app.py

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="pravin1214/vehicle_break_down", filename="maintainance_prediction_v1.joblib")
model = joblib.load(model_path)

# Streamlit UI for Machine Failure Prediction
st.title("Engine Failure Prediction")
st.write("""
This application Predicts Engine Failure Data.
""")

# User input

engine_rpm = st.number_input("The number of revolutions per minute", min_value=60, max_value=2500, value=60)
oil_pressure = st.number_input("The pressure of the lubricating oil", min_value=0.01, max_value=10.00, value=0.01,format="%.9f", step=0.000000001)
fuel_pressure = st.number_input("The pressure at which fuel is supplied", min_value=0.01, max_value=25.00, value=0.01,format="%.9f", step=0.000000001)
coolant_pressure = st.number_input("The pressure of the engine coolant", min_value=0.01, max_value=10.00, value=0.01,format="%.9f", step=0.000000001)
lub_oil_temp = st.number_input("The temperature of the lubricating oil", min_value=60.00, max_value=100.00, value=60.00,format="%.9f", step=0.000000001)   
coolant_temp = st.number_input("The temperature of the engine coolant", min_value=60.00, max_value=200.00, value=60.00,format="%.9f", step=0.000000001)                             
                                                       

# Assemble input into DataFrame
input_data = pd.DataFrame([{
    'Engine rpm': engine_rpm,
    'Lub oil pressure': oil_pressure,
    'Fuel pressure': fuel_pressure,
    'Coolant pressure': coolant_pressure,
    'lub oil temp': lub_oil_temp,
    'Coolant temp': coolant_temp,
}])


if st.button("Predict Engine Condition:"):
    proba = model.predict_proba(input_data)[0][1]
    prediction = 1 if proba >= 0.45 else 0
    result = "Faulty" if prediction == 1 else "Healthy"
    st.subheader("Prediction Result::")
    st.success(f"The model predicts: **{result}**")