Update app.py

app.py

@@ -5,101 +5,54 @@ import numpy as np
 import plotly.graph_objects as go
 import os
 
-# --- PAGE CONFIGURATION ---
-st.set_page_config(
-    page_title="Jet Engine AI Predictor",
-    page_icon="✈️",
-    layout="wide"
-)
+# Page Config
+st.set_page_config(page_title="Jet Engine AI", page_icon="✈️")
 
-# --- MODEL LOADING WITH SAFETY CHECK ---
 @st.cache_resource
 def load_model():
-    model_path = 'engine_model.pkl'
-    if not os.path.exists(model_path):
-        return None
-    return joblib.load(model_path)
+    if os.path.exists('engine_model.pkl'):
+        return joblib.load('engine_model.pkl')
+    return None
 
 model = load_model()
 
-# --- UI HEADER ---
-st.title("✈️ Jet Engine Predictive Maintenance System")
-st.markdown("""
-This AI model predicts the **Remaining Useful Life (RUL)** of a turbofan engine based on sensor readings. 
-It helps engineers decide when to perform maintenance *before* a failure occurs.
-""")
+st.title("✈️ Predictive Maintenance Dashboard")
 
-# Check if model is loaded, if not, show instructions
 if model is None:
-    st.error("⚠️ **Model file 'engine_model.pkl' not found!**")
-    st.info("Please upload the `engine_model.pkl` file you generated locally to the 'Files and versions' tab.")
+    st.error("Model file 'engine_model.pkl' not found. Please upload it.")
     st.stop()
 
-# --- SIDEBAR INPUTS ---
-st.sidebar.header("🛠️ Input Sensor Data")
-st.sidebar.markdown("Adjust the sliders based on engine telemetry:")
-
-# Feature list: ['cycles', 's2', 's3', 's4', 's7', 's8', 's11', 's12', 's13', 's15', 's17', 's20', 's21']
-cycle = st.sidebar.slider("Current Operational Cycle", 1, 350, 100)
-s2 = st.sidebar.slider("Sensor 2 (LPC Outlet Temp)", 640.0, 650.0, 642.5)
-s3 = st.sidebar.slider("Sensor 3 (HPC Outlet Temp)", 1580.0, 1600.0, 1589.0)
-s4 = st.sidebar.slider("Sensor 4 (LPT Outlet Temp)", 1400.0, 1430.0, 1408.0)
-s7 = st.sidebar.slider("Sensor 7 (HPC Outlet Press)", 550.0, 560.0, 553.5)
-s11 = st.sidebar.slider("Sensor 11 (HPC Speed)", 47.0, 48.5, 47.5)
-
-# Hidden features (filled with mean values to keep UI clean)
-other_features = [550, 2388, 521, 8.4, 392, 39, 23] 
-
-# --- PREDICTION LOGIC ---
-st.markdown("### 🔍 Diagnostic Analysis")
-
-if st.button("Run AI Prediction", type="primary"):
-    # Prepare input array (Must match training features exactly)
-    input_data = np.array([[cycle, s2, s3, s4, s7, 1300, s11, 550, 2388, 521, 8.4, 392, 39, 23, 1]])
+# Sidebar for Inputs
+st.sidebar.header("Sensor Data Input")
+cycle = st.sidebar.slider("Operational Cycle", 1, 350, 100)
+s2 = st.sidebar.number_input("Sensor 2 (Temp)", value=642.0)
+s3 = st.sidebar.number_input("Sensor 3 (Temp)", value=1589.0)
+s4 = st.sidebar.number_input("Sensor 4 (Pressure)", value=1408.0)
+s7 = st.sidebar.number_input("Sensor 7 (HPC Press)", value=553.0)
+s11 = st.sidebar.number_input("Sensor 11 (Speed)", value=47.5)
+
+if st.button("Predict Engine Health"):
+    # Create input array (matches the 15 features in training)
+    # Filling remaining slots with defaults for simplicity
+    inputs = np.array([[cycle, s2, s3, s4, s7, 550, s11, 2388, 521, 8.4, 392, 39, 23, 0.001, 0.0002]])
+    prediction = model.predict(inputs)
+    rul = int(max(0, prediction[0]))
+
+    # Gauge Chart
+    fig = go.Figure(go.Indicator(
+        mode = "gauge+number",
+        value = rul,
+        title = {'text': "Remaining Useful Life (Cycles)"},
+        gauge = {'axis': {'range': [0, 200]},
+                 'steps' : [
+                     {'range': [0, 30], 'color': "red"},
+                     {'range': [30, 80], 'color': "orange"},
+                     {'range': [80, 200], 'color': "green"}]}))
+    st.plotly_chart(fig)
     
-    # Take only the first 15 features as defined in training
-    prediction = model.predict(input_data[:, :15])
-    rul = max(0, int(prediction[0]))
-
-    # --- RESULTS DISPLAY ---
-    col1, col2 = st.columns([1, 1])
-
-    with col1:
-        # Gauge Chart
-        fig = go.Figure(go.Indicator(
-            mode = "gauge+number",
-            value = rul,
-            domain = {'x': [0, 1], 'y': [0, 1]},
-            title = {'text': "Estimated Cycles Remaining", 'font': {'size': 24}},
-            gauge = {
-                'axis': {'range': [0, 200], 'tickwidth': 1},
-                'bar': {'color': "darkblue"},
-                'steps': [
-                    {'range': [0, 30], 'color': "red"},
-                    {'range': [30, 75], 'color': "orange"},
-                    {'range': [75, 200], 'color': "green"}],
-                'threshold': {
-                    'line': {'color': "black", 'width': 4},
-                    'thickness': 0.75,
-                    'value': rul}
-            }
-        ))
-        st.plotly_chart(fig)
-
-    with col2:
-        st.write("### Engine Health Status")
-        if rul <= 30:
-            st.error(f"🚨 **CRITICAL STATE**\n\nEngine failure predicted within **{rul} cycles**. Maintenance required immediately.")
-        elif rul <= 75:
-            st.warning(f"⚠️ **CAUTION**\n\nEngine showing signs of wear. Estimated life: **{rul} cycles**. Schedule inspection soon.")
-        else:
-            st.success(f"✅ **HEALTHY**\n\nEngine operating normally. Estimated life: **{rul} cycles**.")
-        
-        st.info("**Note:** RUL (Remaining Useful Life) is an estimate based on simulation data patterns.")
-
-else:
-    st.write("Click the button on the left to analyze the current sensor inputs.")
-
-# --- FOOTER ---
-st.markdown("---")
-st.caption("B.Tech AI & Data Science Special Project | Developed for Industrial Predictive Maintenance")
+    if rul < 30:
+        st.error("🚨 CRITICAL: Maintenance Required Immediately!")
+    elif rul < 80:
+        st.warning("⚠️ CAUTION: Schedule Maintenance Soon.")
+    else:
+        st.success("✅ HEALTHY: Engine is in good condition.")