Update app.py

app.py

@@ -1,52 +1,143 @@
+%%writefile mlops/deployment/app.py
+
 import streamlit as st
 import pandas as pd
-from huggingface_hub import hf_hub_download
 import joblib
+from huggingface_hub import hf_hub_download
+
+# --------------------------------------------------
+# Page Configuration
+# --------------------------------------------------
+st.set_page_config(
+    page_title="Predictive Maintenance | Engine Health",
+    page_icon="🛠️",
+    layout="centered"
+)
+
+# --------------------------------------------------
+# Load Model (cached)
+# --------------------------------------------------
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id="csankaran3/engine-condition-prediction",
+        filename="best_engine_condition_prediction_model_v1.joblib"
+    )
+    return joblib.load(model_path)
+
+model = load_model()
+
+# --------------------------------------------------
+# Header
+# --------------------------------------------------
+st.title("🛠️ Predictive Maintenance – Engine Condition")
+st.markdown(
+    """
+    This **internal diagnostic tool** helps automobile companies  
+    predict **engine health (Active / Faulty)** using real-time sensor values.
+    """
+)
+
+st.divider()
+
+# --------------------------------------------------
+# Sidebar – Sensor Inputs
+# --------------------------------------------------
+st.sidebar.header("📊 Sensor Inputs")
+
+engine_rpm = st.sidebar.number_input(
+    "Engine RPM",
+    min_value=0.0,
+    value=1150.0,
+    step=10.0,
+    help="Rotational speed of the engine"
+)
 
-# Download the model from the Model Hub
-model_path = hf_hub_download(repo_id="csankaran3/engine-condition-prediction", filename="best_engine_condition_prediction_model_v1.joblib")
+lub_oil_pressure = st.sidebar.number_input(
+    "Lub Oil Pressure (kPa)",
+    min_value=0.0,
+    value=3.63,
+    step=0.01
+)
 
-# Load the model
-model = joblib.load(model_path)
+fuel_pressure = st.sidebar.number_input(
+    "Fuel Pressure (kPa)",
+    min_value=0.0,
+    value=10.57,
+    step=0.01
+)
 
-# Streamlit UI for Customer Churn Prediction
-st.set_page_config(page_title="Predictive Maintenance", layout="centered")
-st.title("Predictive Maintenance - Engine fault prediction application")
-st.write("This App is an internal tool for automobie companies to predict engine condition (Active / Faulty) based on the sensor values.")
-st.subheader("Kindly enter the sensor details to check whether engine condition is active or faulty.")
+coolant_pressure = st.sidebar.number_input(
+    "Coolant Pressure (kPa)",
+    min_value=0.0,
+    value=7.48,
+    step=0.01
+)
 
-# Setting the display value 
-engine_rpm = st.number_input("Engine RPM", min_value=0.0, value=1150.0, step=10.0)
-lub_oil_pressure = st.number_input("Lub Oil Pressure (kPa)", min_value=0.0, value=3.63, step=0.01)
-fuel_pressure = st.number_input("Fuel Pressure (kPa)", min_value=0.0, value=10.57, step=0.01)
-coolant_pressure = st.number_input("Coolant Pressure (kPa)", min_value=0.0, value=7.48, step=0.01)
-lub_oil_temp = st.number_input("Lub Oil Temperature (°C)", min_value=0.32, value=89.58, step=0.01)
-coolant_temp = st.number_input("Coolant Temperature (°C)", min_value=0.67, value=128.60, step=0.01)
+lub_oil_temp = st.sidebar.number_input(
+    "Lub Oil Temperature (°C)",
+    min_value=0.0,
+    value=89.58,
+    step=0.01
+)
 
+coolant_temp = st.sidebar.number_input(
+    "Coolant Temperature (°C)",
+    min_value=0.0,
+    value=128.60,
+    step=0.01
+)
 
-# Convert inputs to match model training
+# --------------------------------------------------
+# Prepare Input Data
+# --------------------------------------------------
 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
+    "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
 }])
 
-# Set the classification threshold
+# --------------------------------------------------
+# Prediction Section
+# --------------------------------------------------
+st.subheader("🔍 Engine Condition Prediction")
+
 classification_threshold = 0.45
 
-# Predict button
-if st.button("Predict Engine Condition"):
-    prediction_proba = model.predict_proba(input_data)[0, 1]
-    prediction = (prediction_proba >= classification_threshold).astype(int)
-    result = "Active" if prediction == 1 else "Faulty"
-    if result == "Active":
+if st.button("🚀 Predict Engine Condition", use_container_width=True):
+    with st.spinner("Analyzing sensor data..."):
+        prediction_proba = model.predict_proba(input_data)[0, 1]
+        prediction = int(prediction_proba >= classification_threshold)
+
+    st.divider()
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.metric(
+            label="Fault Probability",
+            value=f"{prediction_proba:.2%}"
+        )
+
+    with col2:
+        st.metric(
+            label="Threshold",
+            value=f"{classification_threshold:.0%}"
+        )
+
+    st.divider()
+
+    if prediction == 1:
         st.success(
-            f"Engine condition prediction completed!.. **The Engine condition is {result}.**"
+            "✅ **Engine Status: ACTIVE**\n\n"
+            "The engine is operating within safe parameters."
         )
     else:
         st.error(
-            f"Engine condition prediction completed!.. **The Engine condition is {result}.**"
-        )
+            "⚠️ **Engine Status: FAULTY**\n\n"
+            "Potential fault detected. Immediate inspection recommended."
+        )
+)