Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+from model_loader import load_model
+
+st.set_page_config(page_title="Predictive Maintenance App")
+
+st.title("Predictive Maintenance – Engine Fault Prediction")
+
+st.markdown(
+    "This app uses a Gradient Boosting model to predict whether an engine is **Normal (0)** "
+    "or **Faulty (1)** based on sensor readings."
+)
+
+# Load model once
+model = load_model()
+
+# Input fields
+st.sidebar.header("Input Engine Sensor Values")
+
+engine_rpm = st.sidebar.number_input("Engine RPM", min_value=1, max_value=5000, value=800)
+lub_oil_pressure = st.sidebar.number_input("Lub Oil Pressure (bar)", min_value=0.0, max_value=10.0, value=3.0)
+fuel_pressure = st.sidebar.number_input("Fuel Pressure (bar)", min_value=0.0, max_value=25.0, value=6.0)
+coolant_pressure = st.sidebar.number_input("Coolant Pressure (bar)", min_value=0.0, max_value=10.0, value=2.0)
+lub_oil_temp = st.sidebar.number_input("Lub Oil Temperature (°C)", min_value=0.0, max_value=150.0, value=78.0)
+coolant_temp = st.sidebar.number_input("Coolant Temperature (°C)", min_value=0.0, max_value=200.0, value=78.0)
+
+# Convert input to dataframe (as required by model)
+input_df = pd.DataFrame([{
+    "Engine_RPM": engine_rpm,
+    "Lub_Oil_Pressure": lub_oil_pressure,
+    "Fuel_Pressure": fuel_pressure,
+    "Coolant_Pressure": coolant_pressure,
+    "Lub_Oil_Temperature": lub_oil_temp,
+    "Coolant_Temperature": coolant_temp
+}])
+
+st.subheader("Input Data")
+st.write(input_df)
+
+if st.button("Predict Engine Condition"):
+    expected = getattr(model, "expected_features", None)
+
+    if expected:
+      for col in expected:
+        if col not in input_df.columns:
+          input_df[col] = 0
+      input_df = input_df[expected]
+
+    pred = model.predict(input_df)[0]
+    label = "Faulty Engine (Maintenance Required)" if pred == 1 else "Normal Engine"
+    st.subheader("Prediction")
+    st.success(f"Engine Condition: {label}")