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| import streamlit as st | |
| from transformers import pipeline | |
| from transformers import MarianMTModel, MarianTokenizer | |
| # Inisialisasi model terjemahan | |
| model_name = "Helsinki-NLP/opus-mt-id-en" | |
| tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| model = MarianMTModel.from_pretrained(model_name) | |
| # Inisialisasi model analisis sentimen | |
| sentiment_pipe = pipeline('sentiment-analysis') | |
| text = st.text_area('Masukkan teks dalam bahasa Indonesia') | |
| if text: | |
| # Terjemahkan teks ke bahasa Inggris | |
| inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) | |
| outputs = model.generate(**inputs) | |
| translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Analisis sentimen | |
| sentiment_result = sentiment_pipe(translated_text) | |
| # Tampilkan hasil | |
| st.write(f"Hasil Analisis Sentimen: {sentiment_result[0]['label']}") | |
| st.write(f"Terjemahan ke Bahasa Inggris: {translated_text}") | |