Initial upload

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load tokenizer and model from Hugging Face Hub
+model_name = "TrioF/InSERT2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Create a pipeline
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Inference function
+def classify_text(text):
+    result = classifier(text)[0]
+    label = result["label"]  # This will already be mapped using id2label from config.json
+    score = round(result["score"], 3)
+    return f"{label} ({score})"
+
+# Build Gradio UI
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(lines=4, label="Masukkan pesan SMS/WA"),
+    outputs=gr.Textbox(label="Prediksi"),
+    title="Klasifikasi Pesan Spam Bahasa Indonesia",
+    description="Model ini mengklasifikasikan pesan menjadi 6 kategori: hadiah,lowongan/investasi, no spam, program pemerintah/bantuan, promo/penjualan, dan urgensi."
+)
+
+demo.launch()

requirements.txt

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+transformers
+torch
+gradio