starting refactor

app.py

@@ -1,18 +1,21 @@
 import streamlit as st
 import transformers as tf
 
+# 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
 
+# 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...')
@@ -22,13 +25,13 @@ for i, mn in enumerate(models_to_load):
     models[mn] = load_model(USERNAME, PREFIX, mn)
 lc_placeholder.empty()
 
-text = st.text_area('Type your stuff')
+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](text)[0]
+    res = models[mn](comment)[0]
 
     if mn == 'qual':
         cols[0].metric('Score', f"{res['label'].split('_')[1]}/5")