nb lin regression model method name fix

api/DataProcesser.py

@@ -1,7 +1,7 @@
 import pandas as pd
 from NbNewsModel import NbNewsModel
 from NbEmotionsModel import make_prediction
-from NbLinRegressionModel import make_prediction
+from NbLinRegressionModel import make_prediction_nblin
 from io import BytesIO
 import nltk
 import re
@@ -45,12 +45,12 @@ def classify_emotions(self, df):
  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)
+ self.preprocess_text).apply(make_prediction_nblin)
  result_csv = df # converte o df pra csv
  return result_csv
- 
+
 
  # TODO métodos com o processamento de classificação

api/NbLinRegressionModel.py

@@ -1,14 +1,14 @@
 import pickle
 
-def make_prediction(my_sentence):
+def make_prediction_nblin(my_sentence):
  with open("./models/vectorizer_lin.pkl", "rb") as f:
  vectorizer = pickle.load(f)
 
  with open("./models/linear_reg.pkl", "rb") as f:
  model = pickle.load(f)
 
  new_sentence = vectorizer.transform(my_sentence)
- 
+
  prediction = model.predict(new_sentence)
  if prediction == 0:
  return "Negativo"

main.js

@@ -20,10 +20,10 @@ function createWindow() {
  show: false, // Ocultar a janela até que esteja pronta
  //fullscreen: true,
  });
- win.loadURL("http://localhost:3000/"); // carrega a página hospedada localmente
- //win.loadFile(path.join(__dirname, "./build/index.html")); // carrega a build do react
+ //win.loadURL("http://localhost:3000/"); // carrega a página hospedada localmente
+ win.loadFile(path.join(__dirname, "./build/index.html")); // carrega a build do react
 
- /* 
+ /*
  win.loadURL(
  url.format({
  pathname: path.join(__dirname, "./build/index.html"), // relative path to the HTML-file