Create app.py
Browse files
app.py
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import tensorflow as tf
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from transformers import BertTokenizer
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from transformers import TFBertForSequenceClassification
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from Sastrawi.Stemmer.StemmerFactory import StemmerFactory # Import Sastrawi
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import streamlit as st
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# Fungsi untuk memuat model BERT dan tokenizer
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PRE_TRAINED_MODEL = 'indobenchmark/indobert-base-p2'
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bert_tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL)
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bert_model = TFBertForSequenceClassification.from_pretrained(PRE_TRAINED_MODEL, num_labels=2)
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bert_model.load_weights('model.h5')
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# Inisialisasi stemmer dari Sastrawi
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stemmer = StemmerFactory().create_stemmer() # Membuat stemmer Sastrawi
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def preprocess_text(text):
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# Menggunakan Sastrawi untuk stemming
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stemmed_text = stemmer.stem(text.lower())
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return stemmed_text
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def predict_sentiment(text):
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preprocessed_text = preprocess_text(text) # Pra-pemrosesan teks dengan Sastrawi
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input_ids = tf.constant(bert_tokenizer.encode(preprocessed_text, add_special_tokens=True))[None, :]
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logits = bert_model(input_ids)[0]
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probabilities = tf.nn.softmax(logits, axis=1)
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sentiment = tf.argmax(probabilities, axis=1)
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return sentiment.numpy()[0], probabilities.numpy()[0]
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# Judul aplikasi
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st.title('Prediksi Sentimen menggunakan BERT')
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# Input teks
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text = st.text_area('Masukkan teks', '')
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# Tombol untuk memprediksi sentimen
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if st.button('Prediksi'):
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if text.strip() == '':
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st.warning('Masukkan teks terlebih dahulu.')
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else:
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sentiment, probabilities = predict_sentiment(text)
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# Menghitung persentase probabilitas sentimen positif
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positive_probability = probabilities[1] * 100
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negative_probability = probabilities[0] * 100
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st.write(f'HASIL PREDIKSI')
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if sentiment == 0:
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st.write(f'Negatif ({negative_probability:.2f}%)')
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else:
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st.write(f'Positif ({positive_probability:.2f}%)')
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