basic layout and function

app.py

@@ -1,5 +1,7 @@
 import streamlit as st
 import transformers as tf
+import plotly.graph_objects as go
+import matplotlib.cm as cm
 
 # Function to load and cache models
 @st.experimental_singleton(show_spinner=False)
@@ -7,6 +9,27 @@ def load_model(username, prefix, model_name):
     p = tf.pipeline('text-classification', f'{username}/{prefix}-{model_name}')
     return p
 
+def get_results(model, c):
+    res = model(c)[0]
+    label = float(res['label'].split('_')[1])
+    score = res['score']
+    return {'label': label, 'score': score}
+
+def run_models(model_names, models, c):
+    results = {}
+    for mn in model_names:
+        results[mn] = get_results(models[mn], c)
+    return results
+
+
+st.title('How *great* is your feedback?')
+st.markdown(
+"""Medical education *requires* high-quality feedback, but evaluating feedback
+is difficult and time-consuming. This tool uses NLP/ML to predict a validated
+feedback quality metric known as the QuAL Score. *Try it for yourself!*
+""")
+
+### Load models
 # Specify which models to load 
 USERNAME = 'maxspad'
 PREFIX = 'nlp-qual'
@@ -25,29 +48,105 @@ for i, mn in enumerate(models_to_load):
     models[mn] = load_model(USERNAME, PREFIX, mn)
 lc_placeholder.empty()
 
-comment = st.text_area('Try a comment:')
-
-denoms = ['5','3']
-for mn in models_to_load:
-    st.header(mn)
-    cols = st.columns(2)
-    res = models[mn](comment)[0]
-
-    if mn == 'qual':
-        cols[0].metric('Score', f"{res['label'].split('_')[1]}/5")
-    elif mn == 'q1':
-        cols[0].metric('Score', f"{res['label'].split('_')[1]}/3")
-    elif mn == 'q2i':
-        if res['label'] == 'LABEL_0':
-            cols[0].metric('Suggestion for improvement?', 'Yes')
-        else:
-            cols[0].metric('Suggestion for improvement?', 'No')
-    elif mn == 'q3i':
-        if res['label'] == 'LABEL_0':
-            cols[0].metric('Suggestion linked?', 'Yes')
-        else:
-            cols[0].metric('Suggestion linked?', 'No')
-
-    cols[1].caption('Confidence')
-    cols[1].progress(res['score'])
+
+### Process input
+with st.form('comment_form'):
+    comment = st.text_area('Try a comment:')
+    left_col, right_col = st.columns([1,9], gap='medium')
+    submitted = left_col.form_submit_button('Submit')
+    try_example = right_col.form_submit_button('Try an example!')
+
+results = run_models(models_to_load, models, comment)
+
+tab_titles = ['Overview', 'Q1 - Level of Detail', 'Q2 - Suggestion Given', 'Q3 - Suggestion Linked']
+tabs = st.tabs(tab_titles)
+
+
+with tabs[0]:
+    with st.expander('What is the QuAL score?'):
+        st.markdown('**The best thing ever**!')
+    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"},
+        # delta = {'reference': 380},
+        gauge = {'axis': {'range': [None, 5]},
+                'bgcolor': 'lightgray',
+                #  'steps': [
+                #     {'range': [0,1], 'color': "rgb(215,48,39)"},
+                #     {'range': [1,2], 'color': "rgb(244,109,67)"},
+                #     {'range': [2,3], 'color': "rgb(254,224,139)"},
+                #     {'range': [3,4], 'color': "rgb(102,189,99)"},
+                #     {'range': [4,5], 'color': "rgb(0,104,55)"}
+                #  ],
+                'bar': {'color': color, 'thickness': 1.0},
+                
+                }
+        ), layout=go.Layout(width=750, height=375))# layout={'paper_bgcolor': 'rgb(245,245,245)'})#,
+
+    st.plotly_chart(fig)
+
+    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].markdown('#### Suggestion Given')
+    q2lab = results['q2i']['label']
+    if q2lab == 0:
+        md_str = '# ✅ Yes'
+    else:
+        md_str = '# ❌ No'
+    cols[1].markdown(md_str)
+    # cols[1].markdown('# ✅ Yes', unsafe_allow_html=True)
+
+    cols[2].markdown('#### Suggestion Linked')
+    q3lab = results['q3i']['label']
+    if q3lab == 0:
+        md_str = '# ✅ Yes'
+    else:
+        md_str = '# ❌ No'
+    cols[2].markdown(md_str)
+
+with tabs[1]:
+    st.write('hello')
+
+
+
+# denoms = ['5','3']
+# for mn in models_to_load:
+#     st.header(mn)
+#     cols = st.columns(2)
+#     res = models[mn](comment)[0]
+
+#     if mn == 'qual':
+#         cols[0].metric('Score', f"{res['label'].split('_')[1]}/5")
+#     elif mn == 'q1':
+#         cols[0].metric('Score', f"{res['label'].split('_')[1]}/3")
+#     elif mn == 'q2i':
+#         if res['label'] == 'LABEL_0':
+#             cols[0].metric('Suggestion for improvement?', 'Yes')
+#         else:
+#             cols[0].metric('Suggestion for improvement?', 'No')
+#     elif mn == 'q3i':
+#         if res['label'] == 'LABEL_0':
+#             cols[0].metric('Suggestion linked?', 'Yes')
+#        else:
+#             cols[0].metric('Suggestion linked?', 'No')
+
+#     cols[1].caption('Confidence')
+#     cols[1].progress(res['score'])
 

requirements.txt

@@ -2,3 +2,4 @@ torch
 torchvision 
 torchaudio
 transformers
+plotly==5.11.0