import pandas as pd import joblib import gradio as gr # Load the pkl model try: pipe = joblib.load('car_price_model.pkl') except FileNotFoundError: print("ERROR: 'car_price_model.pkl' file not found. Please ensure the file is in the correct path.") pipe = None except Exception as e: print(f"An error occurred while loading the model: {e}") pipe = None # Load data try: df = pd.read_excel('cars.xls') # Get unique and non-NaN values for Gradio dropdowns, then sort them 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("ERROR: 'cars.xls' file not found. Please ensure the file is in the correct path.") # Create a sample or empty DataFrame to prevent the application from crashing df = pd.DataFrame({ 'Make': [], 'Model': [], 'Trim': [], 'Type': [], 'Cylinder': [], 'Doors': [] }) make_options = [] cylinder_options = [] doors_options = [] except Exception as e: print(f"An error occurred while loading the data: {e}") df = pd.DataFrame({ 'Make': [], 'Model': [], 'Trim': [], 'Type': [], 'Cylinder': [], 'Doors': [] }) make_options = [] cylinder_options = [] doors_options = [] def predict_price(make, model, trim, mileage, car_type, cylinder, liter, doors, cruise, sound, leather): if pipe is None: return "ERROR: Model could not be loaded, prediction cannot be made." try: # Convert user input data to DataFrame 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"Estimated Price: ${int(prediction):,}" # Format the number except Exception as e: return f"An error occurred during prediction: {e}" # Function to dynamically update model options def update_models(selected_make): if pd.isna(selected_make) or not selected_make: return gr.Dropdown(choices=[], label="Model", interactive=True, value=None) models = sorted(df[df['Make'] == selected_make]['Model'].dropna().unique().tolist()) return gr.Dropdown(choices=models, label="Model", interactive=True, value=None if not models else models[0]) # Function to dynamically update trim options def update_trims(selected_make, selected_model): if pd.isna(selected_make) or not selected_make or pd.isna(selected_model) or not selected_model: return gr.Dropdown(choices=[], label="Trim", interactive=True, value=None) trims = sorted(df[(df['Make'] == selected_make) & (df['Model'] == selected_model)]['Trim'].dropna().unique().tolist()) return gr.Dropdown(choices=trims, label="Trim", interactive=True, value=None if not trims else trims[0]) # Function to dynamically update car type options def update_types(selected_make, selected_model, selected_trim): if pd.isna(selected_make) or not selected_make or \ pd.isna(selected_model) or not selected_model or \ pd.isna(selected_trim) or not selected_trim: return gr.Dropdown(choices=[], label="Car Type", interactive=True, value=None) 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="Car Type", interactive=True, value=None if not types else types[0]) # Gradio Interface with gr.Blocks(theme=gr.themes.Monochrome(), title="Car Price Predictor") as demo: gr.Markdown(""" # 🚗 **Luxurious Car Price Predictor** ### *Predict the market value of your dream car with advanced AI!* """) with gr.Row(): with gr.Column(scale=1): gr.Markdown("## 📋 Car Specifications") make_dd = gr.Dropdown(choices=make_options, label="Make", interactive=True, info="Select the car's manufacturer.") model_dd = gr.Dropdown(choices=[], label="Model", interactive=True, info="Choose the specific model.") trim_dd = gr.Dropdown(choices=[], label="Trim", interactive=True, info="Specify the car's trim level.") type_dd = gr.Dropdown(choices=[], label="Car Type", interactive=True, info="What type of car is it (e.g., Sedan, SUV)?") with gr.Column(scale=1): gr.Markdown("## ⚙️ Performance & Features") mileage_num = gr.Slider(label="Mileage (km)", minimum=0, maximum=600000, step=1000, value=50000, info="Enter the total kilometers driven.") cylinder_dd = gr.Dropdown(choices=cylinder_options, label="Cylinders", interactive=True, info="Number of engine cylinders.") liter_num = gr.Slider(label="Engine Volume (Liters)", minimum=0.8, maximum=8.0, step=0.1, value=2.0, info="Engine displacement in liters.") doors_dd = gr.Dropdown(choices=doors_options, label="Number of Doors", interactive=True, info="How many doors does the car have?") with gr.Row(): with gr.Column(scale=1): gr.Markdown("## ✨ Comfort & Technology") cruise_rb = gr.Radio(choices=[True, False], label="Cruise Control", value=True, type="value", info="Does the car have cruise control?") sound_rb = gr.Radio(choices=[True, False], label="Premium Sound System", value=True, type="value", info="Is there an upgraded sound system?") leather_rb = gr.Radio(choices=[True, False], label="Leather Seats", value=False, type="value", info="Are the seats upholstered in leather?") with gr.Row(): predict_button = gr.Button("💰 **Get Estimated Price** 💰", size="lg", variant="primary") with gr.Row(): output_text = gr.Textbox(label="Prediction Result", interactive=False, show_copy_button=True) # Event listeners for dynamic dropdown updates 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("---") gr.Markdown(""" ### 💡 **Usage Notes:** * Please fill in all fields accurately for the best prediction. * **Make** selection updates **Model** options. * **Model** selection updates **Trim** options. * **Make**, **Model**, and **Trim** selections update **Car Type** options. * For 'Cruise Control', 'Premium Sound System', and 'Leather Seats', select 'True' (Yes) or 'False' (No). * This predictor uses an AI model trained on specific car data. Predictions are estimates and may vary from actual market prices. """) gr.Markdown("---") gr.Markdown("

Developed with ❤️ by @drmurataltun

") if __name__ == '__main__': if pipe is None or df.empty: print("Gradio interface cannot be launched because the model or data could not be loaded.") print("Please check the existence and integrity of 'car_price_model.pkl' and 'cars.xls' files.") else: demo.launch()