app.py

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

# Load model dan tokenizer dari Hugging Face
model = AutoModelForSequenceClassification.from_pretrained("kelompokjavonlp/sentiment_analysis")
tokenizer = AutoTokenizer.from_pretrained("kelompokjavonlp/sentiment_analysis")

# Update label sesuai model kamu
labels = ["Very Negative", "Negative", "Neutral", "Positive", "Very Positive"]

# Fungsi prediksi
def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        pred = torch.argmax(probs).item()
    return {labels[i]: float(probs[0][i]) for i in range(len(labels))}

# Interface Gradio
iface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(label="Masukkan Kalimat"),
    outputs=gr.Label(label="Hasil Sentimen"),
    title="Demo Analisis Sentimen Bahasa Indonesia",
    description="Model klasifikasi sentimen: Very Negative, Negative, Neutral, Positive, Very Positive."
)

iface.launch()