Upload folder using huggingface_hub

app.py

@@ -6,6 +6,7 @@ import joblib
 from huggingface_hub import hf_hub_download
 import json
 import os
+import plotly.graph_objects as go
 
 # =============================================================================
 # STREAMLIT CONFIG
@@ -27,38 +28,21 @@ def load_artifacts():
         repo_id = "SharleyK/predictive-maintenance-model"
         token = os.getenv("HF_TOKEN")
 
-        model_path = hf_hub_download(
-            repo_id=repo_id,
-            filename="best_model.pkl",
-            token=token
-        )
-        model = joblib.load(model_path)
-
-        scaler_path = hf_hub_download(
-            repo_id=repo_id,
-            filename="scaler.pkl",
-            token=token
-        )
-        scaler = joblib.load(scaler_path)
+        model = joblib.load(hf_hub_download(repo_id, "best_model.pkl", token=token))
+        scaler = joblib.load(hf_hub_download(repo_id, "scaler.pkl", token=token))
 
-        metadata_path = hf_hub_download(
-            repo_id=repo_id,
-            filename="metadata.json",
-            token=token
-        )
-        with open(metadata_path, "r") as f:
-            metadata = json.load(f)
+        metadata_path = hf_hub_download(repo_id, "metadata.json", token=token)
+        metadata = json.load(open(metadata_path))
 
         return model, scaler, metadata, True
 
     except Exception as e:
-        st.error(f"❌ Error loading model artifacts: {e}")
+        st.error(f"❌ Error loading model: {e}")
         return None, None, {}, False
-model, scaler, metadata, MODEL_LOADED = load_artifacts()
 
+model, scaler, metadata, MODEL_LOADED = load_artifacts()
 MODEL_NAME = metadata.get("model_name", "Predictive Maintenance Model")
 
-
 # =============================================================================
 # FEATURE ENGINEERING
 # =============================================================================
@@ -93,8 +77,10 @@ def engineer_features(df):
 # =============================================================================
 
 def predict(df):
+
     df_eng = engineer_features(df)
     X_scaled = scaler.transform(df_eng)
+
     pred = model.predict(X_scaled)[0]
 
     if hasattr(model, "predict_proba"):
@@ -104,33 +90,129 @@ def predict(df):
 
     return pred, confidence
 
+# =============================================================================
+# VISUALS
+# =============================================================================
+
+def gauge_chart(confidence):
+
+    fig = go.Figure(go.Indicator(
+        mode="gauge+number",
+        value=confidence * 100,
+        title={'text': "Confidence %"},
+        gauge={
+            'axis': {'range': [0, 100]},
+            'steps': [
+                {'range': [0, 50], 'color': 'lightgreen'},
+                {'range': [50, 70], 'color': 'yellow'},
+                {'range': [70, 85], 'color': 'orange'},
+                {'range': [85, 100], 'color': 'red'}
+            ],
+        }
+    ))
+
+    fig.update_layout(height=300)
+    return fig
+
+def radar_chart(params):
+
+    categories = [
+        'RPM',
+        'Oil Pressure',
+        'Fuel Pressure',
+        'Coolant Pressure',
+        'Oil Temp',
+        'Coolant Temp'
+    ]
+
+    values = [
+        params["engine_rpm"]/25,
+        params["lub_oil_pressure"]/0.08,
+        params["fuel_pressure"]/0.22,
+        params["coolant_pressure"]/0.08,
+        params["lub_oil_temp"],
+        params["coolant_temp"]/2
+    ]
+
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatterpolar(
+        r=values,
+        theta=categories,
+        fill='toself'
+    ))
+
+    return fig
+
+# =============================================================================
+# RECOMMENDATIONS
+# =============================================================================
+
+def recommendations(pred, rpm, oil_p, fuel_p, coolant_p, oil_t, coolant_t):
+
+    rec = []
+
+    if pred == 1:
+        rec.append("### 🚨 Maintenance Recommended")
+
+        if oil_p < 2.5:
+            rec.append("• Check oil pump / leaks")
+
+        if coolant_t > 85:
+            rec.append("• Cooling system inspection needed")
+
+        if rpm < 600:
+            rec.append("• Engine load or fuel intake check")
+
+        if fuel_p > 8:
+            rec.append("• Possible fuel regulator issue")
+
+    else:
+        rec.append("### ✅ Engine Healthy")
+        rec.append("• Continue routine maintenance")
+        rec.append("• Monitor coolant weekly")
+
+    return "\n".join(rec)
+
 # =============================================================================
 # UI
 # =============================================================================
 
