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import re
import emoji
import tensorflow as tf
from transformers import BertTokenizer,TFBertForSequenceClassification
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
model.load_weights('weights.h5')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
def preprocess(text):
text = emoji.demojize(text)
text = text.replace(":"," ")
text = ' '.join(text.split())
text = re.sub("@[A-Za-z0-9]+", "", text)
text = re.sub("#", "", text)
text = re.sub("https?://[A-Za-z0-9./]+", "", text)
text = re.sub("[^a-zA-Z.!?']", " ", text)
return text
def predict(text):
text = preprocess(text)
tf_batch = tokenizer([text], max_length=128, padding=True, truncation=True, return_tensors='tf')
tf_outputs = model(tf_batch)
tf_predictions = tf.nn.softmax(tf_outputs[0], axis=-1)
labels = ['Negative','Positive']
label = tf.argmax(tf_predictions, axis=1)
label = label.numpy()
return labels[label[0]]
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