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import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# Download the model from the Model Hub
model_path = hf_hub_download(repo_id="siddhesh1981/Predictive-Maintenance-Model", filename="bagging_predict_model_v1.joblib")
# Load the model
model = joblib.load(model_path)
# Streamlit UI for Tourism Package Purchase Prediction
st.title("Predictive Maintenance Prediction App")
st.write("The Predictive Maintenance Prediction App is an internal tool for Fleet owners and Vehicle Manufacturers, that predicts whether a Vehicle engine is faulty and requires maintenance or not.")
st.write("Kindly enter the Vehicle engine sensor details to check whether the engine is faulty or not.")
# Collect user input
Engine_rpm=st.number_input('Engine_rpm',min_value=60,max_value=2240,value=746)
Lub_oil_pressure= st.number_input('Lub_oil_pressure',min_value=0.000000,max_value=8.000000,value=3.000000)
Fuel_pressure= st.number_input('Fuel_pressure',min_value=0.000000,max_value=22.000000,value=6.000000)
Coolant_pressure=st.number_input('Coolant_pressure',min_value=0.000000,max_value=8.000000,value=2.000000)
lub_oil_temp=st.number_input('lub_oil_temp',min_value=70.000000,max_value=90.000000,value=76.000000)
Coolant_temp=st.number_input('Coolant_temp',min_value=60.000000,max_value=196.000000,value=78.000000)
input_data = pd.DataFrame([{
'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
}])
# Predict button
if st.button("Predict"):
prediction = model.predict(input_data).astype(int)
result = "Faulty and requires maintenance" if prediction == 1 else "NonFaulty and does not require maintenance"
st.write(f"Based on the vehicle engine sensor information provided, the vehicle engine is likely to be {result}.")
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