src/streamlit_app.py

import streamlit as st
from transformers import pipeline

# Load model
@st.cache_resource
def load_model():
    return pipeline(
        "sentiment-analysis",
        model="w11wo/indonesian-roberta-base-sentiment-classifier"
    )

classifier = load_model()

# UI
st.title("Sentiment Analysis Bahasa Indonesia 🇮🇩")

text = st.text_area("Masukkan teks:")

if st.button("Analisis"):
    if text.strip() != "":
        result = classifier(text)

        label = result[0]['label']
        score = result[0]['score']

        st.write(f"Sentimen: {label}")
        st.write(f"Confidence: {score:.2f}")

        if label.lower() == "positive":
            st.success("Positif")
        elif label.lower() == "negative":
            st.error("Negatif")
    else:
        st.warning("Masukkan teks dulu!")