app.py

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

# Load model Random Forest
model = joblib.load("model_rf.joblib")

def prediksi_harga(brand, model_input, year, mileage, fuel_type,
                   transmission, body_type, engine_capacity, seller_type):
    input_df = pd.DataFrame([{
        'brand': brand,
        'model': model_input,
        'year': int(year),
        'mileage': float(mileage),
        'fuel_type': fuel_type,
        'transmission': transmission,
        'body_type': body_type,
        'engine_capacity': float(engine_capacity),
        'seller_type': seller_type
    }])
    try:
        pred = model.predict(input_df)[0]
        pred_rupiah = pred * 1_000_000
        return f"Perkiraan Harga: Rp {pred_rupiah:,.0f}".replace(",", ".")
    except Exception as e:
        return f"Gagal prediksi: {str(e)}"

def get_unique(col):
    return sorted(df[col].dropna().unique().tolist())

with gr.Blocks() as demo:
    gr.Markdown("## 🚗 Prediksi Harga Mobil Bekas (Random Forest)")
    gr.Markdown("Isi informasi mobil untuk memprediksi harga jual.")
    with gr.Row():
        with gr.Column():
            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)
            mileage = gr.Number(label="Jarak Tempuh (km)", value=50000)
            fuel_type = gr.Dropdown(choices=get_unique('fuel_type'), label="Jenis Bahan Bakar")
            transmission = gr.Dropdown(choices=get_unique('transmission'), label="Transmisi")
            body_type = gr.Dropdown(choices=get_unique('body_type'), label="Tipe Body")
            engine_capacity = gr.Number(label="Kapasitas Mesin (cc)", value=1500)
            seller_type = gr.Dropdown(choices=get_unique('seller_type'), label="Tipe Penjual")
            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=[
        brand, model_input, year, mileage, fuel_type,
        transmission, body_type, engine_capacity, seller_type
    ], outputs=output)

demo.launch()