Upload app.py with huggingface_hub

app.py

@@ -4,73 +4,147 @@ import streamlit as st
 from huggingface_hub import hf_hub_download
 import numpy as np
 
-st.set_page_config(page_title="Engine Predictive Maintenance", page_icon="🛠️")
-st.title("Engine Predictive Maintenance – Failure Risk Prediction")
-
-HF_MODEL_REPO = os.getenv("HF_MODEL_REPO")
-if not HF_MODEL_REPO:
-    st.error("HF_MODEL_REPO environment variable is not set. Please configure it in your Space settings.")
-    st.stop()
+st.set_page_config(page_title="Engine Predictive Maintenance", page_icon="🛠️", layout="wide")
+st.title("🚗 Engine Predictive Maintenance – Failure Risk Prediction")
 
+# Get model repo from environment or use default
+HF_MODEL_REPO = os.getenv("HF_MODEL_REPO", "arnavarpit/engine-predictive-maintenance-sklearn")
 MODEL_FILE = "model.joblib"
 
 @st.cache_resource
 def load_model():
-    local_path = hf_hub_download(repo_id=HF_MODEL_REPO, filename=MODEL_FILE)
-    return joblib.load(local_path)
-
+    """Load the trained model from Hugging Face Hub"""
+    try:
+        with st.spinner("🔄 Loading model from Hugging Face Hub..."):
+            local_path = hf_hub_download(repo_id=HF_MODEL_REPO, filename=MODEL_FILE)
+            model = joblib.load(local_path)
+        st.success("✅ Model loaded successfully!")
+        return model
+    except Exception as e:
+        st.error(f"❌ Error loading model: {e}")
+        st.info(f"Attempting to load from: {HF_MODEL_REPO}")
+        st.stop()
+        return None
+
+# Load model
 model = load_model()
 
-st.sidebar.header("Engine Sensor Readings")
+# Show model info
+with st.expander("ℹ️ Model Information"):
+    st.write(f"**Model Repository:** {HF_MODEL_REPO}")
+    st.write(f"**Model File:** {MODEL_FILE}")
+    if model:
+        st.write(f"**Model Type:** {type(model).__name__}")
+
 
+# Input Section
+st.markdown("### 📊 Enter Engine Sensor Readings")
 col1, col2 = st.columns(2)
-engine_rpm = col1.number_input("Engine RPM", value=800.0, step=10.0, help="Engine rotations per minute")
-lub_oil_pressure = col1.number_input("Lube Oil Pressure (bar)", value=3.0, step=0.1, help="Lubrication oil pressure")
-fuel_pressure = col1.number_input("Fuel Pressure (bar)", value=6.0, step=0.1, help="Fuel system pressure")
-coolant_pressure = col1.number_input("Coolant Pressure (bar)", value=2.0, step=0.1, help="Cooling system pressure")
 
-lub_oil_temperature = col2.number_input("Lube Oil Temp (°C)", value=80.0, step=0.5, help="Lubrication oil temperature")
-coolant_temperature = col2.number_input("Coolant Temp (°C)", value=80.0, step=0.5, help="Coolant temperature")
+with col1:
+    st.markdown("**Pressure & RPM Readings**")
+    engine_rpm = st.number_input("Engine RPM", value=800.0, min_value=0.0, max_value=5000.0, step=10.0, help="Engine rotations per minute")
+    lub_oil_pressure = st.number_input("Lube Oil Pressure (bar)", value=3.0, min_value=0.0, max_value=20.0, step=0.1, help="Lubrication oil pressure")
+    fuel_pressure = st.number_input("Fuel Pressure (bar)", value=6.0, min_value=0.0, max_value=20.0, step=0.1, help="Fuel system pressure")
+    coolant_pressure = st.number_input("Coolant Pressure (bar)", value=2.0, min_value=0.0, max_value=20.0, step=0.1, help="Cooling system pressure")
+
+with col2:
+    st.markdown("**Temperature Readings**")
+    lub_oil_temperature = st.number_input("Lube Oil Temp (°C)", value=80.0, min_value=-50.0, max_value=200.0, step=0.5, help="Lubrication oil temperature")
+    coolant_temperature = st.number_input("Coolant Temp (°C)", value=80.0, min_value=-50.0, max_value=200.0, step=0.5, help="Coolant temperature")
 
 st.markdown("---")
 
