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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import joblib |
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from huggingface_hub import hf_hub_download |
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import os |
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MODEL_REPO_ID = "RajendrakumarPachaiappan/engine-predictive-model" |
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MODEL_FILE = "final_random_forest_model.joblib" |
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SCALER_FILE = "standard_scaler.joblib" |
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FEATURE_COLS = ['Engine rpm', 'Lub oil pressure', 'Fuel pressure', |
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'Coolant pressure', 'lub oil temp', 'Coolant temp'] |
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@st.cache_resource |
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def load_model_and_scaler(): |
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"""Downloads and loads the model and scaler from Hugging Face Hub.""" |
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st.info("Loading model and scaler from Hugging Face Hub...") |
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try: |
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model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILE) |
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model = joblib.load(model_path) |
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scaler_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=SCALER_FILE) |
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scaler = joblib.load(scaler_path) |
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st.success("Artifacts loaded successfully!") |
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return model, scaler |
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except Exception as e: |
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st.error(f"Error loading artifacts from Hugging Face Hub: {e}") |
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return None, None |
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model, scaler = load_model_and_scaler() |
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st.set_page_config(page_title="Predictive Maintenance", layout="wide") |
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st.title("Engine Health Predictor") |
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st.markdown("Use the sliders to simulate real-time sensor data and predict the **Engine Condition** (0=Healthy, 1=Faulty).") |
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col1, col2, col3 = st.columns(3) |
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with col1: |
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Engine_rpm = st.slider("Engine RPM (rev/min)", min_value=60, max_value=2300, value=791, step=10) |
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Lub_oil_pressure = st.slider("Lub Oil Pressure (bar)", min_value=0.0, max_value=7.3, value=3.3, step=0.1) |
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Fuel_pressure = st.slider("Fuel Pressure (bar)", min_value=0.0, max_value=22.0, value=6.7, step=0.1) |
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with col2: |
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Coolant_pressure = st.slider("Coolant Pressure (bar)", min_value=0.0, max_value=7.5, value=2.3, step=0.1) |
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Lub_oil_temp = st.slider("Lub Oil Temp (°C)", min_value=71.0, max_value=90.0, value=78.0, step=0.1) |
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Coolant_temp = st.slider("Coolant Temp (°C)", min_value=60.0, max_value=200.0, value=78.5, step=0.5) |
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if st.button("Predict Engine Condition", type="primary"): |
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if model and scaler: |
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input_data = pd.DataFrame({ |
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'Engine rpm': [Engine_rpm], |
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'Lub oil pressure': [Lub_oil_pressure], |
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'Fuel pressure': [Fuel_pressure], |
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'Coolant pressure': [Coolant_pressure], |
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'lub oil temp': [Lub_oil_temp], |
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'Coolant temp': [Coolant_temp] |
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}, index=[0]) |
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scaled_data = scaler.transform(input_data) |
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prediction = model.predict(scaled_data)[0] |
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prediction_proba = model.predict_proba(scaled_data)[0] |
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st.subheader("Prediction Result:") |
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if prediction == 1: |
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st.error(f"**FAULTY (Requires Maintenance)**") |
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st.markdown(f"**Confidence (Faulty):** `{prediction_proba[1]*100:.2f}%`") |
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st.warning("**Actionable Insight:** The model predicts a high risk of failure. Schedule maintenance immediately.") |
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else: |
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st.success(f"**HEALTHY (Normal Operation)**") |
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st.markdown(f"**Confidence (Healthy):** `{prediction_proba[0]*100:.2f}%`") |
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st.info("Engine is operating within normal parameters. Continue monitoring.") |
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