text: import numpy as np
  from PhishingWebsite import feature_extraction
  import PhishingWebsite.trainedmodel.model as tm
  from sklearn.ensemble import RandomForestClassifier as rfc
  #from sklearn.svm import SVC
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LogisticRegression as lr
  from flask import jsonify
  import pickle
  from googlesearch import search
  
  #Importing dataset
  """data = np.loadtxt(r"dataset/dataset.csv", delimiter = ",")
  
  #Seperating features and labels
  X = data[: , :-1]
  y = data[: , -1]
  
  #Seperating training features, testing features, training labels & testing labels
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
  clf = rfc()
  clf.fit(X_train, y_train)
  score = clf.score(X_test, y_test)
  #print(score*100)
  """
  #pickle.dump(clf,open('trained-model/model_random_forest.pkl','wb'))
  
  def getResult(url):
  
  
  
  
      model= tm.fmodel()
  
      X_new = []
  
      X_input = url
      X_new=feature_extraction.generate_data_set(X_input)
      X_new = np.array(X_new).reshape(1,-1)
  
      try:
          prediction = model.predict(X_new)
          if prediction == -1:
              return "Phishing Website"
          else:
              return "Legitimate Website"
      except:
          return "Phishing Website"
  