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| import gradio as gr | |
| import pandas as pd | |
| import pickle | |
| # ------------------ Load Model ------------------ | |
| with open("logistic_regression_model.pkl", "rb") as f: | |
| model = pickle.load(f) | |
| with open("feature_columns.pkl", "rb") as f: | |
| feature_columns = pickle.load(f) | |
| # ------------------ Prediction Function ------------------ | |
| def predict_status(air_temp, proc_temp, speed, torque, tool_wear): | |
| sample_input = { | |
| "Air temperature [K]": air_temp, | |
| "Process temperature [K]": proc_temp, | |
| "Rotational speed [rpm]": speed, | |
| "Torque [Nm]": torque, | |
| "Tool wear [min]": tool_wear | |
| } | |
| df = pd.DataFrame([sample_input]) | |
| for col in feature_columns: | |
| if col not in df.columns: | |
| df[col] = 0 | |
| df = df[feature_columns] | |
| prediction = model.predict(df)[0] | |
| reasons = [] | |
| if air_temp < 150 or air_temp > 500: | |
| reasons.append("Air temperature outside optimal range (150–500 K).") | |
| if proc_temp < 150 or proc_temp > 500: | |
| reasons.append("Process temperature not within safe limits.") | |
| if speed < 500 or speed > 1500: | |
| reasons.append("Rotational speed is outside recommended range.") | |
| if torque < 100 or torque > 800: | |
| reasons.append("Torque level may damage machine components.") | |
| if tool_wear > 300: | |
| reasons.append("Tool wear is high — maintenance required.") | |
| if prediction == 0: | |
| message = "❌ **Machine Status: NOT OPERATIONAL**\n\n" | |
| message += "\n".join([f"- {r}" for r in reasons]) if reasons else "- No clear fault detected." | |
| else: | |
| message = "✅ **Machine Status: OPERATING NORMALLY**\n\nAll parameters are within acceptable ranges." | |
| return message | |
| # ------------------ Professional CSS ------------------ | |
| custom_css = """ | |
| body { | |
| background-color: #0f172a; | |
| } | |
| .gradio-container { | |
| max-width: 1100px; | |
| margin: auto; | |
| padding: 32px; | |
| background: #020617; | |
| border-radius: 16px; | |
| box-shadow: 0 20px 40px rgba(0,0,0,0.4); | |
| } | |
| h1 { | |
| text-align: center; | |
| font-size: 32px; | |
| color: #e5e7eb; | |
| } | |
| .subtitle { | |
| text-align: center; | |
| color: #94a3b8; | |
| margin-bottom: 32px; | |
| } | |
| .panel { | |
| background: #020617; | |
| border: 1px solid #1e293b; | |
| border-radius: 14px; | |
| padding: 24px; | |
| } | |
| label { | |
| color: #cbd5f5 !important; | |
| font-weight: 600; | |
| } | |
| .output-box { | |
| min-height: 240px; | |
| font-size: 16px; | |
| } | |
| footer { | |
| text-align: center; | |
| color: #64748b; | |
| font-size: 13px; | |
| margin-top: 24px; | |
| } | |
| """ | |
| # ------------------ Gradio Layout ------------------ | |
| with gr.Blocks(css=custom_css) as app: | |
| gr.Markdown( | |
| "<h1>Smart Machine Health Predictor</h1>" | |
| "<div class='subtitle'>Industrial Machine Failure Prediction System</div>" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| with gr.Group(elem_classes="panel"): | |
| air = gr.Slider(0, 1000, value=300, label="Air Temperature (K)") | |
| proc = gr.Slider(0, 1000, value=300, label="Process Temperature (K)") | |
| speed = gr.Slider(0, 2000, value=1000, label="Rotational Speed (rpm)") | |
| torque = gr.Slider(0, 1000, value=500, label="Torque (Nm)") | |
| wear = gr.Slider(0, 1000, value=100, label="Tool Wear (min)") | |
| predict_btn = gr.Button("Run Prediction", variant="primary") | |
| with gr.Column(scale=1): | |
| output = gr.Markdown(elem_classes="panel output-box") | |
| predict_btn.click( | |
| predict_status, | |
| inputs=[air, proc, speed, torque, wear], | |
| outputs=output | |
| ) | |
| gr.Markdown("<footer>Built with Gradio • Predictive Maintenance Dashboard</footer>") | |
| # ------------------ Launch ------------------ | |
| if __name__ == "__main__": | |
| app.launch() | |