Update app.py

app.py

@@ -2,15 +2,14 @@ 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)
+# Load model and columns
+with open("logistic_regression_model.pkl", "rb") as f:
+    model = pickle.load(f)
 
-with open('feature_columns.pkl', 'rb') as col_file:
-    feature_columns = pickle.load(col_file)
+with open("feature_columns.pkl", "rb") as f:
+    feature_columns = pickle.load(f)
 
 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,
@@ -18,142 +17,125 @@ def predict_status(air_temp, proc_temp, speed, torque, tool_wear):
         "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
+
+    df = pd.DataFrame([sample_input])
+
     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]
+        if col not in df.columns:
+            df[col] = 0
 
-    # Make a prediction
-    prediction = model.predict(sample_df)[0]
+    df = df[feature_columns]
+    prediction = model.predict(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)
-    }
+    if air_temp < 150 or air_temp > 500:
+        reasons.append("Air temperature out of optimal range.")
+    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.")
+    if torque < 100 or torque > 800:
+        reasons.append("Torque level may stress machine components.")
+    if tool_wear > 300:
+        reasons.append("Tool wear is high — maintenance recommended.")
 
-    # 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
+        msg = "❌ **Machine Status: NOT OPERATIONAL**\n\n"
+        msg += "\n".join([f"- {r}" for r in reasons]) if reasons else "- Cause not clearly identified."
     else:
-        result_message = "🟢 Machine is working fine."
+        msg = "✅ **Machine Status: OPERATING NORMALLY**\n\nAll parameters are within acceptable ranges."
+
+    return msg
 
-    return result_message
 
-# Define custom CSS for professional UI/UX
+# ---------- Professional CSS ----------
 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;
-    }
+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;
+    margin-bottom: 8px;
+}
+
+.subtitle {
+    text-align: center;
+    color: #94a3b8;
+    margin-bottom: 32px;
+}
+
+.block {
+    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;
+}
+
+footer {
+    text-align: center;
+    color: #64748b;
+    font-size: 13px;
+    margin-top: 24px;
+}
 """
 
-# 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
+# ---------- Layout ----------
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown(
+        "<h1>Smart Machine Health Predictor</h1>"
+        "<div class='subtitle'>Predict operational health using real-time machine parameters</div>"
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Group(elem_classes="block"):
+                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")
+
+        with gr.Column(scale=1):
+            output = gr.Markdown(elem_classes="output-box")
+
+    submit.click(
+        predict_status,
+        inputs=[air, proc, speed, torque, wear],
+        outputs=output
+    )
+
+    gr.Markdown(
+        "<footer>Built with Gradio • Machine Health Monitoring System</footer>"
+    )
+
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()