Update app.py

app.py

@@ -1,10 +1,36 @@
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-# Memuat model dan tokenizer dari Hugging Face
-model = AutoModelForSequenceClassification.from_pretrained('hasbigani/sentiment')  # Ganti dengan username dan repo kamu
-tokenizer = AutoTokenizer.from_pretrained('hasbigani/sentiment')  # Ganti dengan username dan repo kamu
+# Ganti dengan nama repository model kamu
+model_name = "username/indobertsentiment"
 
-# Contoh penggunaan model untuk memproses input
-inputs = tokenizer("Masukkan teks di sini", return_tensors="pt")  # Tokenisasi teks
-outputs = model(**inputs)  # Prediksi hasil
-print(outputs)
+# Load model & tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Label mapping (ubah sesuai label modelmu)
+label_map = {
+    0: "Negatif",
+    1: "Netral",
+    2: "Positif"
+}
+
+# Fungsi prediksi
+def predict_sentiment(text):
+    inputs = tokenizer([text], padding=True, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        pred = torch.argmax(outputs.logits, dim=-1).item()
+    return label_map[pred]
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Tulis teks di sini..."),
+    outputs=gr.Label(),
+    title="Demo Sentimen IndoBERT",
+    description="Masukkan kalimat berbahasa Indonesia untuk menguji model sentimen yang sudah diupload di Hugging Face."
+)
+
+iface.launch()