single tab eonly

app.py

@@ -83,13 +83,6 @@ with st.form('comment_form'):
         st.experimental_rerun()
     
 results = run_models(models_to_load, models, st.session_state['comment'])
-# tab_titles = ['Overview', 'Q1 - Level of Detail', 'Q2 - Suggestion Given', 'Q3 - Suggestion Linked', 'About']
-tab_titles = ['Overview', 'Full Report']
-tabs = st.tabs(tab_titles)
 
-with tabs[0]:
-    overview = NQDOverview(st, results)
-    overview.draw()
-with tabs[1]:
-    fullrep = NQDFullReport(st, results)
-    fullrep.draw()
+overview = NQDOverview(st, results)
+overview.draw()

fullreport.py

@@ -1,76 +0,0 @@
-import streamlit as st 
-import altair as alt 
-import pandas as pd
-from plots import altair_gauge, pred_bar_chart
-import streamlit.components.v1 as components
-
-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('Level of Detail')
-        c1, c2 = st.columns(2)
-        with c1:
-            gauge = altair_gauge(self.results['q1']['label'], 3, 'Level of Detail')
-            gauge_html = gauge.to_html()
-            # components.html(gauge_html, height=225, width=334)
-            st.altair_chart(gauge, use_container_width=True)
-        with c2:
-            bar = pred_bar_chart(self.results['q1']['scores'])
-            st.altair_chart(bar, use_container_width=True)
-
-        st.subheader('Suggestion for Improvement')
-        c1, c2 = st.columns(2)
-        with c1:
-            q2lab = self.results['q2i']['label']
-            st.markdown('#### Suggestion Given')
-            if q2lab == 0:
-                md_str = '# ✅ Yes'
-            else:
-                md_str = '# ❌ No'
-            st.markdown(md_str)
-            # st.metric('Suggestion Given', (md_str),
-                # help='Did the evaluator give a suggestion for improvement?')
-            gauge = altair_gauge(self.results['q2i']['label'], 1, 'Suggestion for Improvement')
-            # st.altair_chart(gauge, use_container_width=True)
-        with c2:
-            bar = pred_bar_chart(self.results['q2i']['scores'], binary_labels={0: 'Yes', 1: 'No'})
-            st.altair_chart(bar, use_container_width=True)
-
-        st.subheader('Suggestion Linking')
-        c1, c2 = st.columns(2)
-        with c1:
-            q2lab = self.results['q3i']['label']
-            st.markdown('#### Suggestion Linked')
-            if q2lab == 0:
-                md_str = '# ✅ Yes'
-            else:
-                md_str = '# ❌ No'
-            st.markdown(md_str)
-            # st.metric('Suggestion Given', (md_str),
-                # help='Did the evaluator give a suggestion for improvement?')
-            gauge = altair_gauge(self.results['q3i']['label'], 1, 'Suggestion for Improvement')
-            # st.altair_chart(gauge, use_container_width=True)
-        with c2:
-            bar = pred_bar_chart(self.results['q3i']['scores'], binary_labels={0: 'Yes', 1: 'No'})
-            st.altair_chart(bar, use_container_width=True)

plots.py

@@ -1,32 +0,0 @@
-import altair as alt 
-import pandas as pd
-import matplotlib.cm as cm
-
-def altair_gauge(score, max_score, title):
-    source = pd.DataFrame({"category": [1,2], "value": [4,3], "tt": ['hello!',None]})
-    gauge_theta2 = -1 * 2 * 3.14 * 0.25 + 3.14 * score / float(max_score)
-    c = alt.layer(
-        alt.Chart(source).mark_arc(innerRadius=100, theta=0, thetaOffset=(-1*2*3.14*0.25), theta2=(-1*2*3.14*0.25 + 3.14), color='lightgray', tooltip=None),
-        alt.Chart(source).mark_arc(innerRadius=100, theta=0, thetaOffset=(-1*2*3.14*0.25), theta2=gauge_theta2, tooltip=f'{title}: {int(score)}', color=get_color(score, max_score)),
-        alt.Chart(source).mark_text(text='%.1d' % score, size=80, font='Calibri', dy=-30)
-    ).properties(title=title)
-    return c
-
-def get_color(score, max_score):
-    cmap = cm.get_cmap('RdYlGn')
-    color = cmap(score / float(max_score))
-    color = f'rgba({int(color[0]*256)}, {int(color[1]*256)}, {int(color[2]*256)}, {int(color[3]*256)})'
-    return color 
-
-def pred_bar_chart(scores, binary_labels=None):
-    bar_df = (pd.DataFrame(scores)
-                .reset_index()
-                .rename(columns={'index': 'Rating', 0: 'Score'}))
-    if binary_labels:
-        bar_df['Rating'].replace(binary_labels, inplace=True)
-    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')
-    bar.to_html()
-    return bar