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app.py

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+import streamlit as st
+import joblib
+import pandas as pd
+import os
+def load_data():
+  try:
+    return pd.read_csv("data/engine_data.csv")
+  except:
+    return pd.DataFrame(columns=[
+        "Engine rpm","Lub oil pressure","Fuel pressure","Coolant pressure","lub oil temp","Coolant temp","Engine condition"])
+    
+ 
+data=load_data()
+
+#renaming columns for easy processing
+data.columns = (data.columns
+                   .str.strip()
+                   .str.replace(" ","_")
+                   .str.replace(r"[^\w]","_",regex=True)
+  )
+
+if data.empty:
+  new_id=1
+else:
+  new_id=data["CustomerID"].max()+1
+
+# -----------------------------
+# Load Model
+# -----------------------------
+model = joblib.load("best_engine_PM_prediction_v1.joblib")
+
+st.set_page_config(page_title="Engine Condition Predictor", layout="centered")
+
+st.title("🔧 Engine Health Monitoring System")
+st.write("Enter the engine sensor values below to predict engine condition")
+
+# ---- User Inputs ----
+with st.form("engine_input_form"):
+
+    engine_rpm = st.number_input(
+        "Engine RPM",
+        min_value=0,
+        max_value=10000,
+        value=1500,
+        step=50
+    )
+
+    lub_oil_pressure = st.number_input(
+        "Lub Oil Pressure (bar)",
+        min_value=0.0,
+        max_value=20.0,
+        value=3.5,
+        step=0.1
+    )
+
+    fuel_pressure = st.number_input(
+        "Fuel Pressure (bar)",
+        min_value=0.0,
+        max_value=20.0,
+        value=4.0,
+        step=0.1
+    )
+
+    coolant_pressure = st.number_input(
+        "Coolant Pressure (bar)",
+        min_value=0.0,
+        max_value=10.0,
+        value=1.5,
+        step=0.1
+    )
+
+    lub_oil_temp = st.number_input(
+        "Lub Oil Temperature (°C)",
+        min_value=0.0,
+        max_value=200.0,
+        value=85.0,
+        step=1.0
+    )
+
+    coolant_temp = st.number_input(
+        "Coolant Temperature (°C)",
+        min_value=0.0,
+        max_value=200.0,
+        value=90.0,
+        step=1.0
+    )
+    submit = st.form_submit_button("🚀 Predict Engine Condition")
+
+  # -----------------------------
+  # Predict Button
+  # -----------------------------
+if st.button("Predict"):
+
+    input_df = pd.DataFrame({
+            "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.success("✅ Input captured successfully")
+    st.write("### Input Data")
+    st.dataframe(input_df)
+
+
+    # Predict
+    prediction = model.predict(input_df)[0]
+    prob = model.predict_proba(input_df)[0][1]
+
+    st.subheader("Prediction Result")
+
+    if prediction == 1:
+      st.success(f"Engine needs Preventive maintenance. Probability: {prob:.2f}")
+    else:
+      st.error(f"Engine working normal. Probability: {prob:.2f}")
+
+      # Save prediction to dataframe
+      input_df["Predicted_ProdTaken"] = prediction
+      input_df["Probability"] = round(prob, 4)
+
+      st.write("### Record to be saved:")
+      st.dataframe(pd.DataFrame([input_df]))
+
+      # -----------------------------
+      # SAVE RECORDS SECTION
+      # -----------------------------
+      if st.button("Save Record"):
+          file_path = "records.csv"
+
+          # If file exists → append
+          if os.path.exists(file_path):
+              existing_df = pd.read_csv(file_path)
+              updated_df = pd.concat([existing_df, pd.DataFrame([input_df])], ignore_index=True)
+          else:
+              # Create new CSV
+              updated_df = pd.DataFrame([input_data])
+
+          updated_df.to_csv(file_path, index=False)
+
+          st.success("Record saved successfully!")