Upload app.py with huggingface_hub

app.py

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+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from huggingface_hub import hf_hub_download
+
+st.set_page_config(page_title="Predictive Maintenance", layout="wide")
+
+@st.cache_resource
+def load_model():
+    repo = "SharleyK/predictive-maintenance-model"
+    model = joblib.load(hf_hub_download(repo, "best_model.pkl"))
+    scaler = joblib.load(hf_hub_download(repo, "scaler.pkl"))
+    return model, scaler
+
+model, scaler = load_model()
+
+st.title("Predictive Maintenance - Engine Failure Prediction")
+
+engine_rpm = st.slider("Engine RPM", 100, 2500, 800)
+lub_oil_pressure = st.slider("Lub Oil Pressure", 0.0, 8.0, 3.0)
+fuel_pressure = st.slider("Fuel Pressure", 0.0, 22.0, 6.0)
+coolant_pressure = st.slider("Coolant Pressure", 0.0, 8.0, 2.5)
+lub_oil_temp = st.slider("Lub Oil Temp", 70.0, 95.0, 80.0)
+coolant_temp = st.slider("Coolant Temp", 60.0, 200.0, 85.0)
+
+if st.button("Predict"):
+    X = 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
+    }])
+
+    X_scaled = scaler.transform(X)
+    prediction = model.predict(X_scaled)[0]
+
+    if prediction == 1:
+        st.error("Engine Failure Detected")
+    else:
+        st.success("Engine Operating Normally")