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# Import library yang dibutuhkan | |
import streamlit as st | |
from transformers import pipeline | |
# Buat title pada web apps | |
st.title("Sentiment Analysis App") | |
st.markdown("You can input 8 different language: arabic, englisg, french, german, hindi, italian, portuguese, and spanish") | |
# Buat text input dari user | |
text = st.text_input("Enter text here", "I like langing academy course") | |
# Buat variabel pipeline yang berisi model | |
pipeline = pipeline(task='text-classification', model='cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual') | |
# Buat button predict | |
if st.button("Predict"): | |
result = pipeline(text)[0] # Menyimpan nilai predict pada variabel result | |
sentiment = result['label'] # Menyimpan nilai label pada variabel sentiment | |
score = result['score'] # Menyimpan nilai confidence score pada variabel score | |
st.write(f"Sentiment: {sentiment}") | |
# Jika score ada nilainya maka tampilkan scorenya | |
if score is not None: | |
st.write(f"Score: {score}") |