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import streamlit as st
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download
HF_USERNAME = "Vbhadiar"
MODEL_ID = f"{HF_USERNAME}/engine-failure-classifier"
@st.cache_resource
def load_model():
model_path = hf_hub_download(
repo_id=MODEL_ID,
filename="best_model.pkl",
repo_type="model"
)
return joblib.load(model_path)
model = load_model()
st.title("🔧 Engine Predictive Maintenance")
st.markdown("Predict whether an engine requires maintenance based on sensor readings.")
col1, col2 = st.columns(2)
with col1:
engine_rpm = st.number_input("Engine RPM", 0, 3000, 800)
lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", 0.0, 10.0, 3.5, 0.1)
fuel_pressure = st.number_input("Fuel Pressure (bar)", 0.0, 25.0, 7.0, 0.1)
with col2:
coolant_pressure = st.number_input("Coolant Pressure (bar)", 0.0, 10.0, 2.5, 0.1)
lub_oil_temp = st.number_input("Lub Oil Temp (°C)", 50.0, 120.0, 78.0, 0.5)
coolant_temp = st.number_input("Coolant Temp (°C)", 50.0, 200.0, 80.0, 0.5)
if st.button("Predict"):
df_input = 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],
})
pred = model.predict(df_input)[0]
prob = model.predict_proba(df_input)[0][1]
if pred == 1:
st.error(f"⚠️ Maintenance required (failure probability: {prob*100:.1f}%)")
else:
st.success(f"✅ Engine normal (failure probability: {prob*100:.1f}% ")
st.write("Input data:")
st.dataframe(df_input)
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