Create app.py

app.py

@@ -0,0 +1,63 @@
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+st.set_page_config(page_title="Predictive Maintenance – Engine Health", layout="centered")
+
+st.title("Predictive Maintenance – Engine Health")
+st.write("Enter engine sensor readings to predict whether maintenance is needed.")
+
+MODEL_REPO = "SabarnaDeb/Capstone_PredictiveMaintenance_Model"
+MODEL_FILE = "model.joblib"
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE, repo_type="model")
+    return joblib.load(model_path)
+
+model = load_model()
+
+FEATURES = [
+    "engine_rpm",
+    "lub_oil_pressure",
+    "fuel_pressure",
+    "coolant_pressure",
+    "lub_oil_temperature",
+    "coolant_temperature"
+]
+
+engine_rpm = st.number_input("Engine RPM", value=800.0)
+lub_oil_pressure = st.number_input("Lub Oil Pressure", value=4.0)
+fuel_pressure = st.number_input("Fuel Pressure", value=6.5)
+coolant_pressure = st.number_input("Coolant Pressure", value=3.5)
+lub_oil_temperature = st.number_input("Lub Oil Temperature", value=80.0)
+coolant_temperature = st.number_input("Coolant Temperature", value=85.0)
+
+if st.button("Predict"):
+    input_df = pd.DataFrame([{
+        "engine_rpm": engine_rpm,
+        "lub_oil_pressure": lub_oil_pressure,
+        "fuel_pressure": fuel_pressure,
+        "coolant_pressure": coolant_pressure,
+        "lub_oil_temperature": lub_oil_temperature,
+        "coolant_temperature": coolant_temperature,
+    }])
+
+    pred = int(model.predict(input_df[FEATURES])[0])
+
+    prob = None
+    if hasattr(model, "predict_proba"):
+        prob = float(model.predict_proba(input_df[FEATURES])[:, 1][0])
+
+    st.subheader("Prediction Result")
+    if pred == 1:
+        st.error("⚠️ Maintenance Needed")
+    else:
+        st.success("✅ Normal Operation")
+
+    if prob is not None:
+        st.write(f"Confidence (maintenance probability): **{prob:.2f}**")
+
+    st.subheader("Input Data (saved as DataFrame)")
+    st.dataframe(input_df)