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import streamlit as st
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download
# ---------------------------------------------------
# Page config
# ---------------------------------------------------
st.set_page_config(
page_title="Predictive Maintenance Engine Risk Predictor",
layout="centered"
)
# ---------------------------------------------------
# Load Model from Hugging Face
# ---------------------------------------------------
REPO_ID = "subratm62/predictive-maintenance"
MODEL_FILE = "predictive_maintenance_pipeline.joblib"
@st.cache_resource
def load_model():
model_path = hf_hub_download(
repo_id=REPO_ID,
filename=MODEL_FILE
)
return joblib.load(model_path)
model = load_model()
# Classification threshold
classification_threshold = 0.50
# ---------------------------------------------------
# UI Header
# ---------------------------------------------------
st.title("🔧 Predictive Maintenance — Engine Failure Risk")
st.write(
"""
Enter live engine sensor readings to estimate **failure risk**.
This tool supports proactive maintenance decisions.
"""
)
st.markdown("---")
# ---------------------------------------------------
# Sensor Inputs
# ---------------------------------------------------
st.subheader("Engine Sensor Inputs")
col1, col2 = st.columns(2)
with col1:
engine_rpm = st.number_input(
"Engine RPM",
min_value=0.0,
max_value=5000.0,
value=750.0
)
lub_oil_pressure = st.number_input(
"Lub Oil Pressure",
min_value=0.0,
max_value=10.0,
value=3.0
)
fuel_pressure = st.number_input(
"Fuel Pressure",
min_value=0.0,
max_value=50.0,
value=6.0
)
with col2:
coolant_pressure = st.number_input(
"Coolant Pressure",
min_value=0.0,
max_value=10.0,
value=2.0
)
lub_oil_temp = st.number_input(
"Lub Oil Temperature",
min_value=50.0,
max_value=150.0,
value=77.0
)
coolant_temp = st.number_input(
"Coolant Temperature",
min_value=50.0,
max_value=250.0,
value=78.0
)
st.markdown("---")
# ---------------------------------------------------
# Prepare input 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
}])
# ---------------------------------------------------
# Prediction
# ---------------------------------------------------
if st.button("🔍 Predict Failure Risk"):
probability = model.predict_proba(input_data)[0, 1]
prediction = int(probability >= classification_threshold)
st.subheader("Prediction Result")
if prediction == 1:
st.error(
"⚠ FAILURE RISK — Maintenance inspection recommended."
)
else:
st.success(
"✅ Engine operating within normal range."
)
st.write(f"**Failure Probability:** {probability:.4f}")
st.write(f"**Decision Threshold:** {classification_threshold:.2f}")
# Business interpretation
if probability > 0.75:
st.warning("Critical condition — immediate inspection advised.")
elif probability > 0.50:
st.info("Moderate risk — schedule maintenance soon.")
else:
st.write("Low operational risk detected.")
st.markdown("---")
st.caption(
"Model hosted on Hugging Face | Experiment tracking via MLflow | Built with Streamlit"
)
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