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import gradio as gr
import torch
import joblib
from transformers import AutoTokenizer
from dinstilBert import MultiTaskBERT
model = MultiTaskBERT()
model.load_state_dict(torch.load("model.pt", map_location="cpu"))
model.eval()
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-multilingual-cased")
le = joblib.load("label_encoder.pkl")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def predict(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
with torch.no_grad():
sentiment_logits, lang_logits = model(inputs["input_ids"], inputs["attention_mask"])
pred_sentiment = sentiment_logits.argmax(dim=1).item()
pred_lang = lang_logits.argmax(dim=1).item()
if pred_sentiment == 2:
sentiment_label = "positive"
elif pred_sentiment == 1:
sentiment_label = "neutral"
else:
sentiment_label = "negative"
lang_code_map = {
'de': 'German',
'es': 'Espanyol',
'en': 'English',
'fr': 'French'
}
lang_code = le.inverse_transform([pred_lang])[0]
lang_label = lang_code_map.get(lang_code, "Unknown")
return sentiment_label, lang_label
interface = gr.Interface(
fn=predict,
inputs=gr.Textbox(label="Masukkan Teks Dalam Bahasa (Inggris/Jerman/Spanyol/Perancis)"),
outputs=[
gr.Textbox(label="Prediksi Sentiment (Positif/Neutral/Negatif)"),
gr.Textbox(label="Prediksi Bahasa")
],
title="Multitask DistilBERT: Sentiment + Language",
description="Prediksi sentimen dan bahasa dari teks menggunakan model multitask DistilBERT."
)
interface.launch() |