Create app.py

app.py

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+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()