diff --git a/Project/simple_approach/simple_twitter_learning.py b/Project/simple_approach/simple_twitter_learning.py
index efd8ac1..8d13eee 100644
--- a/Project/simple_approach/simple_twitter_learning.py
+++ b/Project/simple_approach/simple_twitter_learning.py
@@ -538,151 +538,4 @@ class trainer(object):
             self.sdm.create_train_test_split()
         return self.pm.predict(self.sdm.Xt), self.sdm.yt
 
-    
-
-
-# ----
-# ## Train
-
-# * when in notebook environment: run the stuff below:
-
-# In[10]:
-
-
-import __main__ as main
-if not hasattr(main, '__file__'):
-    # we are in an interactive environment (probably in jupyter)
-    # load data:
-    
-    # setting n_kmeans_clusters to a value > 0 activates binarized labeling automatically! 
-    # set to -1 to disable kmeans clustering and generating labels in plain sentiment space
-    
-    #n_kmeans_cluster = 5
-    n_kmeans_cluster = -1
-    sdm = sample_data_manager.generate_and_read(path="./data_en/", n_top_emojis=20, file_range=range(1), n_kmeans_cluster=n_kmeans_cluster)
-    sdm.create_train_test_split()
-    #pm = pipeline_manager.create_keras_pipeline_with_vectorizer(vectorizer=TfidfVectorizer(stop_words='english'),\n",
-    #                                                           layers=[(10000, 'relu'),(5000, 'relu'),(2500, 'relu'),(y1[0].shape[0],None)], sdm=sdm)\n",
-    pm = pipeline_manager.create_keras_pipeline_with_vectorizer(vectorizer=TfidfVectorizer(stop_words='english'),
-                                                           layers=[(2500, 'relu'),(sdm.y.shape[1],None)], sdm=sdm)
-    tr = trainer(sdm=sdm, pm=pm)
-    tr.fit(100)
-
-
-# ----
-# ## save classifier
-
-# In[11]:
-
-
-import __main__ as main
-if not hasattr(main, '__file__'):
-    pm.save('custom_classifier')
-
-
-# ----
-# ## Prediction
-# 
-# * predict and save to `test.csv`
-
-# In[12]:
-
-
-import __main__ as main
-if not hasattr(main, '__file__'):
-    pred, teacher = tr.test()
-    
-    display(pred)
-    display(teacher)
-    
-    print('prediction variance: ', np.linalg.norm(np.var(pred, axis=0)))
-    print('teacher variance: ', np.linalg.norm(np.var(teacher, axis=0)))
-    
-    # build a dataframe to visualize test results:
-    testlist = pd.DataFrame({'text': sdm.Xt, 
-                         'teacher': sent2emoji(sdm.yt),
-                         'teacher_sentiment': sdm.yt.tolist(),
-                         'predict': sent2emoji(pred, custom_target_emojis=sdm.top_emojis),
-                         'predicted_sentiment': pred.tolist()})
-    # display:
-    display(testlist.head())
-    
-    # mean squared error:
-    teacher_sentiments = np.array([sample[1]['teacher_sentiment'] for sample in testlist.iterrows()])
-    predicted_sentiments = np.array([sample[1]['predicted_sentiment'] for sample in testlist.iterrows()])
-
-    mean_squared_error = ((teacher_sentiments - predicted_sentiments)**2).mean(axis=0)
-    print("Mean Squared Error: ", mean_squared_error)
-    print("Variance teacher: ", np.var(teacher_sentiments, axis=0))
-    print("Variance prediction: ", np.var(predicted_sentiments, axis=0))
-    
-    # save to csv:
-    testlist.to_csv('test.csv')
-
-
-# ----
-# ## Load classifier
-# 
-# * loading classifier and show a test widget
-
-# In[13]:
-
-
-import __main__ as main
-if not hasattr(main, '__file__'):
-    try:
-        pm
-    except NameError:
-        pass
-    else:
-        del pm # delete existing pipeline manager if ther is one
-
-    pm = pipeline_manager.load_pipeline_from_files( 'custom_classifier', ['keras_model'], ['vectorizer', 'keras_model'])
-    lookup_emojis = [#'😂',
-         '😭',
-         '😍',
-         '😩',
-         '😊',
-         '😘',
-         '🙏',
-         '🙌',
-         '😉',
-         '😁',
-         '😅',
-         '😎',
-         '😢',
-         '😒',
-         '😏',
-         '😌',
-         '😔',
-         '😋',
-         '😀',
-         '😤']
-    out = widgets.Output()
-
-    t = widgets.Text()
-    b = widgets.Button(
-        description='get emoji',
-        disabled=False,
-        button_style='', # 'success', 'info', 'warning', 'danger' or ''
-        tooltip='Click me',
-        icon='check'
-    )
-
-
-
-    def handle_submit(sender):
-        with out:
-            clear_output()
-        with out:
-            pred = pm.predict([t.value])
-
-            display(Markdown("# Predicted Emoji " + str(sent2emoji(pred, lookup_emojis)[0])))
-            display(Markdown("# Sentiment Vector: $$ \pmatrix{" + str(pred[0,0]) +
-                             "\\\\" + str(pred[0,1]) + "\\\\" + str(pred[0,2]) + "}$$"))
-
-    b.on_click(handle_submit)
-
-    display(t)
-    display(widgets.VBox([b, out]))  
-
+    
\ No newline at end of file