text: import pandas as pd
  import pandas as pd
  import numpy as np
  from sklearn.metrics import confusion_matrix,classification_report
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  import pickle
  import os
  from . import pls_work as ps
  try:
      import create_the_base as ps
  except ImportError:
      from . import create_the_base as ps
  
  
  
  
  modulePath = os.path.dirname(__file__)  # get current directory
  filePath = os.path.join(modulePath, 'corrected_dataset.csv')
  
  df=pd.read_csv(filePath)
  print(df.head())
  df.drop('Unnamed: 0',axis=1,inplace=True)
  X=df['title'].fillna(value='')
  y=df['flair']
  X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)
  Tipeline=ps.pipeline
  Tipeline.fit(X_train,y_train)
  save_classifier=open("svc_f2_model_last"+"title"+".pickle","wb")
  pickle.dump(Tipeline,save_classifier)
  save_classifier.close()
  predictions = Tipeline.predict(X_test)
  print('This is for','')
  print(confusion_matrix(y_test,predictions))
  print(classification_report(y_test,predictions))
  print('accuracy : ',accuracy_score(y_test,predictions))
  
  
  
  '''comb_feats=df['comments']+df['title']+df['url']
  X=comb_feats.fillna(value='')
  y=df['flair']
  X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)
  print('into pipeline')
  pipeline.fit(X_train,y_train)
  print('out of pipeline')
  save_classifier=open("svc_f2"+"comb2_correct"+".pickle","wb")
  pickle.dump(pipeline,save_classifier)
  save_classifier.close()
  predictions = pipeline.predict(X_test)
  print('This is for','')
  print(confusion_matrix(y_test,predictions))
  print(sns.heatmap(confusion_matrix(y_test,predictions)))
  print(classification_report(y_test,predictions))
  print('accuracy : ',accuracy_score(y_test,predictions))'''
  
      
  
  
  
  