File size: 3,683 Bytes
255197b 6945cb8 255197b | 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 | import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
import os
# To ensure app starts loading quickly we set the title first:
st.set_page_config(page_title="Engine Failure Prediction", layout="centered")
# Common constants
HUGGINGFACE_USER_NAME = os.getenv('HUGGINGFACE_USER_NAME')
HUGGINGFACE_MODEL_NAME = os.getenv('HUGGINGFACE_MODEL_NAME')
# Download the model from the Model Hub
@st.cache_resource # Use caching to avoid re-downloading on every slider move
def load_remote_model():
try:
repo_id = f"{HUGGINGFACE_USER_NAME}/{HUGGINGFACE_MODEL_NAME}"
model_path = hf_hub_download(
repo_id=repo_id,
filename="model.joblib"
)
return joblib.load(model_path)
except Exception as e:
print(f"Error loading model: {e}")
return e
# Streamlit UI Setup
st.title("Engine Failure Prediction")
st.write("""
This tool predicts engine health based on real-time telemetry.
Adjust the sliders below to simulate engine sensor data.
""")
st.divider()
# Create UI Layout
col1, col2 = st.columns(2)
with col1:
engine_rpm = st.number_input("Engine RPM", min_value=20, max_value=2500, value=791, step=1, help="Rotations per minute")
lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", min_value=0.0, max_value=8.0, value=3.3, step=0.1)
fuel_pressure = st.number_input("Fuel Pressure (bar)", min_value=0.0, max_value=25.0, value=6.6, step=0.1)
with col2:
coolant_pressure = st.number_input("Coolant Pressure (bar)", min_value=0.0, max_value=8.0, value=2.3, step=0.1)
lub_oil_temp = st.number_input("Lub Oil Temp (°C)", min_value=30.0, max_value=100.0, value=77.6, step=0.1)
coolant_temp = st.number_input("Coolant Temp (°C)", min_value=30.0, max_value=200.0, value=78.4, step=0.1)
# Prepare input data matching the exact training schema
# these keys match the 'numeric_scaling' list in our model training script
input_dict = {
"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
}
input_data = pd.DataFrame([input_dict])
# Prediction Logic
# We use 0.5 but a lower value could be slightly more sensitive to failures (maximizing Recall)
classification_threshold = 0.5
st.divider()
if st.button("Generate Prediction", type="primary"):
# loading the model(it will be cached so only first time it will actually download)
model = load_remote_model()
# The 'model' here is the Scikit-Learn Pipeline
# It automatically runs the StandardScaler on input_data before passing to XGBoost
prediction_proba = model.predict_proba(input_data)[0, 1]
# Apply custom threshold
prediction = 1 if prediction_proba >= classification_threshold else 0
# using markdown heading syntax (###) to adjust font size on the UI:
if prediction == 1:
st.error(f"### ⚠️ CRITICAL: Engine Failure Likely\n**Probability of Failure:** {prediction_proba:.2%}")
st.write("Immediate maintenance inspection recommended to avoid service disruption.")
else:
st.success(f"### ✅ NORMAL: Engine Healthy\n**Probability of Failure:** {prediction_proba:.2%}")
st.write("Engine parameters are within safe operating margins.")
# Add technical metadata for your portfolio
with st.expander("View Model & System Details"):
st.write(f"**Model Source:** Hugging Face Hub ({HUGGINGFACE_MODEL_NAME})")
st.write(f"**Threshold Applied:** {classification_threshold}")
st.write("**Architecture:** Pipeline(StandardScaler -> XGBoost)")
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