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app.py

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+
+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()

le_fartoy.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b3222315d74e25b836dd6fe78bfe9df1b860a99d5f8647ce3b6847881969d6fe
+size 772

requirements.txt

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+xgboost
+scikit-learn
+pandas
+numpy
+gradio

xgb_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2a3d52b75480fbd91dde74b517e408f6a3d086bb6628a490a54eaa785acb7974
+size 1094908