Update app.py

app.py

@@ -6,15 +6,16 @@ from huggingface_hub import hf_hub_download
 
 
 # ----------------------------------------------------
-# Load trained model 
+# Load trained model
 # ----------------------------------------------------
-# Download the model from the Model Hub
-MODEL_PATH = hf_hub_download(repo_id="raj2261992/predictive_maintenance_model", filename="engine_condition_xgboost_v1.joblib")
+MODEL_PATH = hf_hub_download(
+    repo_id="raj2261992/predictive_maintenance_model",
+    filename="engine_condition_xgboost_v1.joblib"
+)
 
 model = joblib.load(MODEL_PATH)
 
 
-
 # ----------------------------------------------------
 # Page Config
 # ----------------------------------------------------
@@ -28,6 +29,7 @@ st.markdown("Enter live sensor values to predict engine condition.")
 
 st.divider()
 
+
 # ----------------------------------------------------
 # Sidebar Inputs
 # ----------------------------------------------------
@@ -36,43 +38,50 @@ st.sidebar.header("Sensor Inputs")
 engine_rpm = st.sidebar.number_input("Engine RPM", min_value=0.0, value=1500.0)
 
 lub_oil_pressure = st.sidebar.number_input(
-    "Lub Oil Pressure (bar/kPa)", min_value=0.0, value=3.5
+    "Lub Oil Pressure", min_value=0.0, value=3.5
 )
 
 fuel_pressure = st.sidebar.number_input(
-    "Fuel Pressure (bar/kPa)", min_value=0.0, value=4.0
+    "Fuel Pressure", min_value=0.0, value=4.0
 )
 
 coolant_pressure = st.sidebar.number_input(
-    "Coolant Pressure (bar/kPa)", min_value=0.0, value=2.0
+    "Coolant Pressure", min_value=0.0, value=2.0
 )
 
 lub_oil_temp = st.sidebar.number_input(
-    "Lub Oil Temperature (°C)", min_value=0.0, value=80.0
+    "Lub Oil Temperature", min_value=0.0, value=80.0
 )
 
 coolant_temp = st.sidebar.number_input(
-    "Coolant Temperature (°C)", min_value=0.0, value=75.0
+    "Coolant Temperature", min_value=0.0, value=75.0
 )
 
+
 # ----------------------------------------------------
 # Prediction
 # ----------------------------------------------------
 if st.button("Predict Engine Condition"):
 
-    input_data = np.array([[
-        engine_rpm,
-        lub_oil_pressure,
-        fuel_pressure,
-        coolant_pressure,
-        lub_oil_temp,
-        coolant_temp
+    # IMPORTANT: Must be DataFrame (not NumPy)
+    input_data = pd.DataFrame([{
+        "Engine rpm": float(engine_rpm),
+        "Lub oil pressure": float(lub_oil_pressure),
+        "Fuel pressure": float(fuel_pressure),
+        "Coolant pressure": float(coolant_pressure),
+        "lub oil temp": float(lub_oil_temp),
+        "Coolant temp": float(coolant_temp),
+    }])
 
-    ]])
+    # Debug display (optional)
+    st.write("### Input Data")
+    st.write(input_data)
 
+    # Predict probability
+    prob = model.predict_proba(input_data)[0][1]
 
-
-    prediction = model.predict(input_data)[0]
+    threshold = 0.45
+    prediction = int(prob >= threshold)
 
     st.subheader("Prediction Result")
 
@@ -81,17 +90,13 @@ if st.button("Predict Engine Condition"):
     else:
         st.error("Engine Condition: FAULTY / AT RISK")
 
+    st.metric("Failure Probability", f"{prob:.2%}")
+
     st.divider()
 
     st.write("### Input Summary")
-    st.table({
-        "Engine rpm": [engine_rpm],
-        "Lub oil pressure": [lub_oil_pressure],
-        "Fuel pressure": [fuel_pressure],
-        "Coolant pressure": [coolant_pressure],
-        "lub oil temp": [lub_oil_temp],
-        "Coolant temp": [coolant_temp],
-    })
+    st.table(input_data)
+
 
 # ----------------------------------------------------
 # Footer