text: import requests
  import json
  
  '''
  This function offers the ability to predict the sentiment of a single sentence 
  through the API, the sentiment is one of three classes (positive negative, neutral)
  Input: 
          sentence(str): the input sentence of which the sentiment is to be predicted
  Output:
          prediction(str): the sentiment of the given sentence 
  '''
  
  
  def predict(sentence):
      url = "http://mazajak.inf.ed.ac.uk:8000/api/predict"
      to_sent = {'data': sentence}
      data = json.dumps(to_sent)
      headers = {'content-type': 'application/json'}
      # sending get request and saving the response as response object
      response = requests.post(url=url, data=data, headers=headers)
  
      prediction = json.loads(response.content)['data']
  
      return prediction
  
  
  '''
  This function offers the ability to predict the sentiment of a list of sentences
  through the API, the sentiment is one of three classes (positive negative, neutral)
  Input: 
          sent_lst(list of str): the input list of which the sentiment of its sentences is to be predicted
  Output:
          prediction(list of str): the sentiments of the given sentences
  '''
  
  
  def predict_list(sent_lst):
      url = "http://mazajak.inf.ed.ac.uk:8000/api/predict_list"
      to_sent = {'data': sent_lst}
      data = json.dumps(to_sent)
      headers = {'content-type': 'application/json'}
      # sending get request and saving the response as response object
      response = requests.post(url=url, data=data, headers=headers)
  
      prediction = json.loads(response.content)['data']
  
      return prediction
  
  
  '''
  This is an example to use the functions
  '''
  tweets = ['انا زعلان منك', 'انا مبسوط اوي']
  l = []
  for tweet in tweets:
      print(predict(tweet))
  
  print(predict_list(tweets))
  