Upload app.py

app.py

@@ -0,0 +1,118 @@
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# ----------------------------------------
+# Page Configuration
+# ----------------------------------------
+
+st.set_page_config(
+    page_title="Predictive Maintenance",
+    page_icon="🚗",
+    layout="centered"
+)
+
+st.title("🚗 Predictive Maintenance System")
+st.write(
+    """
+    This application predicts whether an engine requires maintenance
+    based on its sensor readings using the trained AdaBoost model.
+    """
+)
+
+# ----------------------------------------
+# Load Model from Hugging Face
+# ----------------------------------------
+
+MODEL_REPO = "killswitch009/predictive-maintenance-model"
+MODEL_FILE = "best_model.pkl"
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id=MODEL_REPO,
+        filename=MODEL_FILE
+    )
+    return joblib.load(model_path)
+
+#model = load_model()
+
+try:
+    model = load_model()
+    st.success("✅ Model loaded successfully!")
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.stop()
+
+# ----------------------------------------
+# User Inputs
+# ----------------------------------------
+
+st.header("Enter Engine Sensor Values")
+
+engine_rpm = st.number_input(
+    "Engine RPM",
+    min_value=0,
+    value=800
+)
+
+lub_pressure = st.number_input(
+    "Lub Oil Pressure",
+    min_value=0.0,
+    value=3.2
+)
+
+fuel_pressure = st.number_input(
+    "Fuel Pressure",
+    min_value=0.0,
+    value=6.5
+)
+
+coolant_pressure = st.number_input(
+    "Coolant Pressure",
+    min_value=0.0,
+    value=2.3
+)
+
+lub_temp = st.number_input(
+    "Lub Oil Temperature",
+    min_value=0.0,
+    value=77.0
+)
+
+coolant_temp = st.number_input(
+    "Coolant Temperature",
+    min_value=0.0,
+    value=78.0
+)
+
+# ----------------------------------------
+# Prediction
+# ----------------------------------------
+
+if st.button("Predict Engine Condition"):
+
+    input_data = pd.DataFrame({
+        "Engine rpm":[engine_rpm],
+        "Lub oil pressure":[lub_pressure],
+        "Fuel pressure":[fuel_pressure],
+        "Coolant pressure":[coolant_pressure],
+        "lub oil temp":[lub_temp],
+        "Coolant temp":[coolant_temp]
+    })
+
+    prediction = model.predict(input_data)[0]
+    probability = model.predict_proba(input_data)[0]
+
+    st.subheader("Prediction Result")
+
+    if prediction == 1:
+        st.error("⚠️ Engine Requires Maintenance")
+    else:
+        st.success("✅ Engine Operating Normally")
+
+    st.write("### Prediction Confidence")
+
+    st.write(f"Normal : {probability[0]*100:.2f}%")
+    st.write(f"Maintenance Required : {probability[1]*100:.2f}%")