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import streamlit as st |
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import pandas as pd |
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import joblib |
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from huggingface_hub import hf_hub_download |
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HF_USERNAME = "Vbhadiar" |
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MODEL_ID = f"{HF_USERNAME}/engine-failure-classifier" |
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@st.cache_resource |
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def load_model(): |
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model_path = hf_hub_download( |
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repo_id=MODEL_ID, |
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filename="best_model.pkl", |
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repo_type="model" |
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) |
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return joblib.load(model_path) |
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model = load_model() |
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st.title("🔧 Engine Predictive Maintenance") |
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st.markdown("Predict whether an engine requires maintenance based on sensor readings.") |
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col1, col2 = st.columns(2) |
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with col1: |
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engine_rpm = st.number_input("Engine RPM", 0, 3000, 800) |
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lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", 0.0, 10.0, 3.5, 0.1) |
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fuel_pressure = st.number_input("Fuel Pressure (bar)", 0.0, 25.0, 7.0, 0.1) |
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with col2: |
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coolant_pressure = st.number_input("Coolant Pressure (bar)", 0.0, 10.0, 2.5, 0.1) |
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lub_oil_temp = st.number_input("Lub Oil Temp (°C)", 50.0, 120.0, 78.0, 0.5) |
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coolant_temp = st.number_input("Coolant Temp (°C)", 50.0, 200.0, 80.0, 0.5) |
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if st.button("Predict"): |
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df_input = 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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}) |
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pred = model.predict(df_input)[0] |
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prob = model.predict_proba(df_input)[0][1] |
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if pred == 1: |
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st.error(f"⚠️ Maintenance required (failure probability: {prob*100:.1f}%)") |
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else: |
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st.success(f"✅ Engine normal (failure probability: {prob*100:.1f}% ") |
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st.write("Input data:") |
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st.dataframe(df_input) |
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