Create app.py

app.py

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