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app.py

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+import streamlit as st
+import joblib
+import numpy as np
+import pandas as pd
+
+# Set page configuration
+st.set_page_config(
+    page_title="Engine Predictive Maintenance",
+    page_icon="⚙️",
+    layout="wide"
+)
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    model = joblib.load('best_xgboost_model.pkl')
+    return model
+
+model = load_model()
+
+# Title and description
+st.title("⚙️ Engine Predictive Maintenance System")
+st.markdown("""
+This application predicts whether an engine is **Normal** or **Faulty** based on sensor readings.
+Enter the sensor values below to get a prediction.
+""")
+
+# Create two columns for better layout
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("📊 Input Sensor Readings")
+
+    # Input fields for the 6 features
+    engine_rpm = st.number_input(
+        "Engine RPM",
+        min_value=0.0,
+        max_value=10000.0,
+        value=2000.0,
+        step=100.0,
+        help="Engine Revolutions Per Minute"
+    )
+
+    lub_oil_pressure = st.number_input(
+        "Lub Oil Pressure (psi)",
+        min_value=0.0,
+        max_value=200.0,
+        value=50.0,
+        step=1.0,
+        help="Lubricating Oil Pressure"
+    )
+
+    fuel_pressure = st.number_input(
+        "Fuel Pressure (psi)",
+        min_value=0.0,
+        max_value=200.0,
+        value=50.0,
+        step=1.0,
+        help="Fuel Pressure"
+    )
+
+with col2:
+    st.subheader("🌡️ Temperature & Pressure")
+
+    coolant_pressure = st.number_input(
+        "Coolant Pressure (psi)",
+        min_value=0.0,
+        max_value=200.0,
+        value=50.0,
+        step=1.0,
+        help="Coolant Pressure"
+    )
+
+    lub_oil_temp = st.number_input(
+        "Lub Oil Temperature (°C)",
+        min_value=0.0,
+        max_value=200.0,
+        value=80.0,
+        step=1.0,
+        help="Lubricating Oil Temperature"
+    )
+
+    coolant_temp = st.number_input(
+        "Coolant Temperature (°C)",
+        min_value=0.0,
+        max_value=150.0,
+        value=70.0,
+        step=1.0,
+        help="Coolant Temperature"
+    )
+
+# Predict button
+if st.button("🔍 Predict Engine Condition", type="primary"):
+    # Create input array with the correct feature order
+    input_data = np.array([[
+        engine_rpm,
+        lub_oil_pressure,
+        fuel_pressure,
+        coolant_pressure,
+        lub_oil_temp,
+        coolant_temp
+    ]])
+
+    # Make prediction
+    prediction = model.predict(input_data)[0]
+    prediction_proba = model.predict_proba(input_data)[0]
+
+    # Display results
+    st.markdown("---")
+    st.subheader("Prediction Result")
+
+    if prediction == 0:
+        st.success("**Engine Status: NORMAL**")
+        st.metric("Confidence", f"{prediction_proba[0]*100:.2f}%")
+        st.info("The engine is operating within normal parameters. Continue regular maintenance schedule.")
+    else:
+        st.error("**Engine Status: FAULTY**")
+        st.metric("Confidence", f"{prediction_proba[1]*100:.2f}%")
+        st.warning("The engine shows signs of potential failure. Immediate inspection recommended!")
+
+    # Show probability distribution
+    st.markdown("### Prediction Probabilities")
+    prob_df = pd.DataFrame({
+        'Condition': ['Normal', 'Faulty'],
+        'Probability': [prediction_proba[0], prediction_proba[1]]
+    })
+    st.bar_chart(prob_df.set_index('Condition'))
+
+# Add footer with information
+st.markdown("---")
+st.markdown("""
+**Model Information:**
+- Algorithm: XGBoost Classifier
+- F1-Score: 0.7630
+- Recall: 87.01%
+- Training Dataset: 19,535 engine records
+
+**Features Used:**
+1. Engine RPM
+2. Lubricating Oil Pressure
+3. Fuel Pressure
+4. Coolant Pressure
+5. Lubricating Oil Temperature
+6. Coolant Temperature
+""")