Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+from transformers import BertTokenizer
+from transformers import TFBertForSequenceClassification
+
+
+# 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')
+
+
+def predict_sentiment(text):
+    input_ids = tf.constant(bert_tokenizer.encode(text, add_special_tokens=True))[None, :]  # Menambahkan token khusus [CLS] dan [SEP]
+    logits = bert_model(input_ids)[0]
+    probabilities = tf.nn.softmax(logits, axis=1)
+    sentiment = tf.argmax(probabilities, axis=1)
+    return sentiment.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 = predict_sentiment(text)
+        if sentiment == 0:
+            st.error('Sentimen: Negatif')
+        else:
+            st.success('Sentimen: Positif')