import streamlit as st
import pandas as pd
import numpy as np
import joblib
from huggingface_hub import hf_hub_download
import os


MODEL_REPO_ID = "RajendrakumarPachaiappan/engine-predictive-model"
MODEL_FILE = "final_random_forest_model.joblib" 
SCALER_FILE = "standard_scaler.joblib"

FEATURE_COLS = ['Engine rpm', 'Lub oil pressure', 'Fuel pressure', 
                'Coolant pressure', 'lub oil temp', 'Coolant temp']


@st.cache_resource
def load_model_and_scaler():
    """Downloads and loads the model and scaler from Hugging Face Hub."""
    st.info("Loading model and scaler from Hugging Face Hub...")
    try:
        model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILE)
        model = joblib.load(model_path)
        scaler_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=SCALER_FILE)
        scaler = joblib.load(scaler_path)
        st.success("Artifacts loaded successfully!")
        return model, scaler
    except Exception as e:
        st.error(f"Error loading artifacts from Hugging Face Hub: {e}")
        return None, None

model, scaler = load_model_and_scaler()

# Streamlit UI and Prediction Logic
st.set_page_config(page_title="Predictive Maintenance", layout="wide")
st.title("Engine Health Predictor")
st.markdown("Use the sliders to simulate real-time sensor data and predict the **Engine Condition** (0=Healthy, 1=Faulty).")

col1, col2, col3 = st.columns(3)

with col1:
    
    Engine_rpm = st.slider("Engine RPM (rev/min)", min_value=60, max_value=2300, value=791, step=10)
    Lub_oil_pressure = st.slider("Lub Oil Pressure (bar)", min_value=0.0, max_value=7.3, value=3.3, step=0.1)
    Fuel_pressure = st.slider("Fuel Pressure (bar)", min_value=0.0, max_value=22.0, value=6.7, step=0.1)

with col2:

    Coolant_pressure = st.slider("Coolant Pressure (bar)", min_value=0.0, max_value=7.5, value=2.3, step=0.1)
    Lub_oil_temp = st.slider("Lub Oil Temp (°C)", min_value=71.0, max_value=90.0, value=78.0, step=0.1)
    Coolant_temp = st.slider("Coolant Temp (°C)", min_value=60.0, max_value=200.0, value=78.5, step=0.5)
    
# Prediction
if st.button("Predict Engine Condition", type="primary"):
    if model and scaler:
        
        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]
        }, index=[0])
        
        
        scaled_data = scaler.transform(input_data)
        
        prediction = model.predict(scaled_data)[0]
        prediction_proba = model.predict_proba(scaled_data)[0]

        # Display Results
        st.subheader("Prediction Result:")
        if prediction == 1:
            st.error(f"**FAULTY (Requires Maintenance)**")
            st.markdown(f"**Confidence (Faulty):** `{prediction_proba[1]*100:.2f}%`")
            st.warning("**Actionable Insight:** The model predicts a high risk of failure. Schedule maintenance immediately.")
        else:
            st.success(f"**HEALTHY (Normal Operation)**")
            st.markdown(f"**Confidence (Healthy):** `{prediction_proba[0]*100:.2f}%`")
            st.info("Engine is operating within normal parameters. Continue monitoring.")