Update app.py

app.py

@@ -1,46 +1,49 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# ID model di Hugging Face Hub
+# ID model klasifikasi yang sudah kamu upload di Hugging Face Hub
 MODEL_ID = "Ranti0603/job-classifier-xlm-roberta-v2"
 
 def respond(message, history, hf_token: gr.OAuthToken):
     """
-    Chatbot untuk klasifikasi pekerjaan: Non-TIK (0) atau TIK (1)
+    Chatbot klasifikasi pekerjaan:
+    Input: Deskripsi pekerjaan
+    Output: Prediksi label + confidence
     """
     client = InferenceClient(token=hf_token.token, model=MODEL_ID)
 
-    # Panggil model untuk klasifikasi
+    # Panggil model untuk klasifikasi teks
     result = client.text_classification(message)
 
-    # Ambil hasil terbaik
+    # Ambil hasil prediksi
     best = max(result, key=lambda x: x["score"])
     label = best["label"]
     score = round(best["score"] * 100, 2)
 
-    # Mapping label
+    # Mapping label ke format readable
     if label in ["LABEL_0", "0", "Non-TIK"]:
         label_out = "Non-TIK (0)"
     else:
         label_out = "TIK (1)"
 
-    reply = f"Prediksi: **{label_out}**\n✅ Confidence: {score}%"
+    reply = f"Prediksi: **{label_out}**\n Confidence: {score}%"
     return reply
 
-# === Gradio Chatbot UI ===
+# === Gradio Chatbot ===
 chatbot = gr.ChatInterface(
-    fn=respond,   # fungsi dipanggil
-    chatbot=gr.Chatbot(height=400),
+    fn=respond,
     type="messages",
+    chatbot=gr.Chatbot(height=500),
+    title="Job Classification Chatbot",
+    description="Masukkan deskripsi pekerjaan, sistem akan mengklasifikasikan apakah pekerjaan tersebut termasuk **Non-TIK (0)** atau **TIK (1)**."
 )
 
 with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-        gr.Markdown("### Login ke Hugging Face untuk pakai model")
-
-    gr.Markdown("## 🤖 Job Classification Chatbot (Non-TIK vs TIK)")
-    chatbot.render()
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.LoginButton()  # login HF
+        with gr.Column(scale=5):
+            chatbot.render()
 
 if __name__ == "__main__":
     demo.launch()