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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load tokenizer dan model dari Hugging Face Hub
model_name = "ElizabethSrgh/customer-service-multitask"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Daftar label sesuai urutan model (ubah jika berbeda)
label_map = {
0: "Complaint - Negative",
1: "Inquiry - Neutral",
2: "Request - Positive"
}
def predict(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_id = torch.argmax(logits, dim=1).item()
return label_map.get(predicted_class_id, "Unknown")
# Gradio UI
interface = gr.Interface(
fn=predict,
inputs=gr.Textbox(lines=4, label="Masukkan Teks Percakapan"),
outputs=gr.Textbox(label="Hasil Prediksi"),
title="Klasifikasi Layanan Pelanggan",
description="Masukkan teks untuk memprediksi topik dan sentimen."
)
if __name__ == "__main__":
interface.launch()
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