text: import sklearn
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.naive_bayes import MultinomialNB
  import nltk
  from nltk import *
  import csv
  txt=[]
  lab=[]
  with open('[your classifier filename]') as csvfile:
  	reader=csv.DictReader(csvfile)
  	for row in reader:	
  		t=str(row['TEXT']),str(row['LABEL'])
  		txt.append(t[0])
  		lab.append(t[1])
  
  print (len(txt))
  print (len(lab))
  
  
  count_vect = CountVectorizer()
  train_counts = count_vect.fit_transform(txt)
  transformer = TfidfTransformer(use_idf=False).fit(train_counts)
  train_tf = transformer.transform(train_counts)
  clf = MultinomialNB().fit(train_tf, lab)
  
  docs_new =[]
  with open('[your texts you want to analyze]') as csvfile:
  	reader=csv.DictReader(csvfile)
  	for row in reader:	
  		t=str(row['TEXT'])
  		docs_new.append(t)
  
  
  new_counts = count_vect.transform(docs_new)
  new_tf = transformer.transform(new_counts)
  predicted = clf.predict(new_tf)
  
  
  label=[]
  y=1
  
  for i,x in zip(docs_new,predicted):
  ##USE THIS LINES IF YOU WANT A HUMAN SUPERVISION#####
  	print (i,'LABEL:',x)
  	rep=input('OK?')
  	if rep=='':
  		wr=i,x
  		label.append(wr)
  	else:
  		wr=i,'1'
  		label.append(wr)
  	print (len(docs_new)-y,'TO GO')
  	y+=1
  #########################################################
  ##USE THIS LINES IF YOU DON'T WANT A HUMAN SUPERVISION###
    wr=i,x
  	label.append(wr)
  ##########################################################
  
  with open('[where you want to save the results of this A.I.]','a') as csvfile:
  	fieldnames = ['TEXT','LABEL']
  	writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
  	for i in label:
  		writer.writerow({'TEXT':i[0],'LABEL':i[1]})
  