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import streamlit as st
import joblib
import pandas as pd
import numpy as np
from huggingface_hub import hf_hub_download
import os
# Configuration
HF_MODEL_REPO = os.getenv("HF_MODEL_REPO", "dhani10/engine-maintenance-model")
MODEL_FILE = os.getenv("MODEL_FILE", "best_model.pkl")
# Expected features (match your training data exactly - should be snake_case)
EXPECTED_COLS = [
'engine_rpm', 'lub_oil_pressure', 'fuel_pressure',
'coolant_pressure', 'lub_oil_temp', 'coolant_temp'
]
@st.cache_resource
def load_model():
"""Load the model from Hugging Face Hub"""
try:
model_path = hf_hub_download(
repo_id=HF_MODEL_REPO,
filename=MODEL_FILE,
repo_type="model",
token=os.getenv("HF_TOKEN")
)
model = joblib.load(model_path)
st.success("Model loaded successfully!")
return model
except Exception as e:
st.error(f"Failed to load model: {e}")
return None
def main():
st.set_page_config(
page_title="Engine Condition Predictor",
layout="centered",
page_icon="🚧"
)
st.title("Predictive Maintenance — Engine Condition")
st.markdown("Monitor engine health using real-time sensor data")
st.caption(f"Model: {HF_MODEL_REPO}")
# Load model
with st.spinner("Loading AI model..."):
model = load_model()
if model is None:
st.stop()
# Input form
st.header("Engine Sensor Readings")
with st.form("prediction_form"):
col1, col2 = st.columns(2)
with col1:
engine_rpm = st.slider("Engine RPM", 100, 2500, 1200)
lub_oil_pressure = st.slider("Lub Oil Pressure (bar)", 0.5, 7.0, 3.0, 0.1)
fuel_pressure = st.slider("Fuel Pressure (bar)", 0.5, 20.0, 6.0, 0.1)
with col2:
coolant_pressure = st.slider("Coolant Pressure (bar)", 0.5, 7.0, 2.0, 0.1)
lub_oil_temp = st.slider("Lub Oil Temp (°C)", 70.0, 110.0, 80.0, 0.1)
coolant_temp = st.slider("Coolant Temp (°C)", 60.0, 100.0, 75.0, 0.1)
submitted = st.form_submit_button("Analyze Engine Condition", type="primary")
if submitted:
# Create input data with EXACT column names from training
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
}])
try:
# Make prediction
prediction = model.predict(input_data)[0]
probability = model.predict_proba(input_data)[0]
# Display results
st.header("Analysis Results")
if prediction == 1:
st.error("**FAULTY ENGINE DETECTED**")
st.progress(probability[1])
st.warning(f"**Risk Probability:** {probability[1]*100:.1f}%")
st.markdown("""
**Recommended Actions:**
- Schedule immediate maintenance
- Inspect lubrication system
- Check cooling system
""")
else:
st.success("**ENGINE OPERATING NORMALLY**")
st.progress(probability[0])
st.info(f"**Health Score:** {probability[0]*100:.1f}%")
st.markdown("""
**Status:** Continue routine monitoring
**Next maintenance:** As scheduled
""")
# Show input data
with st.expander("View Input Data"):
st.dataframe(input_data)
except Exception as e:
st.error(f"Prediction error: {str(e)}")
st.info("Please check that the model expects the correct feature names")
if __name__ == "__main__":
main()
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