integration with pre trained pipelines

Author: jonasgabriel18

api/NbEmotionsModel.py

@@ -7,12 +7,16 @@
 #tfidf_vectorizer = TfidfVectorizer(use_idf=True)
 
 def make_prediction(my_sentence):
-    with open("./models/nb_emotion.pkl", "rb") as f:
-        model = pickle.load(f)
+    model_file = "./models/emotions_pipeline.pkl"
+    try:
+        # Carregando o pipeline do arquivo .pkl
+        with open(model_file, 'rb') as model_file:
+            pipeline = pickle.load(model_file)
 
-    with open("./models/tfidf_vectorizer_em.pkl", 'rb') as f:
-        tfidf_vectorizer = pickle.load(f)
+        # Fazendo previsões para os textos
+        predictions = pipeline.predict([texts])
 
-    new_sentence = tfidf_vectorizer.transform([my_sentence])
-    prediction = model.predict(new_sentence)
-    return prediction[0]
+        return predictions[0]
+
+    except Exception as e:
+        return str(e)

api/models/emotion_pipeline.pkl

@@ -1,5 +1,8 @@
 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
@@ -10,15 +13,12 @@
 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)
 
-tfidf_vectorizer = TfidfVectorizer(use_idf=True)
-X_train_vectors_tfidf = tfidf_vectorizer.fit_transform(train_data)
-X_test_vectors_tfidf = tfidf_vectorizer.transform(test_data)
-
-nb_tfidf = MultinomialNB(alpha = 0)
-nb_tfidf.fit(X_train_vectors_tfidf, train_target)
+# Criando um pipeline com o vetorizador TF-IDF e o classificador Multinomial Naive Bayes
+pipeline = make_pipeline(TfidfVectorizer(), MultinomialNB())
 
-with open("../models/nb_emotion.pkl", "wb") as f:
-    pickle.dump(nb_tfidf, f)
+# Ajustando o modelo ao conjunto de treinamento
+pipeline.fit(train_data, train_target)
 
-with open("../models/tfidf_vectorizer_em.pkl", "wb") as f:
-    pickle.dump(tfidf_vectorizer, f)
+# Salvando o pipeline em um arquivo .pkl
+with open("../models/emotion_pipeline.pkl", "wb") as model_file:
+    pickle.dump(pipeline, model_file)