Update app.py

app.py

@@ -2,57 +2,76 @@ import streamlit as st
 import pandas as pd
 import joblib
 import numpy as np
+from huggingface_hub import hf_hub_download
 
-# Load the model
-model = joblib.load("rf_predictive_maintenance.pkl")
+# --- MODEL LOADING ---
+# Fetches the model directly from your Hugging Face model repository
+REPO_ID = "P-Mishra/engine-predictive-maintenance"
+MODEL_FILENAME = "rf_predictive_maintenance.pkl"
 
+@st.cache_resource
+def load_model():
+    try:
+        model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)
+        return joblib.load(model_path)
+    except Exception as e:
+        st.error(f"Error loading model from Hub: {e}")
+        return None
+
+model = load_model()
+
+# --- UI SETUP ---
+st.set_page_config(page_title="Engine Health Monitor", page_icon="🚢")
 st.title("🚢 Engine Predictive Maintenance")
-st.write("Enter sensor readings to check engine health status.")
+st.write("Input sensor data to predict potential engine failure.")
+
+# --- USER INPUTS ---
+col1, col2 = st.columns(2)
+
+with col1:
+    engine_rpm = st.number_input("Engine RPM", value=1200.0)
+    lub_oil_temp = st.number_input("Lubricating Oil Temp (°C)", value=85.0)
+    lub_oil_pressure = st.number_input("Lubricating Oil Pressure (bar)", value=4.5)
 
-# 1. User Inputs (The 6 base sensors)
-engine_rpm = st.slider("Engine RPM", 400.0, 2500.0, 1200.0)
-lub_oil_temp = st.slider("Lubricating Oil Temp", 60.0, 120.0, 85.0)
-lub_oil_pressure = st.slider("Lubricating Oil Pressure", 1.0, 10.0, 4.5) 
-fuel_pressure = st.slider("Fuel Pressure", 2.0, 10.0, 5.0)
-coolant_temp = st.slider("Coolant Temp", 60.0, 110.0, 80.0)
-coolant_pressure = st.slider("Coolant Pressure", 1.0, 5.0, 2.5)
+with col2:
+    fuel_pressure = st.number_input("Fuel Pressure (bar)", value=5.0)
+    coolant_temp = st.number_input("Coolant Temp (°C)", value=80.0)
+    coolant_pressure = st.number_input("Coolant Pressure (bar)", value=2.5)
 
-# 2. Recreate Engineered Features (Must match notebook logic exactly)
+# --- FEATURE ENGINEERING (Must match notebook logic) ---
 eps = 1e-6
 coolant_temp_pressure_interaction = coolant_temp * coolant_pressure
 coolant_temp_pressure_ratio = coolant_temp / (coolant_pressure + eps)
 lub_oil_temp_engine_rpm_interaction = lub_oil_temp * engine_rpm
 fuel_pressure_engine_rpm_ratio = fuel_pressure / (engine_rpm + eps)
 
-# 3. Create DataFrame with ALL 10 features in the EXACT order of training
-# Order: engine_rpm, lub_oil_temp, lub_oil_pressure, fuel_pressure, coolant_temp, coolant_pressure, 
-#        coolant_temp_pressure_interaction, coolant_temp_pressure_ratio, 
-#        lub_oil_temp_engine_rpm_interaction, fuel_pressure_engine_rpm_ratio
-input_df = pd.DataFrame([[
-    engine_rpm, 
-    lub_oil_temp, 
-    lub_oil_pressure, 
-    fuel_pressure, 
-    coolant_temp, 
-    coolant_pressure,
-    coolant_temp_pressure_interaction,
-    coolant_temp_pressure_ratio,
-    lub_oil_temp_engine_rpm_interaction,
-    fuel_pressure_engine_rpm_ratio
-]], columns=[
+# --- DATAFRAME CONSTRUCTION ---
+# Important: Features must be in the exact order they were trained in your notebook
+feature_columns = [
     'engine_rpm', 'lub_oil_temp', 'lub_oil_pressure', 'fuel_pressure',
     'coolant_temp', 'coolant_pressure', 'coolant_temp_pressure_interaction',
     'coolant_temp_pressure_ratio', 'lub_oil_temp_engine_rpm_interaction',
     'fuel_pressure_engine_rpm_ratio'
-])
+]
 
-# 4. Prediction Logic
-if st.button("Analyze Engine Condition"):
-    try:
-        prediction = model.predict(input_df)
+input_data = pd.DataFrame([[
+    engine_rpm, lub_oil_temp, lub_oil_pressure, fuel_pressure, 
+    coolant_temp, coolant_pressure, coolant_temp_pressure_interaction,
+    coolant_temp_pressure_ratio, lub_oil_temp_engine_rpm_interaction,
+    fuel_pressure_engine_rpm_ratio
+]], columns=feature_columns)
+
+# --- PREDICTION ---
+if st.button("Run Diagnostic"):
+    if model is not None:
+        prediction = model.predict(input_data)
+        
+        st.divider()
         if prediction[0] == 1:
-            st.error("⚠️ Maintenance Required: High Failure Risk Detected")
+            st.error("### 🚨 Result: Maintenance Required")
+            st.write("The model has detected patterns indicating a high probability of engine failure.")
         else:
-            st.success("✅ Engine Condition: Normal")
-    except Exception as e:
-        st.error(f"Prediction Error: {e}")
+            st.success("### ✅ Result: Normal Operation")
+            st.write("Engine sensors are within safe operating parameters.")
+    else:
+        st.error("Model not available.")