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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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st.set_page_config(page_title="Engine Condition Prediction") |
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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="ShanRaja/engine-fault-xgboost", |
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filename="best_model.joblib" |
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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 Condition Prediction") |
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engine_rpm = st.number_input( |
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"Engine RPM", |
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min_value=50, |
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max_value=2250, |
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value=791, |
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step=50 |
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) |
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lub_oil_pressure = st.number_input( |
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"Lub Oil Pressure (bar)", |
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min_value=0.0, |
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max_value=8.0, |
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value=3.3, |
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step=0.1, |
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format="%.3f" |
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) |
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fuel_pressure = st.number_input( |
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"Fuel Pressure (bar)", |
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min_value=0.0, |
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max_value=22.0, |
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value=6.66, |
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step=0.1, |
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format="%.3f" |
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) |
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coolant_pressure = st.number_input( |
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"Coolant Pressure (bar)", |
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min_value=0.0, |
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max_value=8.0, |
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value=2.33, |
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step=0.1, |
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format="%.3f" |
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) |
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lub_oil_temp = st.number_input( |
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"Lub Oil Temp (°C)", |
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min_value=70.0, |
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max_value=90.0, |
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value=77.6, |
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step=0.5, |
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format="%.2f" |
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) |
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coolant_temp = st.number_input( |
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"Coolant Temp (°C)", |
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min_value=60.0, |
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max_value=200.0, |
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value=78.4, |
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step=0.5, |
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format="%.2f" |
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) |
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if st.button("Predict"): |
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input_df = pd.DataFrame( |
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[[ |
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int(engine_rpm), |
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lub_oil_pressure, |
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fuel_pressure, |
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coolant_pressure, |
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lub_oil_temp, |
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coolant_temp |
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]], |
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columns=[ |
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"Engine rpm", |
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"Lub oil pressure", |
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"Fuel pressure", |
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"Coolant pressure", |
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"lub oil temp", |
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"Coolant temp" |
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] |
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) |
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prediction = model.predict(input_df)[0] |
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label = "FAULTY" if prediction == 1 else "NORMAL" |
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st.success(f"Engine Condition: {label}") |
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st.header("Batch Prediction from CSV") |
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uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type="csv") |
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if uploaded_file is not None: |
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input_data = pd.read_csv(uploaded_file) |
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required_cols = ["Engine rpm","Lub oil pressure","Fuel pressure","Coolant pressure","lub oil temp","Coolant temp"] |
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if not all(col in input_data.columns for col in required_cols): |
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st.error(f"CSV must contain columns: {', '.join(required_cols)}") |
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else: |
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predictions = model.predict(input_data) |
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input_data["Engine Condition"] = ["FAULTY" if p == 1 else "NORMAL" for p in predictions] |
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st.success("Predictions completed!") |
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st.dataframe(input_data) |
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csv = input_data.to_csv(index=False).encode("utf-8") |
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st.download_button( |
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label="Download Predictions as CSV", |
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data=csv, |
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file_name="engine_predictions.csv", |
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mime="text/csv", |
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) |
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