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| import gradio as gr | |
| import pandas as pd | |
| import pickle | |
| # Load the saved model and feature columns | |
| with open('logistic_regression_model.pkl', 'rb') as model_file: | |
| model = pickle.load(model_file) | |
| with open('feature_columns.pkl', 'rb') as col_file: | |
| feature_columns = pickle.load(col_file) | |
| def predict_status(air_temp, proc_temp, speed, torque, tool_wear): | |
| # Prepare the input data | |
| sample_input = { | |
| "Air temperature [K]": air_temp, | |
| "Process temperature [K]": proc_temp, | |
| "Rotational speed [rpm]": speed, | |
| "Torque [Nm]": torque, | |
| "Tool wear [min]": tool_wear | |
| } | |
| # Convert the sample input to a DataFrame | |
| sample_df = pd.DataFrame([sample_input]) | |
| # Ensure all columns from training data are present in the sample DataFrame | |
| for col in feature_columns: | |
| if col not in sample_df.columns: | |
| sample_df[col] = 0 # Add missing columns and fill with 0 | |
| # Reorder columns to match the order in the training data | |
| sample_df = sample_df[feature_columns] | |
| # Make a prediction | |
| prediction = model.predict(sample_df)[0] | |
| # Initialize the reason list | |
| reasons = [] | |
| # Define acceptable ranges for each feature with user-friendly feedback | |
| acceptable_ranges = { | |
| "Air temperature [K]": (150, 500), | |
| "Process temperature [K]": (150, 500), | |
| "Rotational speed [rpm]": (500, 1500), | |
| "Torque [Nm]": (100, 800), | |
| "Tool wear [min]": (50, 300) | |
| } | |
| # User-friendly reasons for each feature | |
| if air_temp < 150: | |
| reasons.append("• The air temperature is too low, which might be causing the machine to underperform.") | |
| elif air_temp > 500: | |
| reasons.append("• The air temperature is too high, which could be causing the machine to overheat.") | |
| if proc_temp < 150: | |
| reasons.append("• The process temperature is below the normal range, possibly affecting machine efficiency.") | |
| elif proc_temp > 500: | |
| reasons.append("• The process temperature is too high, which might lead to overheating.") | |
| if speed < 500: | |
| reasons.append("• The rotational speed is too low, causing the machine to operate inefficiently.") | |
| elif speed > 1500: | |
| reasons.append("• The rotational speed is too high, which might be causing excessive strain on the machine.") | |
| if torque < 100: | |
| reasons.append("• The torque is too low, which may indicate that the machine is not operating at its full capacity.") | |
| elif torque > 800: | |
| reasons.append("• The torque is too high, possibly putting excessive stress on the machine's components.") | |
| if tool_wear < 50: | |
| reasons.append("• The tool wear is minimal, but routine checks are still recommended to prevent future issues.") | |
| elif tool_wear > 300: | |
| reasons.append("• The tool wear is high, and the tool may need to be replaced soon to maintain optimal performance.") | |
| # Generate the final result message | |
| if prediction == 0: | |
| result_message = "🔴 Machine is NOT working.\n" # First line for machine status | |
| if reasons: | |
| result_message += "\n".join(reasons) # Second row onward for reasons with bullet points | |
| else: | |
| result_message += "No specific reason identified." # In case no issues found | |
| else: | |
| result_message = "🟢 Machine is working fine." | |
| return result_message | |
| # Define custom CSS for professional UI/UX | |
| custom_css = """ | |
| body { | |
| background-color: #f0f2f5; | |
| font-family: Arial, sans-serif; | |
| } | |
| .gradio-container { | |
| max-width: 800px; | |
| margin: auto; | |
| padding: 40px; | |
| border-radius: 12px; | |
| box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1); | |
| background-color: #fff; | |
| border: 1px solid #ddd; | |
| } | |
| .gradio-title { | |
| font-size: 28px; | |
| font-weight: bold; | |
| color: #333; | |
| text-align: center; | |
| margin-bottom: 20px; | |
| } | |
| .gradio-description { | |
| font-size: 16px; | |
| color: #555; | |
| text-align: center; | |
| margin-bottom: 40px; | |
| } | |
| .gradio-footer { | |
| font-size: 14px; | |
| color: #777; | |
| text-align: center; | |
| margin-top: 20px; | |
| } | |
| .gradio-input label { | |
| font-weight: bold; | |
| color: #444; | |
| } | |
| footer { | |
| font-size: 12px; | |
| color: #555; | |
| text-align: center; | |
| padding: 10px; | |
| border-top: 1px solid #e0e0e0; | |
| margin-top: 40px; | |
| } | |
| footer a { | |
| color: #555; | |
| text-decoration: none; | |
| } | |
| footer a:hover { | |
| text-decoration: underline; | |
| } | |
| """ | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=predict_status, | |
| inputs=[ | |
| gr.Slider(label="Air Temperature (K)", minimum=0, maximum=1000, step=0.01, value=300), | |
| gr.Slider(label="Process Temperature (K)", minimum=0, maximum=1000, step=0.01, value=300), | |
| gr.Slider(label="Rotational Speed (rpm)", minimum=0, maximum=2000, step=1, value=1000), | |
| gr.Slider(label="Torque (Nm)", minimum=0, maximum=1000, step=0.1, value=500), | |
| gr.Slider(label="Tool Wear (min)", minimum=0, maximum=1000, step=1, value=100), | |
| ], | |
| outputs="text", | |
| title="Smart Machine Health Predictor", | |
| description="Provide machine parameters to predict whether the machine is working or not.", | |
| css=custom_css, # Apply custom CSS | |
| ) | |
| # Launch the Gradio app | |
| if __name__ == "__main__": | |
| iface.launch() | |