import streamlit as st
from mlpipeline import Pipeline
import warnings
warnings.filterwarnings('ignore')


st.set_page_config(layout='wide')

st.markdown(
    body="<h3 style='text-align: center;'>Stellar Classification</h3>",
    unsafe_allow_html=True
)

col1, col2 = st.columns([1, 2])

with col1:
    # It can be any random value between 0 to 360. Doesn't contribute to model prediction.
    alpha = 150
    # It can be any random value between 0 to 360. Doesn't contribute to model prediction.
    delta = 150
    u = st.slider(label='Ultraviolet', min_value=0.0,
                  max_value=30.0, value=22.0)
    g = st.slider(label='Green', min_value=0.0, max_value=30.0, value=22.0)
    r = st.slider(label='Red', min_value=0.0, max_value=30.0, value=25.0)
    i = st.slider(label='Infrared (I)', min_value=0.0,
                  max_value=30.0, value=10.0)
    z = st.slider(label='Infrared (Z)', min_value=0.0,
                  max_value=30.0, value=5.0)
    redshift = st.slider(label='Redshift', min_value=0.0,
                         max_value=10.0, value=2.0)

data = [[alpha, delta, u, g, r, i, z, redshift]]
pipe = Pipeline(data=data)
conclusion, fig, pred_class = pipe.pipeline()
image_credits = "A random {} image taken from nasa.gov image gallery.".format(
    pred_class.lower())
conclusion = "<p style='text-align: center;'>{}</p>".format(conclusion)
image_credits = "<p style='text-align: center;'>{}</p>".format(image_credits)

with col2:
    st.markdown(body=conclusion, unsafe_allow_html=True)
    st.plotly_chart(figure_or_data=fig, use_container_width=True)
    st.markdown(body=image_credits, unsafe_allow_html=True)