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| import pandas as pd | |
| import shap | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import joblib | |
| import gradio as gr | |
| # Load the model | |
| try: | |
| loaded_model = joblib.load('machine_failure_prediction_model.joblib') | |
| print("Model loaded successfully for Gradio.") | |
| except FileNotFoundError: | |
| print("Error: 'machine_failure_prediction_model.joblib' not found. Ensure the model file is in the correct directory.") | |
| loaded_model = None | |
| def predict_failure_with_shap(Type, air_temp, process_temp, rotational_speed, torque, tool_wear): | |
| if loaded_model is None: | |
| return "Error: Model not loaded.", "", None | |
| input_data = pd.DataFrame({ | |
| 'Type': [Type], | |
| 'Air temperature [K]': [air_temp], | |
| 'Process temperature [K]': [process_temp], | |
| 'Rotational speed [rpm]': [rotational_speed], | |
| 'Torque [Nm]': [torque], | |
| 'Tool wear [min]': [tool_wear] | |
| }) | |
| predicted_failure = loaded_model.predict(input_data)[0] | |
| predicted_proba = loaded_model.predict_proba(input_data)[0] | |
| prediction_label = f"Predicted Failure: {'Failure' if predicted_failure == 1 else 'No Failure'}" | |
| probabilities_label = f"Probabilities (No Failure, Failure): {predicted_proba[0]:.4f}, {predicted_proba[1]:.4f}" | |
| preprocessor = loaded_model.named_steps['preprocessor'] | |
| classifier = loaded_model.named_steps['classifier'] | |
| X_transformed = preprocessor.transform(input_data) | |
| explainer = shap.TreeExplainer(classifier) | |
| shap_values = explainer.shap_values(X_transformed) | |
| feature_names = preprocessor.get_feature_names_out() | |
| if isinstance(shap_values, list): | |
| shap_val = shap_values[1][0] | |
| base_val = explainer.expected_value[1] | |
| else: | |
| shap_val = shap_values[0, :] if shap_values.ndim == 2 else shap_values[0, :, 1] | |
| base_val = explainer.expected_value if not isinstance(explainer.expected_value, (list, tuple, np.ndarray)) else explainer.expected_value[1] | |
| fig = plt.figure() | |
| shap.waterfall_plot( | |
| shap.Explanation( | |
| values=shap_val, | |
| base_values=base_val, | |
| data=X_transformed[0], | |
| feature_names=feature_names | |
| ), show=False | |
| ) | |
| plt.title("SHAP Waterfall Plot for Failure Prediction") | |
| plt.tight_layout() | |
| return prediction_label, probabilities_label, fig | |
| # Define Gradio interface OUTSIDE the function | |
| iface = gr.Interface( | |
| fn=predict_failure_with_shap, | |
| inputs=[ | |
| gr.Dropdown(choices=['L', 'M', 'H'], label='Machine Type'), | |
| gr.Number(label='Air temperature [K]', step=0.1), | |
| gr.Number(label='Process temperature [K]', step=0.1), | |
| gr.Number(label='Rotational speed [rpm]', step=1), | |
| gr.Number(label='Torque [Nm]', step=0.1), | |
| gr.Number(label='Tool wear [min]', step=1) | |
| ], | |
| outputs=[ | |
| gr.Label(label='Predicted Failure (0=No Failure, 1=Failure)'), | |
| gr.Label(label='Prediction Probabilities'), | |
| gr.Plot(label='SHAP Waterfall Plot') | |
| ], | |
| title="Machine Failure Prediction with SHAP Analysis", | |
| description="Enter the machine parameters to predict failure and see the SHAP analysis.", | |
| flagging_mode='never' | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch(server_name="0.0.0.0", server_port=7860) | |