Create app.py

app.py

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+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()