Removing unnecessary code

TailUFPB · Apr 14, 2024 · b346555 · b346555
1 parent 890c991
commit b346555
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Showing 7 changed files with 34 additions and 28 deletions.
diff --git a/api/NbEmotionsModel.py b/api/NbEmotionsModel.py
@@ -1,11 +1,5 @@
-import pandas as pd
 import pickle
 
-# bag of words
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-#tfidf_vectorizer = TfidfVectorizer(use_idf=True)
-
 def make_prediction(my_sentence):
  model_file = "./models/emotions_pipeline.pkl"
  try:

diff --git a/api/NbLinRegressionModel.py b/api/NbLinRegressionModel.py
diff --git a/api/NbNewsModel.py b/api/NbNewsModel.py
@@ -1,4 +1,3 @@
-import pandas as pd
 import pickle
 
 def news_prediction(texts):

diff --git a/api/models/OIGENTE b/api/models/OIGENTE
diff --git a/api/models_code/nb_emotions.py b/api/models_code/nb_emotions.py
@@ -1,8 +1,6 @@
 import pandas as pd
 import pickle
 from sklearn.pipeline import make_pipeline
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.feature_extraction.text import TfidfVectorizer
 
 # bag of words
 from sklearn.feature_extraction.text import TfidfVectorizer
@@ -11,7 +9,12 @@
 from sklearn.naive_bayes import MultinomialNB
 
 df = pd.read_csv('../training_df/tweet_emotions.csv')
-train_data, test_data, train_target, test_target = train_test_split(df["content"], df["sentiment"], test_size=0.2, shuffle=True)
+
+# Dividindo os dados em um conjunto de treinamento e um conjunto de teste
+x = df['content']
+y = df['sentiment']
+
+train_data, test_data, train_target, test_target = train_test_split(x, y, test_size=0.2, shuffle=True)
 
 # Criando um pipeline com o vetorizador TF-IDF e o classificador Multinomial Naive Bayes
 pipeline = make_pipeline(TfidfVectorizer(), MultinomialNB())

diff --git a/api/models_code/nb_news.py b/api/models_code/nb_news.py
@@ -1,18 +1,20 @@
 import pandas as pd
 import pickle
 from sklearn.pipeline import make_pipeline
-from sklearn.feature_extraction.text import CountVectorizer
+
+# bag of words
 from sklearn.feature_extraction.text import TfidfVectorizer
+
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import MultinomialNB
 
 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']
 
 X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
-# Criando um pipeline que inclui o vetorizador TF-IDF e o modelo Naive Bayes
 
 # Criando um pipeline com o vetorizador TF-IDF e o classificador Multinomial Naive Bayes
 pipeline = make_pipeline(TfidfVectorizer(), MultinomialNB())

diff --git a/api/requirements.txt.orig b/api/requirements.txt.orig
@@ -0,0 +1,24 @@
+<<<<<<< Updated upstream:api/requirements.txt
+Flask==2.3.2
+Flask-Cors==4.0.0
+pandas==2.0.3
+nltk==3.8.1
+scikit-learn==1.3.0
+numpy==1.25.2
+matplotlib==3.7.1
+seaborn==0.13.0
+tensorflow==2.16.1
+keras==3.0.0
+=======
+Flask==2.3.2
+Flask-Cors==4.0.0
+pandas==2.0.3
+nltk==3.8.1
+scikit-learn==1.3.0
+numpy==1.25.2
+matplotlib==3.7.1
+seaborn==0.13.0
+tensorflow==2.16.1
+keras==3.0.0
+torchtext==0.17.2
+>>>>>>> Stashed changes:requirements.txt