initial commit

app.py

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+import streamlit as st
+import transformers as tf
+import plotly.graph_objects as go
+import matplotlib.cm as cm
+import pandas as pd
+
+
+# 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
+
+@st.experimental_singleton(show_spinner=False)
+def load_pickle(f):
+    return pd.read_pickle(f)
+
+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('Assess the *QuAL*ity of your feedback')
+st.caption(
+"""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()
+
+### Load example data
+examples = load_pickle('test.pkl')
+
+### Process input
+ex = examples['comment'].sample(1).tolist()[0]
+try:
+    ex = ex.strip().replace('_x000D_', '').replace('nan', 'blank')
+except:
+    ex = 'blank'
+if 'comment' not in st.session_state:
+    st.session_state['comment'] = ex
+with st.form('comment_form'):
+    comment = st.text_area('Try a comment:', value=st.session_state['comment'])
+    left_col, right_col = st.columns([1,9], gap='medium')
+    submitted = left_col.form_submit_button('Submit')
+    trying_example = right_col.form_submit_button('Try an example!')
+    
+    if submitted:
+        st.session_state['button_clicked'] = 'submit'
+        st.session_state['comment'] = comment
+        st.experimental_rerun()
+    elif trying_example:
+        st.session_state['button_clicked'] = 'example'
+        st.session_state['comment'] = ex
+        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']
+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?')

requirements.txt

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+torch 
+torchvision 
+torchaudio
+transformers
+plotly==5.11.0
+pandas
+spacy

test.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fffa01055fbedf253065c9a12f02c0aa3599856b59c22ebaa09ca23404a9742b
+size 10474780