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3508743 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | import streamlit as st
import pandas as pd
import joblib
import matplotlib.pyplot as plt
# -----------------------------
# PAGE CONFIG
# -----------------------------
st.set_page_config(page_title="Engine Health Monitor", layout="wide")
st.title("🚗 Engine Predictive Maintenance Dashboard")
st.write(
"Predict engine health using sensor data. "
"Adjust the decision threshold to balance false alarms vs missed failures."
)
# -----------------------------
# LOAD MODEL
# -----------------------------
@st.cache_resource
def load_model():
return joblib.load("best_model.pkl")
model = load_model()
feature_names = model.feature_names_in_
# -----------------------------
# SIDEBAR SETTINGS
# -----------------------------
st.sidebar.header("⚙ Prediction Settings")
threshold = st.sidebar.slider(
"Failure Decision Threshold",
min_value=0.10,
max_value=0.90,
value=0.50,
step=0.01,
help="Lower → detect more faults\nHigher → reduce false alarms"
)
st.sidebar.markdown("---")
st.sidebar.write("**Engine Condition Meaning**")
st.sidebar.write("0 → Normal")
st.sidebar.write("1 → Faulty")
# -----------------------------
# SEVERITY FUNCTION
# -----------------------------
def get_severity(prob):
if prob < 0.4:
return "🟢 Low Risk"
elif prob < 0.7:
return "🟡 Moderate Risk"
else:
return "🔴 High Risk"
# -----------------------------
# MANUAL PREDICTION
# -----------------------------
st.header("🔧 Manual Prediction")
cols = st.columns(3)
inputs = []
for i, feature in enumerate(feature_names):
with cols[i % 3]:
val = st.number_input(feature, value=0.0)
inputs.append(val)
if st.button("Predict Engine Condition"):
input_df = pd.DataFrame([inputs], columns=feature_names)
prob = model.predict_proba(input_df)[0][1]
prediction = 1 if prob >= threshold else 0
severity = get_severity(prob)
# Result
if prediction == 1:
st.error("⚠ Engine Likely Faulty")
else:
st.success("✅ Engine Operating Normally")
st.write(f"### Failure Probability: **{prob:.3f}**")
st.write(f"### Severity Level: {severity}")
# Gauge Chart
fig, ax = plt.subplots()
ax.barh(["Risk"], [prob])
ax.set_xlim(0,1)
ax.set_title("Failure Risk Level")
st.pyplot(fig)
# -----------------------------
# BATCH PREDICTION
# -----------------------------
st.header("📂 Batch Prediction")
uploaded_file = st.file_uploader("Upload CSV", type=["csv"])
if uploaded_file is not None:
try:
df = pd.read_csv(uploaded_file)
if len(df) > 20000:
st.warning("⚠ Maximum 10,000 rows allowed.")
else:
missing_cols = [col for col in feature_names if col not in df.columns]
if missing_cols:
st.error(f"Missing columns: {missing_cols}")
else:
df = df[feature_names]
probs = model.predict_proba(df)[:, 1]
df["Failure_Probability"] = probs
df["Prediction"] = (probs >= threshold).astype(int)
df["Severity"] = df["Failure_Probability"].apply(get_severity)
st.success("✅ Predictions completed")
st.dataframe(df.head())
csv = df.to_csv(index=False).encode("utf-8")
st.download_button(
"Download Results",
csv,
"engine_predictions.csv",
"text/csv"
)
except Exception as e:
st.error(f"Error: {e}")
# -----------------------------
# MODEL INFO
# -----------------------------
st.markdown("---")
st.subheader("📊 Model Information")
st.write("✔ Algorithm: Random Forest")
st.write("✔ Handles feature correlation & non-linearity")
st.write("✔ Optimized for predictive maintenance")
st.info(
"Tip: Lower threshold if missing failures is costly.\n"
"Raise threshold if false alarms are costly."
)
st.markdown("---")
st.caption("Built for predictive maintenance monitoring")
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