import streamlit as st import pandas as pd from huggingface_hub import hf_hub_download import joblib # ----------------------------- # Load pipeline model # ----------------------------- model_path = hf_hub_download( repo_id="Rajanan/model-predictive-engine-maintenance", filename="best_engine_failure_model.joblib", repo_type="model" ) model = joblib.load(model_path) # ----------------------------- # UI # ----------------------------- st.title("Predictive Engine Maintenance System") st.markdown(""" Predict whether an engine requires maintenance based on sensor inputs. """) # ----------------------------- # User Inputs # ----------------------------- engine_rpm = st.number_input("Engine RPM", min_value=0, max_value=3000, value=800) 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=25.0, value=6.0) 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 (°C)", min_value=50.0, max_value=120.0, value=75.0) coolant_temp = st.number_input("Coolant Temperature (°C)", min_value=50.0, max_value=200.0, value=80.0) # ----------------------------- # Create 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 with threshold # ----------------------------- if st.button("Predict Engine Condition"): # Probability prob = model.predict_proba(input_data)[0][1] # SAME threshold used in training threshold = 0.4 prediction = 1 if prob >= threshold else 0 st.write(f"Failure Probability: {prob:.2f}") if prediction == 1: st.error("Engine likely requires maintenance!") else: st.success("Engine operating normally")