stop caching

app.py

@@ -30,7 +30,7 @@ def load_data(sentence_num):
     df = df.loc[lambda d: (d['sentence_num']==sentence_num)&(d['iter_num']<1000)]
     return df
 
-@st.cache(show_spinner=False)
+#@st.cache(show_spinner=False)
 def mask_prob(model,mask_id,sentences,position,temp=1):
     masked_sentences = sentences.clone()
     masked_sentences[:, position] = mask_id
@@ -38,7 +38,7 @@ def mask_prob(model,mask_id,sentences,position,temp=1):
         logits = model(masked_sentences)[0]
     return F.log_softmax(logits[:, position] / temp, dim = -1)
 
-@st.cache(show_spinner=False)
+#@st.cache(show_spinner=False)
 def sample_words(probs,pos,sentences):
     candidates = [[tokenizer.decode([candidate]),torch.exp(probs)[0,candidate].item()]
                   for candidate in torch.argsort(probs[0],descending=True)[:10]]
@@ -64,7 +64,7 @@ def run_chains(tokenizer,model,mask_id,input_text,num_steps):
         sentence,_ = sample_words(probs,pos,sentence)
     return pd.DataFrame(data=data_list,columns=['step','sentence','next_sample_loc'])
 
-@st.cache(suppress_st_warning=True,show_spinner=False)
+#@st.cache(suppress_st_warning=True,show_spinner=False)
 def run_tsne(chain):
     st.sidebar.write('Running t-SNE...')
     st.sidebar.write('This takes ~1 min for 1000 steps with ~10 token sentences')
@@ -81,7 +81,7 @@ def run_tsne(chain):
 def clear_df():
     del st.session_state['df']
 
-@st.cache(show_spinner=False)
+#@st.cache(show_spinner=False)
 def plot_fig(df,sent_id,xlims,ylims,color_list):
     x_tsne, y_tsne = df.x_tsne, df.y_tsne
     fig = plt.figure(figsize=(5,5),dpi=200)