Update Streamlit app

app.py

@@ -0,0 +1,215 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from huggingface_hub import hf_hub_download
+import joblib
+from datetime import datetime
+import warnings
+warnings.filterwarnings("ignore")
+
+# ============================================
+# PAGE CONFIGURATION
+# ============================================
+st.set_page_config(
+    page_title="Engine Predictive Maintenance System",
+    page_icon="🔧",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# ============================================
+# LOAD MODEL FROM HUGGING FACE
+# ============================================
+@st.cache_resource
+def load_model():
+    """Load trained model from Hugging Face Hub"""
+    try:
+        model = hf_hub_download(
+            repo_id="nilanjanadevc/engine-predictive-maintenance-model",
+            filename="model.joblib"
+        )
+        return joblib.load(model)
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+# ============================================
+# FEATURE ENGINEERING FUNCTION
+# ============================================
+def engineer_features(df):
+    """Apply physics-based feature engineering"""
+    df_enhanced = df.copy()
+
+    # Lubrication Stress Index
+    df_enhanced['Lub_Stress_Index'] = df_enhanced['Lub oil pressure'] * df_enhanced['lub oil temp']
+
+    # Thermal Efficiency
+    df_enhanced['Thermal_Efficiency'] = df_enhanced['Coolant pressure'] / (df_enhanced['Coolant temp'] + 1e-5)
+
+    # Power Load Index
+    df_enhanced['Power_Load_Index'] = df_enhanced['Engine rpm'] * df_enhanced['Fuel pressure']
+
+    return df_enhanced
+
+# ============================================
+# MAIN APP
+# ============================================
+st.title("🔧 Engine Predictive Maintenance System")
+st.markdown("Real-time failure prediction using ML and physics-based features")
+
+model = load_model()
+
+if model is None:
+    st.stop()
+
+# ============================================
+# SIDEBAR: INPUT METHOD
+# ============================================
+st.sidebar.header("⚙️ Input Configuration")
+input_method = st.sidebar.radio(
+    "Select input method:",
+    ["📝 Manual Input", "📤 Upload CSV", "🔢 Batch Prediction"]
+)
+
+# ============================================
+# MANUAL INPUT
+# ============================================
+if input_method == "📝 Manual Input":
+    st.header("Manual Engine Sensor Input")
+
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        engine_rpm = st.number_input("Engine RPM", min_value=0.0, max_value=3000.0, value=1000.0)
+        lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", min_value=0.0, max_value=10.0, value=5.0)
+        fuel_pressure = st.number_input("Fuel Pressure (bar)", min_value=0.0, max_value=10.0, value=3.5)
+
+    with col2:
+        coolant_pressure = st.number_input("Coolant Pressure (bar)", min_value=0.0, max_value=5.0, value=2.0)
+        lub_oil_temp = st.number_input("Lub Oil Temp (°C)", min_value=0.0, max_value=150.0, value=80.0)
+        coolant_temp = st.number_input("Coolant Temp (°C)", min_value=0.0, max_value=120.0, value=85.0)
+
+    with col3:
+        st.write("### Summary")
+        st.info(f"✓ {6} sensor inputs ready")
+
+    if st.button("🔍 Predict Engine Condition", key="predict_manual"):
+        # Create dataframe
+        input_data = pd.DataFrame({
+            'Engine rpm': [engine_rpm],
+            'Lub oil pressure': [lub_oil_pressure],
+            'Fuel pressure': [fuel_pressure],
+            'Coolant pressure': [coolant_pressure],
+            'lub oil temp': [lub_oil_temp],
+            'Coolant temp': [coolant_temp]
+        })
+
+        # Engineer features
+        input_enhanced = engineer_features(input_data)
+
+        # Make prediction
+        prediction = model.predict(input_enhanced)[0]
+        probability = model.predict_proba(input_enhanced)[0]
+
+        # Display results
+        st.success("✓ Prediction completed!")
+
+        col_pred, col_prob = st.columns(2)
+
+        with col_pred:
+            if prediction == 0:
+                st.metric("Status", "🟢 HEALTHY", delta="Normal Operation")
+            else:
+                st.metric("Status", "🔴 FAULTY", delta="Maintenance Required")
+
+        with col_prob:
+            st.metric("Confidence", f"{probability[prediction]*100:.2f}%")
+
+        # Risk assessment
+        st.subheader("📊 Risk Assessment")
+        failure_risk = probability[1] * 100
+
+        if failure_risk < 30:
+            risk_level = "🟢 Low Risk"
+        elif failure_risk < 70:
+            risk_level = "🟡 Medium Risk"
+        else:
+            risk_level = "🔴 High Risk"
+
+        st.write(f"Failure Risk: {risk_level} ({failure_risk:.2f}%)")
+
+        # Feature importance for manual input
+        st.subheader("🔍 Sensor Analysis")
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.write("**Oil System:**")
+            st.write(f"• Pressure: {lub_oil_pressure:.2f} bar")
+            st.write(f"• Temp: {lub_oil_temp:.2f}°C")
+        with col2:
+            st.write("**Cooling System:**")
+            st.write(f"• Pressure: {coolant_pressure:.2f} bar")
+            st.write(f"• Temp: {coolant_temp:.2f}°C")
+        with col3:
+            st.write("**Engine Load:**")
+            st.write(f"• RPM: {engine_rpm:.2f}")
+            st.write(f"• Fuel: {fuel_pressure:.2f} bar")
+
+# ============================================
+# CSV UPLOAD
+# ============================================
+elif input_method == "📤 Upload CSV":
+    st.header("Batch CSV Prediction")
+
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+        st.write("### Preview:")
+        st.dataframe(df.head())
+
+        if st.button("🔍 Predict All Rows"):
+            df_enhanced = engineer_features(df)
+            predictions = model.predict(df_enhanced)
+            probabilities = model.predict_proba(df_enhanced)
+
+            results_df = df.copy()
+            results_df['Prediction'] = predictions
+            results_df['Failure_Risk_%'] = probabilities[:, 1] * 100
+            results_df['Status'] = results_df['Prediction'].apply(
+                lambda x: "🟢 HEALTHY" if x == 0 else "🔴 FAULTY"
+            )
+
+            st.success("✓ Predictions completed!")
+            st.dataframe(results_df)
+
+            # Download results
+            csv = results_df.to_csv(index=False)
+            st.download_button(
+                label="📥 Download Predictions",
+                data=csv,
+                file_name=f"predictions_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
+                mime="text/csv"
+            )
+
+# ============================================
+# INFO SECTION
+# ============================================
+st.sidebar.markdown("---")
+st.sidebar.header("ℹ️ About This Model")
+st.sidebar.info("""
+**Physics-Aware Predictive Maintenance System**
+
+- **Training Data**: 19,535 engine observations
+- **Features**: 9 (6 raw + 3 engineered)
+- **Target**: Binary classification (Healthy/Faulty)
+- **Primary Metric**: F2-Score (recall-focused)
+- **Calibration**: Brier Score optimized
+
+**Key Features:**
+- Lubrication Stress Index
+- Thermal Efficiency
+- Power Load Index
+""")
+
+st.sidebar.markdown("---")
+st.sidebar.caption("© 2026 Predictive Maintenance System v1.0")