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# app.py
import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# --- Load model and tokenizer ---
model_name = "taufiqdp/indonesian-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# --- Prediction function ---
def predict_sentiment(text):
if not text.strip():
return "⚠️ Mohon masukkan teks."
inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(device)
with torch.inference_mode():
logits = model(**inputs).logits
pred_class = torch.argmax(logits, dim=1).item()
labels = ['Negatif', 'Netral', 'Positif']
sentiment = labels[pred_class]
return f"**Hasil Analisis Sentimen:** {sentiment}"
# --- Build Gradio UI ---
title = "🇮🇩 Indonesian Sentiment Analyzer"
description = "Masukkan teks Bahasa Indonesia untuk mengetahui apakah sentimennya **Positif**, **Netral**, atau **Negatif**."
interface = gr.Interface(
fn=predict_sentiment,
inputs=gr.Textbox(lines=4, placeholder="Contoh: Pelayanan cepat dan memuaskan"),
outputs="markdown",
title=title,
description=description,
theme="gradio/soft",
examples=[
["Makanan di restoran ini sangat enak dan lezat."],
["Pelayanan biasa saja, tidak buruk tapi tidak istimewa."],
["Saya kecewa dengan produk ini."]
]
)
if __name__ == "__main__":
interface.launch()
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