Update app.py

app.py

@@ -2,14 +2,16 @@ import gradio as gr
 import pandas as pd
 import pickle
 
-# Load model and columns
+# ------------------ 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,
@@ -30,26 +32,26 @@ def predict_status(air_temp, proc_temp, speed, torque, tool_wear):
     reasons = []
 
     if air_temp < 150 or air_temp > 500:
-        reasons.append("Air temperature out of optimal range.")
+        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 outside recommended range.")
+        reasons.append("Rotational speed is outside recommended range.")
     if torque < 100 or torque > 800:
-        reasons.append("Torque level may stress machine components.")
+        reasons.append("Torque level may damage machine components.")
     if tool_wear > 300:
-        reasons.append("Tool wear is high — maintenance recommended.")
+        reasons.append("Tool wear is high — maintenance required.")
 
     if prediction == 0:
-        msg = "❌ **Machine Status: NOT OPERATIONAL**\n\n"
-        msg += "\n".join([f"- {r}" for r in reasons]) if reasons else "- Cause not clearly identified."
+        message = "❌ **Machine Status: NOT OPERATIONAL**\n\n"
+        message += "\n".join([f"- {r}" for r in reasons]) if reasons else "- No clear fault detected."
     else:
-        msg = "✅ **Machine Status: OPERATING NORMALLY**\n\nAll parameters are within acceptable ranges."
+        message = "✅ **Machine Status: OPERATING NORMALLY**\n\nAll parameters are within acceptable ranges."
 
-    return msg
+    return message
 
 
-# ---------- Professional CSS ----------
+# ------------------ Professional CSS ------------------
 custom_css = """
 body {
     background-color: #0f172a;
@@ -68,7 +70,6 @@ h1 {
     text-align: center;
     font-size: 32px;
     color: #e5e7eb;
-    margin-bottom: 8px;
 }
 
 .subtitle {
@@ -77,27 +78,23 @@ h1 {
     margin-bottom: 32px;
 }
 
-.block {
+.panel {
     background: #020617;
     border: 1px solid #1e293b;
     border-radius: 14px;
     padding: 24px;
 }
 
-.output-box {
-    background: #020617;
-    border: 1px solid #1e293b;
-    border-radius: 14px;
-    padding: 24px;
-    min-height: 240px;
-    font-size: 16px;
-}
-
 label {
     color: #cbd5f5 !important;
     font-weight: 600;
 }
 
+.output-box {
+    min-height: 240px;
+    font-size: 16px;
+}
+
 footer {
     text-align: center;
     color: #64748b;
@@ -106,36 +103,36 @@ footer {
 }
 """
 
-# ---------- Layout ----------
-with gr.Blocks(css=custom_css) as demo:
+# ------------------ Gradio Layout ------------------
+with gr.Blocks(css=custom_css) as app:
+
     gr.Markdown(
         "<h1>Smart Machine Health Predictor</h1>"
-        "<div class='subtitle'>Predict operational health using real-time machine parameters</div>"
+        "<div class='subtitle'>Industrial Machine Failure Prediction System</div>"
     )
 
     with gr.Row():
         with gr.Column(scale=1):
-            with gr.Group(elem_classes="block"):
+            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)")
 
-                submit = gr.Button("Run Prediction", variant="primary")
+                predict_btn = gr.Button("Run Prediction", variant="primary")
 
         with gr.Column(scale=1):
-            output = gr.Markdown(elem_classes="output-box")
+            output = gr.Markdown(elem_classes="panel output-box")
 
-    submit.click(
+    predict_btn.click(
         predict_status,
         inputs=[air, proc, speed, torque, wear],
         outputs=output
     )
 
-    gr.Markdown(
-        "<footer>Built with Gradio • Machine Health Monitoring System</footer>"
-    )
+    gr.Markdown("<footer>Built with Gradio • Predictive Maintenance Dashboard</footer>")
 
+# ------------------ Launch ------------------
 if __name__ == "__main__":
-    demo.launch()
+    app.launch()