_testdict, get_path): test_obj = tt.TextDetector(set_testdict["IMG_3755"], analyse_text=True) ref_file = get_path + "text_IMG_3755.txt" with open(ref_file, "r") as file: reference_text = file.read() test_obj.subdict["text_english"] = reference_text test_obj._run_spacy() assert isinstance(test_obj.doc, spacy.tokens.doc.Doc) def test_clean_text(set_testdict): nlp = spacy.load("en_core_web_md") doc = nlp("I like cats and fjejg") test_obj = tt.TextDetector(set_testdict["IMG_3755"]) test_obj.doc = doc test_obj.clean_text() result = "I like cats and" assert test_obj.subdict["text_clean"] == result def test_correct_spelling(): mydict = {} test_obj = tt.TextDetector(mydict, analyse_text=True) test_obj.subdict["text_english"] = "I lik cats ad dogs." test_obj.correct_spelling() result = "I like cats ad dogs." assert test_obj.subdict["text_english_correct"] == result def test_sentiment_analysis(): mydict = {} test_obj = tt.TextDetector(mydict, analyse_text=True) test_obj.subdict["text_english"] = "I love cats and dogs." test_obj._run_spacy() test_obj.correct_spelling() test_obj.sentiment_analysis() assert test_obj.subdict["polarity"] == 0.5 assert test_obj.subdict["subjectivity"] == 0.6 @pytest.mark.win_skip def test_text_summary(get_path): mydict = {} test_obj = tt.TextDetector(mydict, analyse_text=True) ref_file = get_path + "example_summary.txt" with open(ref_file, "r", encoding="utf8") as file: reference_text = file.read() mydict["text_english"] = reference_text test_obj.text_summary() reference_summary = " I’m sorry, but I don’t want to be an emperor" assert mydict["text_summary"] == reference_summary def test_text_sentiment_transformers(): mydict = {} test_obj = tt.TextDetector(mydict, analyse_text=True) mydict["text_english"] = "I am happy that the CI is working again." test_obj.text_sentiment_transformers() assert mydict["sentiment"] == "POSITIVE" assert mydict["sentiment_score"] == pytest.approx(0.99, 0.01) def test_text_ner(): mydict = {} test_obj = tt.TextDetector(mydict, analyse_text=True) mydict["text_english"] = "Bill Gates was born in Seattle." test_obj.text_ner() assert mydict["entity"] == ["Bill Gates", "Seattle"] assert mydict["entity_type"] == ["PER", "LOC"] def test_PostprocessText(set_testdict, get_path): reference_dict = "THE\nALGEBRAIC\nEIGENVALUE\nPROBLEM\nDOM\nNVS TIO\nMINA\nMonographs\non Numerical Analysis\nJ.. H. WILKINSON" reference_df = "Mathematische Formelsammlung\nfür Ingenieure und Naturwissenschaftler\nMit zahlreichen Abbildungen und Rechenbeispielen\nund einer ausführlichen Integraltafel\n3., verbesserte Auflage" img_numbers = ["IMG_3755", "IMG_3756", "IMG_3757"] for image_ref in img_numbers: ref_file = get_path + "text_" + image_ref + ".txt" with open(ref_file, "r") as file: reference_text = file.read() set_testdict[image_ref]["text_english"] = reference_text obj = tt.PostprocessText(mydict=set_testdict) test_dict = obj.list_text_english[2].replace("\r", "") assert test_dict == reference_dict for key in set_testdict.keys(): set_testdict[key].pop("text_english") with pytest.raises(ValueError): tt.PostprocessText(mydict=set_testdict) obj = tt.PostprocessText(use_csv=True, csv_path=get_path + "test_data_out.csv") # make sure test works on windows where end-of-line character is \r\n test_df = obj.list_text_english[0].replace("\r", "") assert test_df == reference_df with pytest.raises(ValueError): tt.PostprocessText(use_csv=True, csv_path=get_path + "test_data_out_nokey.csv") with pytest.raises(ValueError): tt.PostprocessText() def test_analyse_topic(get_path): _, topic_df, most_frequent_topics = tt.PostprocessText( use_csv=True, csv_path=get_path + "topic_analysis_test.csv" ).analyse_topic() # since this is not deterministic we cannot be sure we get the same result twice assert len(topic_df) == 2 assert topic_df["Name"].iloc[0] == "0_the_feat_of_is" assert most_frequent_topics[0][0][0] == "the"