Adicionando novo modelo

api/NN_Fakenews_2.py

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+import pandas as pd
+import pickle
+from Neural_Network2 import create_and_train_model
+
+
+def Neural_Network(texts_train,labels_train):
+ trained_model = create_and_train_model(texts_train, labels_train)
+
+# Saving the model
+ with open("nn_fakenews_model.pkl", "wb") as model_file:
+ pickle.dump(trained_model, model_file)
+
+
+ return trained_model
+
+
+

api/Neural_Network2.py

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+import csv
+import re
+import string
+import os
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+import pickle
+from tensorflow.keras import layers
+from tensorflow.keras.layers import TextVectorization
+from tensorflow.keras.models import Sequential
+
+def preprocess_text(text):
+ text = text.lower()
+ text = re.sub('\[.*?\]', '', text)
+ text = re.sub("\\W", " ", text)
+ text = re.sub('https?://\S+|www\.\S+', '', text)
+ text = re.sub('<.*?>+', '', text)
+ text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
+ text = re.sub('\n', '', text)
+ text = re.sub('\w*\d\w*', '', text)
+ return text
+
+def create_and_train_model(train_texts, train_labels, epochs=5):
+ train_df = pd.DataFrame({'text': train_texts, 'label': train_labels})
+ train_df['text'] = train_df['text'].apply(preprocess_text)
+
+ output_directory = 'arquivos_texto_treino_nn'
+ os.makedirs(output_directory, exist_ok=True)
+
+ for index, row in train_df.iterrows():
+ filename = os.path.join(output_directory, f'texto_{index}.txt')
+ with open(filename, 'w', encoding='utf-8') as file:
+ file.write(row['text'])
+
+ treino_dir = output_directory
+
+ train_dataset = tf.keras.utils.text_dataset_from_directory(
+ treino_dir,
+ batch_size=32,
+ shuffle=True,
+ )
+
+ max_features = 20000
+ embedding_dim = 128
+ sequence_length = 500
+
+ vectorize_layer = TextVectorization(
+ max_tokens=max_features,
+ output_mode="int",
+ output_sequence_length=sequence_length,
+ )
+
+ text_ds = train_dataset.map(lambda x, y: x)
+ vectorize_layer.adapt(text_ds)
+
+ def vectorize_text(text, label):
+ text = tf.expand_dims(text, -1)
+ return vectorize_layer(text), label
+
+ train_ds = train_dataset.map(vectorize_text)
+ train_ds = train_ds.cache().prefetch(buffer_size=10)
+
+ inputs = tf.keras.Input(shape=(None,), dtype="int64")
+ x = layers.Embedding(max_features, embedding_dim)(inputs)
+ x = layers.Dropout(0.5)(x)
+ x = layers.Conv1D(128, 7, padding="valid", activation="relu", strides=3)(x)
+ x = layers.Conv1D(128, 7, padding="valid", activation="relu", strides=3)(x)
+ x = layers.GlobalMaxPooling1D()(x)
+ x = layers.Dense(128, activation="relu")(x)
+ x = layers.Dropout(0.5)(x)
+ predictions = layers.Dense(1, activation="sigmoid", name="predictions")(x)
+
+ model = tf.keras.Model(inputs, predictions)
+
+ model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
+ model.fit(train_ds, epochs=epochs)
+
+ return model