diff --git a/api/DataProcesser.py b/api/DataProcesser.py
index be56cbda..7c82fff2 100644
--- a/api/DataProcesser.py
+++ b/api/DataProcesser.py
@@ -7,19 +7,19 @@
 from nltk.corpus import stopwords
 # mais imports
 
-
 class DataProcesser():
-
     df = pd.DataFrame()
     input_column = ''
-    stopwordsenglish = nltk.corpus.stopwords.words('english')
-
+    stopwords_english = stopwords.words('english')
 
     def handle_classify(self, df, classifier):
-        if classifier == 'a':
-            return self.classify_emotions(df)
-        elif classifier == 'b':
-            return self.nb_news_application(df)
+        classifier_switcher = {
+            0: self.classify_emotions,
+            1: self.nb_news_application,
+            2: self.lin_regression_model
+        }
+
+        return classifier_switcher.get(classifier, lambda: "Invalid Classifier")(df)
 
     def preprocess_text(self, texto):
         if self.input_column is not None:  # Verifique se a coluna foi definida
@@ -27,34 +27,32 @@ def preprocess_text(self, texto):
             texto = re.sub('[^a-z\s]', '', texto.lower())
             # tokenizo em palavras e elimino as stopwords
             palavras = [w for w in texto.split(
-            ) if w not in set(self.stopwordsenglish)]
+            ) if w not in set(self.stopwords_english)]
             palavras = [w for w in texto if nltk.corpus.wordnet.synsets(w)]
             # texto_junto = ' '.join(texto)
             # junto as palavras novamente com espaços
-            return ' '.join(palavras)
+            return ''.join(palavras)
         else:
             return "Coluna não escolhida. Escolha a coluna primeiro."
 
-    def nb_news_application(self, df):
-        nb_model = NbNewsModel(df)
-        df_result = nb_model.filter_and_classify()
-        return df_result
-
     def classify_emotions(self, df):
         df['output_column'] = df['input_column'].apply(
             self.preprocess_text).apply(make_prediction)
+
         result_csv = df  # converte o df pra csv
         return result_csv
 
     def lin_regression_model(self, df):
         df['output_column'] = df['input_column'].apply(
             self.preprocess_text).apply(make_prediction_nblin)
+
         result_csv = df  # converte o df pra csv
         return result_csv
 
-    def nb_news_application(self):
-        self.df['coluna_classificada'] = self.df[self.input_column].apply(self.preprocess_text).apply(news_prediction)
-        result_csv = self.df
+    def nb_news_application(self, df):
+        df['output_column'] = df['input_column'].apply(news_prediction)
+
+        result_csv = df
         return result_csv
 
     ##TODO métodos com o processamento de classificação
diff --git a/api/NbLinRegressionModel.py b/api/NbLinRegressionModel.py
index 4281320a..472aecd8 100644
--- a/api/NbLinRegressionModel.py
+++ b/api/NbLinRegressionModel.py
@@ -7,7 +7,7 @@ def make_prediction_nblin(my_sentence):
     with open("./models/linear_reg.pkl", "rb") as f:
         model = pickle.load(f)
 
-    new_sentence = vectorizer.transform(my_sentence)
+    new_sentence = vectorizer.transform([my_sentence])
 
     prediction = model.predict(new_sentence)
     if prediction == 0:
diff --git a/api/NbNewsModel.py b/api/NbNewsModel.py
index 174def97..bbe8f9ba 100644
--- a/api/NbNewsModel.py
+++ b/api/NbNewsModel.py
@@ -2,18 +2,16 @@
 import pickle
 
 def news_prediction(texts):
-    model_file = "api/models/text_classification_pipeline.pkl"
+    model_file = "./models/text_classification_pipeline.pkl"
     try:
         # Carregando o pipeline do arquivo .pkl
         with open(model_file, 'rb') as model_file:
             pipeline = pickle.load(model_file)
 
         # Fazendo previsões para os textos
-        predictions = pipeline.predict(texts)
+        predictions = pipeline.predict([texts])
+
+        return predictions[0]
 
-        return predictions
-    
     except Exception as e:
         return str(e)
-# df = pd.read_csv("api/training_df/nb_news.csv")
-# print(news_prediction(df['short_description']))
\ No newline at end of file
diff --git a/api/models/nb_news.py b/api/models/nb_news.py
index db90a8dd..b32627d8 100644
--- a/api/models/nb_news.py
+++ b/api/models/nb_news.py
@@ -6,7 +6,7 @@
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import MultinomialNB
 
-df = pd.read_csv("api/training_df/nb_news.csv")
+df = pd.read_csv("../training_df/nb_news.csv")
 # Dividindo os dados em um conjunto de treinamento e um conjunto de teste
 x = df['short_description']
 y = df['category']
diff --git a/api/models/text_classification_pipeline.pkl b/api/models/text_classification_pipeline.pkl
index be499f23..7ae389e6 100644
Binary files a/api/models/text_classification_pipeline.pkl and b/api/models/text_classification_pipeline.pkl differ