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| import streamlit as st | |
| import transformers as tf | |
| import pandas as pd | |
| from overview import NQDOverview | |
| from fullreport import NQDFullReport | |
| # Function to load and cache models | |
| def load_model(username, prefix, model_name): | |
| p = tf.pipeline('text-classification', f'{username}/{prefix}-{model_name}', return_all_scores=True) | |
| return p | |
| def load_pickle(f): | |
| return pd.read_pickle(f) | |
| def get_results(model, c): | |
| res = model(c)[0] | |
| scores = [r['score'] for r in res] | |
| label = max(range(len(scores)), key=lambda i: scores[i]) | |
| # label = float(res['label'].split('_')[1]) | |
| # scores = res['score'] | |
| return {'label': label, 'scores': scores} | |
| 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']) | |
| st.write(results) | |
| # 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() |