splitting out modules

.gitignore

@@ -0,0 +1 @@
+__pycache__/

app.py

@@ -1,9 +1,9 @@
 import streamlit as st
 import transformers as tf
-import plotly.graph_objects as go
-import matplotlib.cm as cm
 import pandas as pd
 
+from overview import NQDOverview
+
 
 # Function to load and cache models
 @st.experimental_singleton(show_spinner=False)
@@ -85,55 +85,5 @@ tab_titles = ['Overview', 'Q1 - Level of Detail', 'Q2 - Suggestion Given', 'Q3 -
 tabs = st.tabs(tab_titles)
 
 with tabs[0]:
-    cmap = cm.get_cmap('RdYlGn')
-    color = cmap(results['qual']['label'] / 6.0)
-    color = f'rgba({int(color[0]*256)}, {int(color[1]*256)}, {int(color[2]*256)}, {int(color[3]*256)})'
-
-    fig = go.Figure(go.Indicator(
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        value = results['qual']['label'],
-        mode = "gauge+number",
-        title = {'text': "QuAL"},
-        gauge = {'axis': {'range': [None, 5]},
-                'bgcolor': 'lightgray',
-                'bar': {'color': color, 'thickness': 1.0},
-                
-                }
-        ), layout=go.Layout(margin=dict(t=0, b=135)))#, layout=go.Layout(width=750, height=300))# layout={'paper_bgcolor': 'rgb(245,245,245)'})#,
-
-    cols = st.columns([7, 3])
-    with cols[0]:
-        st.plotly_chart(fig, use_container_width=True)
-    with cols[1]:
-
-    
-        # cols = st.columns(3)
-        # cols[0].markdown('#### Level of Detail')
-        q1lab = results['q1']['label']
-        if q1lab == 0:
-            md_str = '😥 None'
-        elif q1lab == 1:
-            md_str = '😐 Low'
-        elif q1lab == 2:
-            md_str = '😊 Medium'
-        elif q1lab == 3:
-            md_str = '😁 High'
-        # cols[0].markdown(md_str)
-        cols[1].metric('Level of Detail', md_str, 
-            help='How specific was the evaluator in describing the behavior?')
-
-        q2lab = results['q2i']['label']
-        if q2lab == 0:
-            md_str = '✅ Yes'
-        else:
-            md_str = '❌ No'
-        cols[1].metric('Suggestion Given', (md_str),
-            help='Did the evaluator give a suggestion for improvement?')
-
-        q3lab = results['q3i']['label']
-        if q3lab == 0:
-            md_str = '✅ Yes'
-        else:
-            md_str = '❌ No'
-        cols[1].metric('Suggestion Linked', md_str,
-            help='Is the suggestion for improvement linked to the described behavior?')
+    overview = NQDOverview(st, results)
+    overview.draw()

overview.py

@@ -0,0 +1,68 @@
+from matplotlib.cm import get_cmap
+import plotly.graph_objects as go
+
+class NQDOverview(object):
+    def __init__(self, parent, results, 
+        dial_cmap='RdYlGn'):
+        self.p = parent
+        self.results = results
+        self.cmap = get_cmap(dial_cmap)
+
+    def _get_color(self):
+        color = self.cmap(self.results['qual']['label'] / 6.0)
+        color = f'rgba({int(color[0]*256)}, {int(color[1]*256)}, {int(color[2]*256)}, {int(color[3]*256)})'
+        return color 
+
+    def _build_figure(self):
+        color = self._get_color()
+        fig = go.Figure(go.Indicator(
+            domain = {'x': [0, 1], 'y': [0, 1]},
+            value = self.results['qual']['label'],
+            mode = "gauge+number",
+            title = {'text': "QuAL"},
+            gauge = {'axis': {'range': [None, 5]},
+                    'bgcolor': 'lightgray',
+                    'bar': {'color': color, 'thickness': 1.0},
+                    
+                    }
+            ),
+            layout=go.Layout(margin=dict(t=0, b=135))
+        )
+        return fig
+
+    def draw(self):    
+        st = self.p
+
+        fig = self._build_figure()
+
+        cols = st.columns([7, 3])
+        with cols[0]:
+            st.plotly_chart(fig, use_container_width=True)
+        with cols[1]:
+            q1lab = self.results['q1']['label']
+            if q1lab == 0:
+                md_str = '😥 None'
+            elif q1lab == 1:
+                md_str = '😐 Low'
+            elif q1lab == 2:
+                md_str = '😊 Medium'
+            elif q1lab == 3:
+                md_str = '😁 High'
+            cols[1].metric('Level of Detail', md_str, 
+                help='How specific was the evaluator in describing the behavior?')
+
+            q2lab = self.results['q2i']['label']
+            if q2lab == 0:
+                md_str = '✅ Yes'
+            else:
+                md_str = '❌ No'
+            cols[1].metric('Suggestion Given', (md_str),
+                help='Did the evaluator give a suggestion for improvement?')
+
+            q3lab = self.results['q3i']['label']
+            if q3lab == 0:
+                md_str = '✅ Yes'
+            else:
+                md_str = '❌ No'
+            cols[1].metric('Suggestion Linked', md_str,
+                help='Is the suggestion for improvement linked to the described behavior?')