import streamlit as st 
import altair as alt 
import pandas as pd
from plots import altair_gauge

md_about_qual = '''
The Quality of Assessment for Learning (QuAL) score measures three 
components of high-quality feedback via three subscores: 

1. A detailed description of the behavior observed (rated 0-3 depending on detail level)
2. A suggestion for improvement is present (rated no = 0, yes = 1)
3. Linkage between the behavior and the suggestion is present (rated no = 0, yes = 1)

The final QuAL score is the sum of these subscores, so it ranges from 0 (lowest quality)
to 5 (highest quality).
'''
class NQDFullReport(object):

    def __init__(self, parent : st, results : dict):
        self.p = parent
        self.results = results

    def draw(self):
        st = self.p
        st.header('Understand Your Score')
        st.subheader('About the QuAL Score')
        # with st.expander('About the QuAL Score', True):
        st.markdown(md_about_qual)

        st.subheader('Your Level of Detail')

        gauge = altair_gauge(self.results['q1']['label'], 3, 'Level of Detail')
        c1, c2 = st.columns(2)
        with c1:
            st.altair_chart(gauge, use_container_width=True)
        with c2:
            # st.write(self.results)
            bar_df = (pd.DataFrame(self.results['q1']['scores'])
                        .reset_index()
                        .rename(columns={'index': 'Rating', 0: 'Score'}))
            bar = alt.Chart(bar_df).mark_bar().encode(
                x='Rating:O', y='Score',
                color=alt.Color('Rating', scale=alt.Scale(scheme='redyellowgreen'), legend=None)
            ).properties(height=225, title='Prediction Scores')
            st.altair_chart(bar, use_container_width=True)