-col_left, col_right = st.columns([2, 1])
+# Display current readings
+st.markdown("### 📋 Current Sensor Summary")
+col_a, col_b, col_c = st.columns(3)
+with col_a:
+    st.metric("Engine RPM", f"{engine_rpm:.0f}")
+    st.metric("Lube Oil Pressure", f"{lub_oil_pressure:.1f} bar")
+with col_b:
+    st.metric("Fuel Pressure", f"{fuel_pressure:.1f} bar")
+    st.metric("Coolant Pressure", f"{coolant_pressure:.1f} bar")
+with col_c:
+    st.metric("Lube Oil Temp", f"{lub_oil_temperature:.1f} °C")
+    st.metric("Coolant Temp", f"{coolant_temperature:.1f} °C")
 
-with col_left:
-    st.subheader("Current Sensor Readings")
-    st.write(f"**Engine RPM:** {engine_rpm}")
-    st.write(f"**Lube Oil Pressure:** {lub_oil_pressure} bar")
-    st.write(f"**Fuel Pressure:** {fuel_pressure} bar")
-    st.write(f"**Coolant Pressure:** {coolant_pressure} bar")
-    st.write(f"**Lube Oil Temperature:** {lub_oil_temperature} °C")
-    st.write(f"**Coolant Temperature:** {coolant_temperature} °C")
+st.markdown("---")
 
-with col_right:
-    if st.button("Predict Maintenance Need", type="primary"):
+# Prediction Section
+st.markdown("### 🔮 Maintenance Prediction")
+if st.button("🔍 Predict Maintenance Need", type="primary", use_container_width=True):
+    if model is None:
+        st.error("❌ Model not loaded. Cannot make predictions.")
+    else:
+        # Prepare input data
         X = np.array([[engine_rpm, lub_oil_pressure, fuel_pressure, coolant_pressure,
                        lub_oil_temperature, coolant_temperature]])
 
         try:
-            proba = model.predict_proba(X)[:, 1][0]
-            pred = model.predict(X)[0]
-
-            if pred == 1:
-                st.error("⚠️ **MAINTENANCE REQUIRED**")
-                st.metric("Failure Risk Score", f"{proba:.1%}")
-            else:
-                st.success("✅ **NORMAL OPERATION**")
-                st.metric("Failure Risk Score", f"{proba:.1%}")
-
-            # Risk level indicator
-            if proba < 0.3:
-                st.info("🟢 Low Risk")
-            elif proba < 0.7:
-                st.warning("🟡 Medium Risk")
+            with st.spinner("Analyzing sensor data..."):
+                proba = model.predict_proba(X)[:, 1][0]
+                pred = model.predict(X)[0]
+
+            # Display results
+            result_col1, result_col2 = st.columns([1, 1])
+
+            with result_col1:
+                if pred == 1:
+                    st.error("### ⚠️ MAINTENANCE REQUIRED")
+                    st.warning("The engine shows signs of potential failure. Schedule maintenance immediately.")
+                else:
+                    st.success("### ✅ NORMAL OPERATION")
+                    st.info("The engine is operating within normal parameters.")
+
+            with result_col2:
+                st.metric("Failure Risk Score", f"{proba:.1%}", delta=None)
+
+                # Risk level indicator with color coding
+                if proba < 0.3:
+                    st.success("🟢 **Low Risk** - Continue normal operations")
+                elif proba < 0.7:
+                    st.warning("🟡 **Medium Risk** - Monitor closely")
+                else:
+                    st.error("🔴 **High Risk** - Immediate attention required")
+
+            # Additional insights
+            st.markdown("---")
+            st.markdown("#### 📈 Risk Analysis")
+            st.progress(proba)
+
+            if proba > 0.5:
+                st.markdown("""
+                **Recommended Actions:**
+                - Schedule comprehensive engine inspection
+                - Check lubrication and cooling systems
+                - Review sensor readings for anomalies
+                - Prepare maintenance resources
+                """)
             else:
-                st.error("🔴 High Risk")
+                st.markdown("""
+                **Current Status:**
+                - All systems operating normally
+                - Continue regular monitoring
+                - Next scheduled maintenance as per routine
+                """)
 
         except Exception as e:
-            st.error(f"Prediction error: {e}")
+            st.error(f"❌ Prediction error: {e}")
+            st.exception(e)
 
 st.markdown("---")
-st.caption("This app loads the latest model directly from the Hugging Face Model Hub.")
+st.markdown("### 💡 About This Application")
+st.info("""
+This application uses machine learning to predict engine maintenance needs based on real-time sensor data.
+The model analyzes six key engine parameters to assess failure risk and provide maintenance recommendations.
+
+**Features:**
+- Real-time failure risk prediction
+- Interactive sensor input controls
+- Visual risk level indicators
+- Maintenance recommendations
+
+**Model Source:** The trained model is loaded directly from the Hugging Face Model Hub.
+""")
+
+st.caption(f"🤗 Model: `{HF_MODEL_REPO}` | Built with Streamlit")