def classify(title="NAN", text="NAN"): data = feature2.process(text) data = pd.DataFrame(data) data.columns = data.columns = [ 'text_wordCount', 'text_charCount', 'text_puncCount', 'text_upperCount', 'text_gunning_fog', 'text_automated_readability_index', 'text_linsear_write_formula', 'text_difficult_words', 'text_dale_chall_readability_score', 'txp1', 'txp2', 'txp3', 'txp4', 'txp5', 'txp6', 'txp7', 'txp8', 'txp9', 'txp10' ] data1 = feature2.process(title) data1 = pd.DataFrame(data1) data1.columns = [ 'title_wordCount', 'title_charCount', 'title_puncCount', 'title_upperCount', 'title_gunning_fog', 'title_automated_readability_index', 'title_linsear_write_formula', 'title_difficult_words', 'title_dale_chall_readability_score', 'tlp1', 'tlp2', 'tlp3', 'tlp4', 'tlp5', 'tlp6', 'tlp7', 'tlp8', 'tlp9', 'tlp10' ] result = pd.concat([data, data1], axis=1, join='inner') file_Name = "trainedModel.sav" fileObject = open(file_Name, 'rb') # load the object from the file into var b b = pickle.load(fileObject) a = b.predict(result) result = result.values #print(result[1]) return a, result
def classify(title="NAN", text="NAN"): data = feature2.process(text) data = pd.DataFrame(data) data.columns = data.columns = [ 'text_wordCount', 'text_charCount', 'text_puncCount', 'text_upperCount', 'text_gunning_fog', 'text_automated_readability_index', 'text_linsear_write_formula', 'text_difficult_words', 'text_dale_chall_readability_score', 'txp1', 'txp2', 'txp3', 'txp4', 'txp5', 'txp6', 'txp7', 'txp8', 'txp9', 'txp10' ] data1 = feature2.process(title) data1 = pd.DataFrame(data1) data1.columns = [ 'title_wordCount', 'title_charCount', 'title_puncCount', 'title_upperCount', 'title_gunning_fog', 'title_automated_readability_index', 'title_linsear_write_formula', 'title_difficult_words', 'title_dale_chall_readability_score', 'tlp1', 'tlp2', 'tlp3', 'tlp4', 'tlp5', 'tlp6', 'tlp7', 'tlp8', 'tlp9', 'tlp10' ] result = pd.concat([data, data1], axis=1, join='inner') file_Name1 = "trainedModel.sav" fileObject1 = open(file_Name1, 'rb') file_Name2 = "svm.sav" fileObject2 = open(file_Name2, 'rb') # load the object from the file into var b rf = pickle.load(fileObject1) svm = pickle.load(fileObject2) a1 = rf.predict(result) a2 = svm.predict(result) if a1[0] == 1 and a2[0] == 0: a = 'Authentic News' elif a1[0] == 0 and a2[0] == 0: a = 'Fake News' else: a = 'Authentic News' # if a2[0] == 0: # a = 'Fake News' # # else: # a = 'Authentic News' result = result.values # print(result[1]) a = b.predict(result) print(a)