Update app.py

app.py

@@ -2,24 +2,14 @@ import gradio as gr
 import pandas as pd
 import joblib
 
+# Load dataset referensi
 df = pd.read_csv('data_summary.csv')  # Pastikan file ada di direktori repo HF
 
-model_paths = {
-    "Random Forest": "model_rf.joblib",
-    "Gradient Boosting": "model_gb.joblib",
-    "SVR": "model_svr.joblib",
-    "Stack RF + GB": "stacking_rf_plus_gb.joblib",
-    "Stack RF + SVR": "stacking_rf_plus_svr.joblib",
-    "Stack GB + SVR": "stacking_gb_plus_svr.joblib",
-    "Stack RF + GB + SVR": "stacking_rf_plus_gb_plus_svr.joblib"
-}
+# Load model Random Forest
+model = joblib.load("model_rf.joblib")
 
-def prediksi_harga(model_choice, brand, model_input, year, mileage, fuel_type,
+def prediksi_harga(brand, model_input, year, mileage, fuel_type,
                    transmission, body_type, engine_capacity, seller_type):
-    try:
-        model = joblib.load(model_paths[model_choice])
-    except Exception as e:
-        return f"Gagal load model: {str(e)}"
     input_df = pd.DataFrame([{
         'brand': brand,
         'model': model_input,
@@ -42,11 +32,10 @@ def get_unique(col):
     return sorted(df[col].dropna().unique().tolist())
 
 with gr.Blocks() as demo:
-    gr.Markdown("## 🚗 Prediksi Harga Mobil Bekas")
-    gr.Markdown("Pilih model machine learning dan isi informasi mobil untuk memprediksi harga jual.")
+    gr.Markdown("## 🚗 Prediksi Harga Mobil Bekas (Random Forest)")
+    gr.Markdown("Isi informasi mobil untuk memprediksi harga jual.")
     with gr.Row():
         with gr.Column():
-            model_choice = gr.Dropdown(choices=list(model_paths.keys()), label="Pilih Model", value="Random Forest")
             brand = gr.Dropdown(choices=get_unique('brand'), label="Brand")
             model_input = gr.Dropdown(choices=get_unique('model'), label="Model")
             year = gr.Number(label="Tahun", value=2020, precision=0)
@@ -59,7 +48,9 @@ with gr.Blocks() as demo:
             predict_button = gr.Button("🔍 Prediksi Harga")
         with gr.Column():
             output = gr.Textbox(label="Hasil Prediksi Harga", lines=2)
-    predict_button.click(fn=prediksi_harga, inputs=[model_choice, brand, model_input, year, mileage, fuel_type,
-                transmission, body_type, engine_capacity, seller_type], outputs=output)
+    predict_button.click(fn=prediksi_harga, inputs=[
+        brand, model_input, year, mileage, fuel_type,
+        transmission, body_type, engine_capacity, seller_type
+    ], outputs=output)
 
-demo.launch()
+demo.launch()