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import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# Download and load the model
model_path = hf_hub_download(repo_id="Sandhya777/engine_condition_prediction_model", filename="best_engine_condition_prediction_model_v1.joblib")
model = joblib.load(model_path)
# Streamlit UI for Machine Failure Prediction
st.title("Engine Condition Prediction App")
st.write("""
This application predicts the likelihood of a engine failing based on its operational parameters.
Please enter the sensor and configuration data below to get a prediction.
""")
# User input
Engine_rpm = st.number_input(
"Engine rpm",
min_value=0.0,
max_value=2500.0,
value=750.0,
step=10.0
)
Lub_oil_pressure = st.number_input(
"Lub oil pressure",
min_value=0.0,
max_value=8.0,
value=3.2,
step=0.1
)
Fuel_pressure = st.number_input(
"Fuel pressure",
min_value=0.0,
max_value=22.0,
value=6.2,
step=0.1
)
Coolant_pressure = st.number_input(
"Coolant pressure",
min_value=0.0,
max_value=8.0,
value=2.2,
step=0.1
)
lub_oil_temp = st.number_input(
"Lub oil temperature",
min_value=60.0,
max_value=100.0,
value=77.0,
step=0.5
)
Coolant_temp = st.number_input(
"Coolant temperature",
min_value=60.0,
max_value=200.0,
value=78.0,
step=0.5
)
# Assemble input into DataFrame
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
}])
if st.button("Predict Failure"):
prediction = model.predict(input_data)[0]
result = "Engine Failure" if prediction == 1 else "No Failure"
st.subheader("Prediction Result:")
st.success(f"The model predicts: **{result}**")
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