Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+import pickle
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.ensemble import RandomForestRegressor
+
+# Загружаем сохраненную модель и feature names
+def load_model():
+    pipeline = joblib.load('car_price_pipeline.pkl')
+    with open('feature_names.pkl', 'rb') as f:
+        feature_names = pickle.load(f)
+    return pipeline, feature_names
+
+# Функция для предсказания
+def predict_car_price(vehicle_manufacturer, vehicle_category, current_mileage, 
+                     vehicle_year, vehicle_gearbox_type, doors_cnt, wheels, 
+                     vehicle_color, car_leather_interior):
+    
+    # Создаем DataFrame из входных данных
+    input_data = pd.DataFrame({
+        'vehicle_manufacturer': [vehicle_manufacturer],
+        'vehicle_category': [vehicle_category],
+        'current_mileage': [current_mileage],
+        'vehicle_year': [vehicle_year],
+        'vehicle_gearbox_type': [vehicle_gearbox_type],
+        'doors_cnt': [doors_cnt],
+        'wheels': [wheels],
+        'vehicle_color': [vehicle_color],
+        'car_leather_interior': [car_leather_interior]
+    })
+    
+    # Загружаем модель
+    pipeline, feature_names = load_model()
+    
+    # Предсказание
+    try:
+        prediction = pipeline.predict(input_data)[0]
+        return f"Предсказанная цена: ${prediction:,.2f}"
+    except Exception as e:
+        return f"Ошибка предсказания: {str(e)}"
+
+# Создаем интерфейс Gradio
+with gr.Blocks(title="Car Price Predictor") as demo:
+    gr.Markdown("# 🚗 Car Price Prediction Model")
+    gr.Markdown("Введите параметры автомобиля для предсказания цены")
+    
+    with gr.Row():
+        with gr.Column():
+            vehicle_manufacturer = gr.Dropdown(
+                choices=['HYUNDAI', 'TOYOTA', 'BMW', 'MAZDA', 'NISSAN', 'MERCEDES-BENZ', 
+                        'LEXUS', 'VOLKSWAGEN', 'HONDA', 'FORD', 'AUDI', 'KIA'],
+                label="Производитель",
+                value='TOYOTA'
+            )
+            
+            vehicle_category = gr.Dropdown(
+                choices=['Sedan', 'Hatchback', 'Jeep', 'Coupe', 'Minivan', 'Pickup'],
+                label="Категория",
+                value='Sedan'
+            )
+            
+            current_mileage = gr.Number(
+                label="Пробег (км)",
+                value=100000,
+                minimum=0
+            )
+            
+            vehicle_year = gr.Slider(
+                label="Год выпуска",
+                minimum=1990,
+                maximum=2024,
+                value=2015,
+                step=1
+            )
+            
+        with gr.Column():
+            vehicle_gearbox_type = gr.Dropdown(
+                choices=['Automatic', 'Manual', 'Tiptronic'],
+                label="Тип коробки передач",
+                value='Automatic'
+            )
+            
+            doors_cnt = gr.Dropdown(
+                choices=['2/3', '4/5'],
+                label="Количество дверей",
+                value='4/5'
+            )
+            
+            wheels = gr.Dropdown(
+                choices=['Left wheel', 'Right-hand drive'],
+                label="Расположение руля",
+                value='Left wheel'
+            )
+            
+            vehicle_color = gr.Dropdown(
+                choices=['Silver', 'White', 'Grey', 'Black', 'Blue', 'Red'],
+                label="Цвет",
+                value='Black'
+            )
+            
+            car_leather_interior = gr.Radio(
+                choices=[0, 1],
+                label="Кожаный салон",
+                info="0 - Нет, 1 - Да",
+                value=1
+            )
+    
+    predict_btn = gr.Button("Предсказать цену", variant="primary")
+    
+    output = gr.Textbox(
+        label="Результат",
+        interactive=False
+    )
+    
+    predict_btn.click(
+        fn=predict_car_price,
+        inputs=[vehicle_manufacturer, vehicle_category, current_mileage, 
+                vehicle_year, vehicle_gearbox_type, doors_cnt, wheels, 
+                vehicle_color, car_leather_interior],
+        outputs=output
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown("### Примеры параметров:")
+    gr.Markdown("- **TOYOTA, Sedan, 100,000 km, 2015, Automatic** → ~$5,000")
+    gr.Markdown("- **BMW, Sedan, 50,000 km, 2018, Automatic** → ~$15,000")
+
+if __name__ == "__main__":
+    demo.launch()