Spaces:
Running on CPU Upgrade
Running on CPU Upgrade
| import gradio as gr | |
| import pickle | |
| import numpy as np | |
| import pandas as pd | |
| # Load the saved model and LabelEncoder | |
| with open('xgb_model.pkl', 'rb') as f: | |
| model = pickle.load(f) | |
| with open('le_fartoy.pkl', 'rb') as f: | |
| le_fartoy = pickle.load(f) | |
| # Define the prediction function | |
| def predict_accidents(fartoy_gruppe, year): | |
| try: | |
| # Encode the selected vessel group | |
| fartoy_encoded = le_fartoy.transform([fartoy_gruppe])[0] | |
| # Features must be in the exact order: ['FartøyGruppe', 'Ulykke_Year'] | |
| features = np.array([[fartoy_encoded, year]]) | |
| # Get model prediction | |
| prediction = model.predict(features)[0] | |
| # Round and ensure non-negative | |
| predicted_count = max(0, int(round(prediction))) | |
| return f"Predicted Number of Accidents: {predicted_count}" | |
| except Exception as e: | |
| return f"Error during prediction: {str(e)}" | |
| # Get the list of vessel groups for the dropdown | |
| vessel_groups = sorted(list(le_fartoy.classes_)) | |
| # Build Gradio UI with custom inputs (Dropdown and Slider) | |
| interface = gr.Interface( | |
| fn=predict_accidents, | |
| inputs=[ | |
| gr.Dropdown(choices=vessel_groups, label="FartøyGruppe (Vessel Group)", value=vessel_groups[0]), | |
| gr.Slider(minimum=1981, maximum=2032, step=1, label="Ulykke_Year (Accident Year)", value=2024) | |
| ], | |
| outputs=gr.Textbox(label="Prediction Output"), | |
| title="Maritime Accident Predictor (XGBoost)", | |
| description="Select a vessel category and year to predict the annual aggregated number of accidents using the optimized XGBoost Regressor model.", | |
| theme="soft" | |
| ) | |
| if __name__ == '__main__': | |
| interface.launch() | |