Upload app.py

app.py

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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()