examples working

app.py

@@ -2,6 +2,8 @@ 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)
@@ -9,6 +11,10 @@ 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])
@@ -36,7 +42,6 @@ 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()
@@ -48,22 +53,36 @@ for i, mn in enumerate(models_to_load):
     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]
+if 'comment' not in st.session_state:
+    st.session_state['comment'] = ex
 with st.form('comment_form'):
-    comment = st.text_area('Try a comment:')
+    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')
-    try_example = right_col.form_submit_button('Try an example!')
-
-results = run_models(models_to_load, models, comment)
+    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']
 tabs = st.tabs(tab_titles)
 
 
 with tabs[0]:
-    with st.expander('What is the QuAL score?'):
+    with st.expander('About the QuAL score?'):
         st.markdown('**The best thing ever**!')
     cmap = cm.get_cmap('RdYlGn')
     color = cmap(results['qual']['label'] / 6.0)
@@ -74,16 +93,8 @@ with tabs[0]:
         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},
                 
                 }

requirements.txt

@@ -2,4 +2,5 @@ torch
 torchvision 
 torchaudio
 transformers
-plotly==5.11.0
+plotly==5.11.0
+pandas

test.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fffa01055fbedf253065c9a12f02c0aa3599856b59c22ebaa09ca23404a9742b
+size 10474780