text: from app.data.data_handler import DataHandler
  from app.service.analyser import Analyser
  
  import pandas as pd
  import argparse
  import spacy
  import nltk
  
  from app.LeIA.leia import SentimentIntensityAnalyzer
  
  class RunAppTest:
      def __init__(self) -> None:
          nltk.download('stopwords')
          self.stopwords = nltk.corpus.stopwords.words('portuguese')
          pass
      
      def execute(self, text_file: str):
          dh = DataHandler(stopwords_list=self.stopwords)
          dh.receive_data(text_file)
          anl = Analyser()
          dh.bigrams = anl.find_bigrams(dh.data)
          dh.trigrams = anl.find_trigrams(dh.data, 6)
          dh.tfidf = anl.calculate_tfidf(dh.data)
          dh.pos = anl.calculate_top_postaggs(dh.data, "S", 4)
          dh.sentiment = anl.calculate_sentiment(dh.data)
  
          #for s in dh.sentiment:
          #    print (s.polarity,'\t', s.polarity_dict, '\t', s.text)
  
          #print(dh.trigrams)
          #print(pd.DataFrame.from_dict(dh.tfidf.idf, orient="index", columns=["Rank"]))
          #print(dh.data.pruned_data)
  
          #sent_analyser = SentimentIntensityAnalyzer()
          #for sentence in dh.data.pruned_data:
          #    print(sentence, sent_analyser.polarity_scores(sentence))
  
          return "This file has {nl} lines and {nw} words".format(nl = dh.line_count, nw=dh.word_count)
  
  if __name__ == "__main__":
  
      parser = argparse.ArgumentParser(description='')
      parser.add_argument('input_file', type=str, help='input file name')
      args = parser.parse_args()
  
      print(RunAppTest().execute(args.input_file))