ข่าวจริงหรือเท็จ AI ช่วยคุณได้

import numpy as np 
import pandas as pd 
import json 
import csv 
import random 

from tensorflow.keras.preprocessing.text import Tokenizer 
from tensorflow.keras.preprocessing.sequence import pad_sequences 
from tensorflow.keras.utils import to_categorical 
from tensorflow.keras import regularizers 

import pprint 
import tensorflow.compat.v1 as tf 
from tensorflow.python.framework import ops 
from sklearn.model_selection import train_test_split 
from sklearn import preprocessing 
tf.disable_eager_execution() 

# Reading the data 
data = pd.read_csv("news.csv") 
data.head() 

data = data.drop(["Unnamed: 0"], axis=1) 
data.head(5) 

# encoding the labels 
le = preprocessing.LabelEncoder() 
le.fit(data['label']) 
data['label'] = le.transform(data['label']) 

embedding_dim = 50
max_length = 54
trunc_type = 'post'
padding_type = 'post'
oov_tok = "<OOV>"
training_size = 3000
test_portion = .1

title = [] 
text = [] 
labels = [] 
for x in range(training_size): 
    title.append(data['title'][x]) 
    text.append(data['text'][x]) 
    labels.append(data['label'][x]) 

tokenizer1 = Tokenizer() 
tokenizer1.fit_on_texts(title) 
word_index1 = tokenizer1.word_index 
vocab_size1 = len(word_index1) 
sequences1 = tokenizer1.texts_to_sequences(title) 
padded1 = pad_sequences( 
    sequences1, padding=padding_type, truncating=trunc_type) 
split = int(test_portion * training_size) 
training_sequences1 = padded1[split:training_size] 
test_sequences1 = padded1[0:split] 
test_labels = labels[0:split] 
training_labels = labels[split:training_size] 

embeddings_index = {} 
with open('glove.6B.50d.txt') as f: 
    for line in f: 
        values = line.split() 
        word = values[0] 
        coefs = np.asarray(values[1:], dtype='float32') 
        embeddings_index[word] = coefs 

# Generating embeddings 
embeddings_matrix = np.zeros((vocab_size1+1, embedding_dim)) 
for word, i in word_index1.items(): 
    embedding_vector = embeddings_index.get(word) 
    if embedding_vector is not None: 
        embeddings_matrix[i] = embedding_vector 

model = tf.keras.Sequential([ 
    tf.keras.layers.Embedding(vocab_size1+1, embedding_dim, 
                            input_length=max_length, weights=[ 
                                embeddings_matrix], 
                            trainable=False), 
    tf.keras.layers.Dropout(0.2), 
    tf.keras.layers.Conv1D(64, 5, activation='relu'), 
    tf.keras.layers.MaxPooling1D(pool_size=4), 
    tf.keras.layers.LSTM(64), 
    tf.keras.layers.Dense(1, activation='sigmoid') 
]) 
model.compile(loss='binary_crossentropy', 
            optimizer='adam', metrics=['accuracy']) 
model.summary() 

num_epochs = 50

training_padded = np.array(training_sequences1) 
training_labels = np.array(training_labels) 
testing_padded = np.array(test_sequences1) 
testing_labels = np.array(test_labels) 

history = model.fit(training_padded, training_labels, 
                    epochs=num_epochs, 
                    validation_data=(testing_padded, 
                                    testing_labels), 
                    verbose=2) 

# sample text to check if fake or not 
X = "Karry to go to France in gesture of sympathy"

# detection 
sequences = tokenizer1.texts_to_sequences([X])[0] 
sequences = pad_sequences([sequences], maxlen=54, 
                        padding=padding_type, 
                        truncating=trunc_type) 
if(model.predict(sequences, verbose=0)[0][0] >= 0.5): 
    print("This news is True") 
else: 
    print("This news is false") 

/usr/local/lib/python3.10/dist-packages/keras/src/engine/training_v1.py:2359: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
  updates=self.state_updates,
This news is True