-st.title("🛠️ Predictive Maintenance – Engine Failure Prediction")
-st.caption("AI-powered real-time engine health assessment")
+st.title("🛠️ Predictive Maintenance Dashboard")
+st.caption("Real-time AI Engine Health Monitoring")
 
-st.markdown(f"""
-**Model:** {MODEL_NAME}
-**Accuracy:** 66.68% | **Recall:** 87.13%
-""")
+st.info(f"Model: {MODEL_NAME}")
 
 st.divider()
 
-col1, col2 = st.columns(2)
+col1, col2, col3 = st.columns(3)
 
 with col1:
-    engine_rpm = st.slider("Engine RPM", 50, 2500, 800, step=10)
+    engine_rpm = st.slider("Engine RPM", 50, 2500, 800)
+
+with col2:
     lub_oil_pressure = st.slider("Lub Oil Pressure", 0.0, 8.0, 3.3)
+
+with col3:
     fuel_pressure = st.slider("Fuel Pressure", 0.0, 22.0, 6.5)
 
-with col2:
+col4, col5, col6 = st.columns(3)
+
+with col4:
     coolant_pressure = st.slider("Coolant Pressure", 0.0, 8.0, 2.3)
-    lub_oil_temp = st.slider("Lub Oil Temp (°C)", 70.0, 95.0, 77.0)
-    coolant_temp = st.slider("Coolant Temp (°C)", 60.0, 200.0, 78.0)
 
-if st.button("Predict Engine Condition"):
+with col5:
+    lub_oil_temp = st.slider("Lub Oil Temp", 70.0, 95.0, 77.0)
+
+with col6:
+    coolant_temp = st.slider("Coolant Temp", 60.0, 200.0, 78.0)
+
+# =============================================================================
+# PREDICT BUTTON
+# =============================================================================
+
+if st.button("🔮 Predict Engine Condition"):
+
     df = pd.DataFrame([{
         "engine_rpm": engine_rpm,
         "lub_oil_pressure": lub_oil_pressure,
@@ -142,9 +224,41 @@ if st.button("Predict Engine Condition"):
 
     pred, conf = predict(df)
 
-    if pred == 1:
-        st.error("🚨 FAULTY ENGINE DETECTED")
-    else:
-        st.success("✅ ENGINE OPERATING NORMALLY")
+    colA, colB = st.columns(2)
+
+    with colA:
+        if pred == 1:
+            st.error("🚨 Faulty Engine Detected")
+        else:
+            st.success("✅ Engine Healthy")
+
+        st.metric("Confidence", f"{conf*100:.2f}%")
+
+    with colB:
+        st.plotly_chart(gauge_chart(conf), use_container_width=True)
+
+    st.divider()
+
+    st.subheader("📊 Engine Parameter Profile")
+
+    st.plotly_chart(
+        radar_chart(df.iloc[0]),
+        use_container_width=True
+    )
+
+    st.divider()
+
+    st.subheader("💡 Recommendations")
+
+    st.markdown(
+        recommendations(
+            pred,
+            engine_rpm,
+            lub_oil_pressure,
+            fuel_pressure,
+            coolant_pressure,
+            lub_oil_temp,
+            coolant_temp
+        )
+    )
 
-    st.metric("Confidence", f"{conf*100:.1f}%")