app.py

import streamlit as st
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download

# ==========================================================
# PAGE CONFIGURATION
# ==========================================================

st.set_page_config(
    page_title="Predictive Maintenance System",
    page_icon="🚗",
    layout="centered"
)

# ==========================================================
# TITLE
# ==========================================================

st.title("🚗 Predictive Maintenance System")

st.write("""
This application predicts whether an engine requires maintenance
based on its sensor readings using a trained **AdaBoost Classifier**.
""")

# ==========================================================
# SIDEBAR
# ==========================================================

st.sidebar.title("📌 Project Information")

st.sidebar.markdown("""
### 🤖 Model
AdaBoost Classifier

### 🎯 Model Accuracy
**66.75%**

### 📊 Dataset
Predictive Maintenance Dataset

### 🚀 Deployment
Hugging Face Spaces

### 👩‍💻 Developed By
Brijesh Pandey
""")

# ==========================================================
# LOAD MODEL
# ==========================================================

MODEL_REPO = "killswitch009/predictive-maintenance-model"
MODEL_FILE = "best_model.pkl"

@st.cache_resource
def load_model():
    model_path = hf_hub_download(
        repo_id=MODEL_REPO,
        filename=MODEL_FILE
    )
    return joblib.load(model_path)

try:
    model = load_model()
    st.success("✅ Model loaded successfully!")
except Exception as e:
    st.error(f"Unable to load model.\n\n{e}")
    st.stop()

# ==========================================================
# USER INPUTS
# ==========================================================

st.header("Enter Engine Sensor Values")

with st.expander("📋 Example Sensor Values", expanded=True):
    st.markdown("""
- **Engine RPM:** 700
- **Lub Oil Pressure:** 2.5
- **Fuel Pressure:** 12
- **Coolant Pressure:** 3.2
- **Lub Oil Temperature:** 84
- **Coolant Temperature:** 82
""")

engine_rpm = st.number_input(
    "Engine RPM",
    min_value=0,
    value=800
)

lub_pressure = st.number_input(
    "Lub Oil Pressure",
    min_value=0.0,
    value=3.20
)

fuel_pressure = st.number_input(
    "Fuel Pressure",
    min_value=0.0,
    value=6.50
)

coolant_pressure = st.number_input(
    "Coolant Pressure",
    min_value=0.0,
    value=2.30
)

lub_temp = st.number_input(
    "Lub Oil Temperature",
    min_value=0.0,
    value=77.00
)

coolant_temp = st.number_input(
    "Coolant Temperature",
    min_value=0.0,
    value=78.00
)

# ==========================================================
# PREDICTION
# ==========================================================

if st.button("🔍 Predict Engine Condition", use_container_width=True):

    input_data = pd.DataFrame({
        "Engine rpm": [engine_rpm],
        "Lub oil pressure": [lub_pressure],
        "Fuel pressure": [fuel_pressure],
        "Coolant pressure": [coolant_pressure],
        "lub oil temp": [lub_temp],
        "Coolant temp": [coolant_temp]
    })

    prediction = model.predict(input_data)[0]
    probability = model.predict_proba(input_data)[0]

    healthy_prob = probability[0] * 100
    maintenance_prob = probability[1] * 100

    st.divider()

    st.header("Prediction Result")

    if prediction == 1:
        st.error("⚠️ Engine Requires Maintenance")
        st.warning(
            "The sensor readings indicate that the engine may require maintenance. "
            "A detailed inspection is recommended."
        )
    else:
        st.success("✅ Engine is Operating Normally")

    st.divider()

    st.header("Prediction Confidence")

    col1, col2 = st.columns(2)

    with col1:
        st.metric(
            label="✅ Healthy Engine",
            value=f"{healthy_prob:.2f}%"
        )

    with col2:
        st.metric(
            label="⚠️ Maintenance Required",
            value=f"{maintenance_prob:.2f}%"
        )

    st.divider()

    st.subheader("Summary")

    if prediction == 1:
        st.markdown(f"""
- **Prediction:** Engine Requires Maintenance
- **Healthy Probability:** **{healthy_prob:.2f}%**
- **Maintenance Probability:** **{maintenance_prob:.2f}%**
- **Recommendation:** Schedule maintenance as soon as possible.
""")
    else:
        st.markdown(f"""
- **Prediction:** Engine Operating Normally
- **Healthy Probability:** **{healthy_prob:.2f}%**
- **Maintenance Probability:** **{maintenance_prob:.2f}%**
- **Recommendation:** Continue normal operation and routine monitoring.
""")

# ==========================================================
# FOOTER
# ==========================================================

st.markdown("---")

st.caption(
    "Developed by Brijesh Pandey | Python • Scikit-learn • Streamlit • Hugging Face"
)