import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model sekali saja
MODEL_NAME = "taufiqdp/indonesian-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

class_names = ['negatif', 'netral', 'positif']

def predict_sentiment(text):
    if not text or text.strip() == "":
        return "Teks kosong"

    tokenized = tokenizer(text, return_tensors="pt")
    with torch.inference_mode():
        logits = model(**tokenized).logits

    pred_id = logits.argmax(dim=1).item()
    sentiment = class_names[pred_id]
    confidence = torch.softmax(logits, dim=1)[0][pred_id].item()

    return f"{sentiment} ({confidence:.2%})"

# Gradio interface
demo = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(label="Masukkan teks"),
    outputs=gr.Textbox(label="Prediksi Sentimen"),
    title="Indonesian Sentiment Analysis",
    description="Model klasifikasi sentimen bahasa Indonesia (negatif, netral, positif)."
)

if __name__ == "__main__":
    demo.launch()