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]]