| 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]] | |