app.py

import streamlit as st
import pandas as pd
import joblib
import os
import xgboost as xgb # Import xgboost for model loading

# Set page config
st.set_page_config(
    page_title="Engine Condition Predictor",
    page_icon="⚙✨",
    layout="centered",
    initial_sidebar_state="expanded",
)

# --- Paths to model and data ---
MODEL_PATH = os.path.join(os.path.dirname(__file__), 'engine1/model', 'tuned_xgboost_model.json') # Update to .json

# Create dummy model directory if it doesn't exist (for local testing)
# In a real deployment, these paths would be correctly set within the Docker container
# or deployment environment.
if not os.path.exists(os.path.join(os.path.dirname(__file__), 'engine/model')):
    os.makedirs(os.path.join(os.path.dirname(__file__), 'engine/model'), exist_ok=True)

# --- Load the Model ---
@st.cache_resource
def load_model(path):
    try:
        # Load model using native XGBoost method
        model = xgb.XGBClassifier() # Initialize an empty model
        model.load_model(path)      # Load the saved parameters
        return model
    except Exception as e:
        st.error(f"Error loading model: {e}")
        return None

model = load_model(MODEL_PATH)

# --- Streamlit UI ---
st.title("\u2699✨ Predictive Engine Maintenance")
st.markdown("\n")
st.markdown(
    "This application predicts whether an engine requires maintenance based on its sensor readings."
)
st.markdown("\n")

st.header("Engine Sensor Data Input")

# Input fields for sensor data
engine_rpm = st.slider("Engine RPM", 0, 2500, 750)
lub_oil_pressure = st.slider("Lub Oil Pressure (bar/kPa)", 0.0, 10.0, 3.0, 0.1)
fuel_pressure = st.slider("Fuel Pressure (bar/kPa)", 0.0, 25.0, 7.0, 0.1)
coolant_pressure = st.slider("Coolant Pressure (bar/kPa)", 0.0, 10.0, 2.5, 0.1)
lub_oil_temp = st.slider("Lub Oil Temperature (\u00b0C)", 60.0, 100.0, 77.0, 0.1)
coolant_temp = st.slider("Coolant Temperature (\u00b0C)", 60.0, 200.0, 78.0, 0.1)

# Create a DataFrame for prediction
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,
        }
    ]
)

st.markdown("\n")
if st.button("Predict Engine Condition", help="Click to predict if maintenance is required"):
    if model is not None:
        prediction = model.predict(input_data)[0]
        prediction_proba = model.predict_proba(input_data)[0][1] # Probability of 'Faulty'

        st.subheader("Prediction Results:")
        if prediction == 1:
            st.error(f"**Prediction: Engine requires maintenance!** ({prediction_proba:.2f} probability of being faulty)")
            st.markdown(
                "*Proactive intervention is recommended based on sensor readings.*"
            )
        else:
            st.success(f"**Prediction: Engine is operating normally.** ({prediction_proba:.2f} probability of being faulty)")
            st.markdown(
                "*Continue regular monitoring. No immediate maintenance is indicated.*"
            )
    else:
        st.warning("Model not loaded. Please check the model path and file.")

st.sidebar.header("About")
st.sidebar.info(
    "This application uses a trained XGBoost Classifier model to predict engine "
    "condition. Input sensor data using the sliders to get real-time predictions."
)
st.sidebar.caption("Developed by Google Colab AI")