import pandas as pd
import joblib
import gradio as gr

# Modeli yükle
try:
    pipe = joblib.load('car_price_model.pkl')
except FileNotFoundError:
    print("HATA: 'car_price_model.pkl' dosyası bulunamadı.")
    pipe = None
except Exception as e:
    print(f"Model yüklenirken bir hata oluştu: {e}")
    pipe = None

# Veri yükle
try:
    df = pd.read_excel('cars.xls')
    make_options = sorted(df['Make'].dropna().unique().tolist())
    cylinder_options = sorted(df['Cylinder'].dropna().unique().tolist())
    doors_options = sorted(df['Doors'].dropna().unique().tolist())
except FileNotFoundError:
    print("HATA: 'cars.xls' dosyası bulunamadı.")
    df = pd.DataFrame({
        'Make': [], 'Model': [], 'Trim': [], 'Type': [],
        'Cylinder': [], 'Doors': []
    })
    make_options, cylinder_options, doors_options = [], [], []
except Exception as e:
    print(f"Veri yüklenirken hata oluştu: {e}")
    df = pd.DataFrame({
        'Make': [], 'Model': [], 'Trim': [], 'Type': [],
        'Cylinder': [], 'Doors': []
    })
    make_options, cylinder_options, doors_options = [], [], []

# Tahmin fonksiyonu
def predict_price(make, model, trim, mileage, car_type, cylinder, liter, doors, cruise, sound, leather):
    if pipe is None:
        return "HATA: Model yüklenemedi."
    try:
        input_data = pd.DataFrame({
            'Make': [make],
            'Model': [model],
            'Trim': [trim],
            'Mileage': [mileage],
            'Type': [car_type],
            'Cylinder': [cylinder],
            'Liter': [liter],
            'Doors': [doors],
            'Cruise': [cruise],
            'Sound': [sound],
            'Leather': [leather]
        })
        prediction = pipe.predict(input_data)[0]
        return f"Tahmini Fiyat: ${int(prediction):,}"
    except Exception as e:
        return f"Tahmin sırasında hata oluştu: {e}"

# Dinamik dropdown güncellemeleri
def update_models(selected_make):
    if pd.isna(selected_make) or not selected_make:
        return gr.Dropdown(choices=[], label="🚘 Model", interactive=True)
    models = sorted(df[df['Make'] == selected_make]['Model'].dropna().unique().tolist())
    return gr.Dropdown(choices=models, label="🚘 Model", interactive=True, value=models[0] if models else None)

def update_trims(selected_make, selected_model):
    if not selected_make or not selected_model:
        return gr.Dropdown(choices=[], label="🎯 Donanım (Trim)", interactive=True)
    trims = sorted(df[(df['Make'] == selected_make) & (df['Model'] == selected_model)]['Trim'].dropna().unique().tolist())
    return gr.Dropdown(choices=trims, label="🎯 Donanım (Trim)", interactive=True, value=trims[0] if trims else None)

def update_types(selected_make, selected_model, selected_trim):
    if not selected_make or not selected_model or not selected_trim:
        return gr.Dropdown(choices=[], label="🚗 Araç Tipi", interactive=True)
    types = sorted(df[(df['Make'] == selected_make) &
                      (df['Model'] == selected_model) &
                      (df['Trim'] == selected_trim)]['Type'].dropna().unique().tolist())
    return gr.Dropdown(choices=types, label="🚗 Araç Tipi", interactive=True, value=types[0] if types else None)

# Gradio arayüzü (tema: mor)
with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple")) as demo:
    gr.Markdown("<h1 style='text-align:center; color:#9b59b6;'>🚗 <b>Araba Fiyat Tahmin Uygulaması</b></h1>")
    gr.Markdown("<p style='text-align:center; font-size:16px;'>🧠 Makine Öğrenmesi tabanlı fiyat tahmini yapmak için bilgileri doldurun.</p>")

    with gr.Accordion("📋 Tahmin Girdileri", open=True):
        with gr.Row():
            make_dd = gr.Dropdown(choices=make_options, label="🔰 Marka", interactive=True)
            model_dd = gr.Dropdown(choices=[], label="🚘 Model", interactive=True)
            trim_dd = gr.Dropdown(choices=[], label="🎯 Donanım (Trim)", interactive=True)

        with gr.Row():
            mileage_num = gr.Number(label="🛣️ Kilometre", minimum=200, maximum=600000, step=1000, value=50000)
            type_dd = gr.Dropdown(choices=[], label="🚗 Araç Tipi", interactive=True)
            cylinder_dd = gr.Dropdown(choices=cylinder_options, label="⚙️ Silindir", interactive=True)

        with gr.Row():
            liter_num = gr.Number(label="🛢️ Motor Hacmi (Litre)", minimum=0.8, maximum=8.0, step=0.1, value=2.0)
            doors_dd = gr.Dropdown(choices=doors_options, label="🚪 Kapı Sayısı", interactive=True)
            cruise_rb = gr.Radio(choices=[True, False], label="🚀 Hız Sabitleme", value=True, type="value")

        with gr.Row():
            sound_rb = gr.Radio(choices=[True, False], label="🔊 Gelişmiş Ses Sistemi", value=True, type="value")
            leather_rb = gr.Radio(choices=[True, False], label="🛋️ Deri Koltuk", value=False, type="value")

    with gr.Row():
        predict_button = gr.Button("💸 Fiyat Tahmini Yap", variant="primary")
    with gr.Row():
        output_text = gr.Textbox(label="📈 Tahmini Sonuç", lines=1)

    # Dropdown güncellemeleri
    make_dd.change(fn=update_models, inputs=make_dd, outputs=model_dd)
    make_dd.change(fn=lambda: (gr.Dropdown(choices=[], value=None), gr.Dropdown(choices=[], value=None)), outputs=[trim_dd, type_dd])
    model_dd.change(fn=update_trims, inputs=[make_dd, model_dd], outputs=trim_dd)
    model_dd.change(fn=lambda: gr.Dropdown(choices=[], value=None), outputs=type_dd)
    trim_dd.change(fn=update_types, inputs=[make_dd, model_dd, trim_dd], outputs=type_dd)

    predict_button.click(
        fn=predict_price,
        inputs=[make_dd, model_dd, trim_dd, mileage_num, type_dd, cylinder_dd, liter_num, doors_dd, cruise_rb, sound_rb, leather_rb],
        outputs=output_text
    )

    gr.Markdown("<hr>")
    gr.Markdown("""
    <div style='background-color:#f7f0ff; padding:15px; border-radius:10px; font-size:15px'>
    <h3>📌 <b>Kullanım Notları</b></h3>
    <ul>
        <li>✅ Tüm alanları eksiksiz ve doğru doldurun.</li>
        <li>📍 <b>Marka</b> → <b>Model</b> → <b>Trim</b> → <b>Tip</b> sıralamasına göre seçim yapın.</li>
        <li>🔘 Özellikler için <b>Var/Yok</b> seçimi yapın.</li>
    </ul>
    </div>
    """)

# Uygulama çalıştır
if __name__ == '__main__':
    if pipe is None or df.empty:
        print("Model veya veri yüklenemedi. Uygulama başlatılamıyor.")
    else:
        demo.launch()