added offline files

app.py

@@ -202,6 +202,25 @@ def topic_distribution(weights, smoothing=0.01):
     # for category in categories_sorted:
     #    return(topic_frequencies[category], topic_frequencies_spotlight[category], topic_ratios[category], category)
 
+def populate_session(dataset,model):
+    data_df = pd.read_parquet('./assets/data/'+dataset+ '_'+ model+'.parquet')
+    if model == 'albert-base-v2-yelp-polarity':
+        tokenizer = AutoTokenizer.from_pretrained('textattack/'+model)
+    else:
+        tokenizer = AutoTokenizer.from_pretrained(model)
+    if "user_data" not in st.session_state:
+        st.session_state["user_data"] = data_df
+    if "selected_slice" not in st.session_state:
+        st.session_state["selected_slice"] = None
+ 
+    loss_quantile = st.sidebar.slider(
+        "Loss Quantile", min_value=0.5, max_value=1.0,step=0.01,value=0.95
+    )
+    data_df['loss'] = data_df['loss'].astype(float)
+    losses = data_df['loss']
+    high_loss = losses.quantile(loss_quantile)
+    data_df['slice'] = 'high-loss'
+    data_df['slice'] = data_df['slice'].where(data_df['loss'] > high_loss, 'low-loss') 
 
 if __name__ == "__main__":
     ### STREAMLIT APP CONGFIG ###
@@ -224,26 +243,10 @@ if __name__ == "__main__":
         ["distilbert-base-uncased-finetuned-sst-2-english",
             "albert-base-v2-yelp-polarity"],
     )
-    ### LOAD DATA AND SESSION VARIABLES ###
-    data_df = pd.read_parquet('./assets/data/'+dataset+ '_'+ model+'.parquet')
-    if model == 'albert-base-v2-yelp-polarity':
-        tokenizer = AutoTokenizer.from_pretrained('textattack/'+model)
-    else:
-        tokenizer = AutoTokenizer.from_pretrained(model)
-    if "user_data" not in st.session_state:
-        st.session_state["user_data"] = data_df
-    if "selected_slice" not in st.session_state:
-        st.session_state["selected_slice"] = None
- 
-    loss_quantile = st.sidebar.slider(
-        "Loss Quantile", min_value=0.5, max_value=1.0,step=0.01,value=0.95
-    )
-    data_df['loss'] = data_df['loss'].astype(float)
-    losses = data_df['loss']
-    high_loss = losses.quantile(loss_quantile)
-    data_df['slice'] = 'high-loss'
-    data_df['slice'] = data_df['slice'].where(data_df['loss'] > high_loss, 'low-loss')
 
+    ### LOAD DATA AND SESSION VARIABLES ###
+    ##uncomment the next next line to run dynamically and not from file
+    #populate_session(dataset, model)
     with rcol:
         with st.spinner(text='loading...'):
             st.markdown('<h3>Word Distribution in Error Slice</h3>', unsafe_allow_html=True)