app.py

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)
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'
models_to_load = ['qual', 'q1', 'q2i', 'q3i']
n_models = float(len(models_to_load))
models = {}

# Show a progress bar while models are downloading, 
# then hide it when done
lc_placeholder = st.empty()
loader_container = lc_placeholder.container()
loader_container.caption('Loading models... please wait...')
pbar = loader_container.progress(0.0)
for i, mn in enumerate(models_to_load):
    pbar.progress((i+1.0) / n_models)
    models[mn] = load_model(USERNAME, PREFIX, mn)
lc_placeholder.empty()


### 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'])