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import streamlit as st
import tensorflow as tf
import numpy as np
import transformers
from transformers import AutoTokenizer,TFBertForSequenceClassification
import re
import string
import preprocessor as p
from tensorflow import keras
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("indolem/indobert-base-uncased")
# Define the maximum sequence length
max_seq = 110
# Function to preprocess the data
def preprocess_data(data):
data = data.tolist() # Convert numpy array to list
processed_data = []
for sentence in data:
sentence = text_preprocess(sentence)
encoded_data = tokenizer.encode_plus(
sentence,
add_special_tokens=True,
max_length=max_seq,
padding="max_length",
truncation=True,
return_tensors="tf"
)
processed_data.append((encoded_data['input_ids'], encoded_data['attention_mask']))
return processed_data
# Function to preprocess the sentence
def text_preprocess(sentence):
pattern = r'[0-9]'
for punctuation in string.punctuation:
sentence = p.clean(sentence)
sentence = re.sub(r'[^a-zA-Z0-9\s]', '', sentence)
sentence = re.sub(r'http[s]?://\S+', '', sentence)
sentence = sentence.replace(punctuation, '')
sentence = re.sub(pattern, '', sentence)
sentence = re.sub(r'\r?\n|\r', '', sentence)
sentence = sentence.encode('ascii', 'ignore').decode('ascii')
sentence = sentence.lower()
return sentence
# Function to perform sentiment prediction
def predict_sentiment(sentence):
preprocessed_sentence = preprocess_data(np.array([sentence]))
input_ids, attention_mask = preprocessed_sentence[0]
prediction = model.predict([input_ids, attention_mask])
predicted_label = np.argmax(prediction)
label_mapping = {0: "negative", 1: "neutral", 2: "positive"}
predicted_label = label_mapping[predicted_label]
return predicted_label
# Streamlit app
def main():
st.title("Analisis Sentimen Berbahasa Indonesia")
sentence = st.text_input("Masukkan teks disini:")
if st.button("Cek Kalimat"):
st.write("Hasil Klasifikasi:")
sentiment = predict_sentiment(sentence)
if sentiment == "positive":
st.markdown('<div style="background-color: green; padding: 10px; color:white;">Sentiment: positive</div>', unsafe_allow_html=True)
elif sentiment == "negative":
st.markdown('<div style="background-color: #FE4365; padding: 10px; color:white;">Sentiment: negative</div>', unsafe_allow_html=True)
elif sentiment == "neutral":
st.markdown('<div style="background-color: #FDFD96; padding: 10px; color: black;">Sentiment: neutral</div>', unsafe_allow_html=True)
if __name__ == '__main__':
# Register the custom objects using custom_object_scope
with keras.utils.custom_object_scope({'TFBertForSequenceClassification': transformers.TFBertForSequenceClassification}):
# Load the saved model
model = TFBertForSequenceClassification.from_pretrained('muhfrrazi/IndoBERT-Sentiment-Analysist_Dataset-Indonesia')
main()
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