import streamlit as st
from pycaret.regression import *
from pathlib import Path

path_root = Path(Path.cwd())

from pycaret.datasets import get_data


st.markdown("# Regression❄️")
with st.sidebar:
    st.sidebar.markdown("# Regression Feature ❄️")
    evaluation_option = st.selectbox("evaluation", ["residuals", "error", "cooks", "rfe", "learning", "vc", "manifold", "feature", "feature_all", "residuals_interactive", "parameter", "tree"])

data = get_data('insurance')
s = setup(data, target = 'charges')
st.markdown("## Compare model")
best = compare_models()
df_metric = pull()
st.dataframe(df_metric)
st.markdown("## Evaluation")
evaluate_model(best)
image_options = {"error": "Prediction Error.png", 
                 "residuals": "Residuals.png",
                 "cooks": "unknow",
                 "rfe": "unknow",
                 "learning": "Learning Curve.png",
                 "vc": "Validation Curve.png",
                 "manifold": "Manifold Curve.png",
                 "feature": "Feature Importance.png",
                 "feature_all": "Feature Importance (All).png",
                 "residuals_interactive": "unknow"
                 }
try:
    st.markdown(f"## {evaluation_option}")
    plot_model(best, plot = evaluation_option, save = 'images')
    st.image(str(path_root.joinpath(f"images/{image_options[evaluation_option]}")))
except e:
    st.text(e)