Update app.py

app.py

@@ -2,32 +2,14 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 
 # Ganti dengan model kamu di Hugging Face
-MODEL_ID = "Ranti0603/job_classifier_model_v2"
+pipe = pipeline("text-classification", model="Ranti0603/job_classifier_model_v2")
 
 def respond(message, history, hf_token: gr.OAuthToken):
-    """
-    Chatbot klasifikasi pekerjaan:
-    Input: Deskripsi pekerjaan
-    Output: Prediksi label + confidence
-    """
-    client = InferenceClient(token=hf_token.token, model=MODEL_ID)
-
-    # Panggil model klasifikasi
-    result = client.text_classification(message)
-
-    # Ambil prediksi terbaik
-    best = max(result, key=lambda x: x["score"])
-    label = best["label"]
-    score = round(best["score"] * 100, 2)
-
-    # Mapping label
-    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}%"
-    return reply
+    result = pipe(message, truncation=True)[0]
+    label = result["label"]
+    score = round(result["score"] * 100, 2)
+    response = f"Pekerjaan ini dikategorikan sebagai **{label}** dengan confidence {score}%"
+    return response
 
 # === UI ===
 with gr.Blocks() as demo:
@@ -35,9 +17,7 @@ with gr.Blocks() as demo:
     gr.Markdown("Masukkan deskripsi pekerjaan, sistem akan mengklasifikasikan apakah pekerjaan tersebut termasuk **Non-TIK (0)** atau **TIK (1)**.")
 
     with gr.Row():
-        with gr.Column(scale=1):
-            gr.LoginButton()  # login HuggingFace
-        with gr.Column(scale=5):
+         with gr.Column(scale=5):
             chatbot = gr.ChatInterface(
                 respond,
                 type="messages",