predict.py

import streamlit as st
import pandas as pd
import joblib

def main():
    # Load the trained model
    try:
        model = joblib.load("RF_adv_stars_class.pkl")
    except Exception as e:
        st.error(f"Error loading model: {e}")
        return  # Exit if model cannot be loaded

    # Title of the web app
    st.title("Star Classification App")

    # Custom CSS for styling
    st.markdown("""
        <style>
            body {
                background-color: #f0f4f8;
                font-family: 'Arial', sans-serif;
            }
            .sidebar .sidebar-content {
                background-color: #f7f7f7;
            }
            .css-1lcbmhc {
                overflow: auto;
            }
            .stButton button {
                background-color: #007bff;
                color: white;
                border: none;
                border-radius: 5px;
                padding: 10px 20px;
                cursor: pointer;
            }
            .stButton button:hover {
                background-color: #0056b3;
            }
            .prediction-box {
                background-color: #333;
                border: 1px solid #fff;
                border-radius: 10px;
                padding: 20px;
                box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
            }
             .note-box {
                background-color: #333; /* Dark background */
                color: #fff; /* White text color */
                border: 1px solid #444; /* Slightly lighter border */
                border-radius: 10px;
                padding: 10px;
                margin-top: 20px;
            }
            .icon {
                font-size: 24px;
                vertical-align: middle;
            }
        </style>
    """, unsafe_allow_html=True)

    # Sidebar for user input
    st.sidebar.header("Input Features")

    def user_input_features():
        alpha = st.sidebar.slider("Alpha", 0.0, 360.0, 180.0)
        delta = st.sidebar.slider("Delta", -90.0, 90.0, 0.0)
        u = st.sidebar.slider("u", 0.0, 30.0, 15.0)
        g = st.sidebar.slider("g", 0.0, 30.0, 15.0)
        r = st.sidebar.slider("r", 0.0, 30.0, 15.0)
        i = st.sidebar.slider("i", 0.0, 30.0, 15.0)
        z = st.sidebar.slider("z", 0.0, 30.0, 15.0)
        redshift = st.sidebar.slider("Redshift", 0.0, 10.0, 0.5)
        plate = st.sidebar.slider("Plate", 0, 9999, 1000)
        MJD = st.sidebar.slider("MJD", 0, 100000, 50000)

        data = {
            'alpha': alpha,
            'delta': delta,
            'u': u,
            'g': g,
            'r': r,
            'i': i,
            'z': z,
            'redshift': redshift,
            'plate': plate,
            'MJD': MJD
        }

        features = pd.DataFrame(data, index=[0])
        return features

    # Get user input
    input_df = user_input_features()

    # Display user input
    st.subheader('User Input Features')
    st.write(input_df)

    # Make prediction with error handling
    try:
        prediction = model.predict(input_df)
    except Exception as e:
        st.error(f"Error making prediction: {e}")
    else:
        # Display the prediction result
        st.subheader('Prediction Result')

        # Customize the prediction message
        prediction_message = ""
        if prediction[0] == 'GALAXY':
            prediction_message = "🌌 It's a `Galaxy`"
        elif prediction[0] == 'QSO':
            prediction_message = "🛰️🔭 It's a `QSO`"
        else:
            prediction_message = "⭐ It's a `Star`"

        st.markdown(f"<div class='prediction-box'>{prediction_message}</div>", unsafe_allow_html=True)

        # Add more details or a description below the result
        st.markdown("""
        <div class="note-box">
            <strong>Note:</strong> The classification is based on the model's analysis of features like alpha, delta, magnitudes in different bands (u, g, r, i, z), redshift, and observation data (plate, MJD).
        </div>
        """, unsafe_allow_html=True)

# Run the main function
if __name__ == "__main__":
    main()